Purpose By incorporating I4.0 technologies, the agri-food supply chain (AFSC) can become leaner, faster, more robust and greener. However, many challenges must be overcome to fully realise I4.0 in this context. Therefore, this paper aims to identify the challenges that hinder the adoption of I4.0 technologies on the development of the Lean, Agile, Resilient and Green (LARG) AFSC. Design/methodology/approach The approach adopted was to identify challenges addressed in the literature with expert opinion and Total Interpretive Structural Modelling (TISM) for adaptation. In addition, a Weighted Influence Non-linear Gauge Systems (WINGS) methodology has been developed that uses expert opinion to generate a power and influence matrix. Findings The results show that lack of commitment and understanding of top management (X12), lack of long term vision (X17) and lack of incentives and government support (15) are the most important challenges. Research limitations/implications This study does not explore the effectiveness of the concluded challenges of I4.0 and their strategy to overcome them. Also, the authors relied on a limited sample size for this study, which might not cover the detailed challenges within LARG AFSC. Finally, this study lacks in future advancement of I4.0, which may further affect the challenges. Practical implications By mentioning the key challenges, this study empowers LARG AFSC organisations to build a targeted strategy for smoother I4.0 implementation. Originality/value Industry 4.0 challenges remain unexplored in LARG AFSC. This improved awareness equips managers to navigate better the potential issues and complexity that may arise when adopting I4.0 in the LARG AFSC.

Judgmental adjustment of demand forecasting models using social media data and sentiment analysis within industry 5.0 ecosystems

Article scientifique ArODES

Yvonne Badulescu, Fernan Cañas, Naoufel Cheikhrouhou

International journal of information management data insights, 4, 2, 100272

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Industry 5.0 ecosystems focus on a human-centric approach to operations and supply chain management by integrating stakeholders, advanced technologies, and processes. While incorporating social media (SM) information into demand forecasting can significantly improve accuracy, it also brings about several challenges. This paper proposes an approach to leverage Big Data originating from SM networks combined with human judgment to build demand forecasts for new products. The structured methodology is demonstrated to improve forecast accuracy in a real case of a F&B company while providing several insights into the challenges and opportunities of integrating advanced information technology into the demand forecasting process. The main challenges include effectively categorising the impact factors of SM on demand forecasting, translating insights from SM into actionable decisions, and ensuring the accuracy and reliability of the data obtained from SM networks. Future studies should involve collaborative expert input and validating the approach across various companies and industries.

The intention of adopting blockchain technology in agri-food supply chains :

Article scientifique ArODES

evidence from an Indian economy

Aditi Saha, Rakesh D. Raut, Mukesh Kumar, Sanjoy Paul Kumar, Naoufel Cheikhrouhou

Journal of modelling in management, 2024, To be published

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Résumé:

Purpose This paper aims to explore the underlying intention behind using blockchain technology (BLCT) in the agri-food supply chain (AFSC). This is achieved by using a conceptual framework based on technology acceptance models that considers various factors influencing user behavior toward implementing this technology in their practices. Design/methodology/approach The conceptual framework developed is empirically validated using structural equation modeling (SEM). A total of 258 respondents from agri-food domain in India were involved in this survey, and their responses were analyzed through SEM to validate our conceptual framework. Findings The findings state that food safety and security, traceability, transparency and cost highly influence the intention to use BLCT. Decision-makers of the AFSCs are more inclined to embrace BLCT if they perceive the usefulness of the technology as valuable and believe it will enhance their productivity. Practical implications This study contributes to the existing literature by providing thorough examination of the variables that influence the intention to adopt BLCT within the AFSC. The insights aim to benefit industry decision-makers, supply chain practitioners and policymakers in their decision-making processes regarding BLCT adoption in the AFSC. Originality/value This study investigates how decision-makers’ perceptions of BLCT influence their intention to use it in AFSCs, as well as the impact of the different underlying factors deemed valuable in the adoption process of this technology.

Joint allocation of buffer sizes and service times in unreliable assembly-disassembly systems

Article scientifique ArODES

Khelil Kassoul, Naoufel Cheikhrouhou

IEEE Access, Vol. 12, pp. 50723 - 50737

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This paper introduces a modeling methodology for Assembly/Disassembly (AD) systems, proposing an effective simulation-based optimization approach. The objective is to simultaneously determine optimal allocations of service times and buffer sizes within unreliable AD systems, ultimately aiming to maximize the production rate. Our method integrates a global search approach, Genetic Algorithm (GA), with a local search method, Finite Perturbation Analysis (FPA). Through this integration, we extract production rate gradient information based on system parameters, which is then incorporated into a stochastic optimization algorithm to simultaneously identify the best allocation of service times and buffer sizes. The proposed approach is applied to various instances of AD systems, and experiments are conducted to assess its performance. The results demonstrate the superior performance of the integrated GA-FPA method compared to the standalone GA and FPA methods, both in terms of convergence behavior and solution quality. The identified allocation patterns and the potential to expand our approach to other complex manufacturing systems can provide valuable insights for managers seeking to establish more sustainable and efficient assembly and disassembly systems.

Time inconsistency in sustainable partner selection for vertical collaborative network organizations

Article scientifique ArODES

Yvonne Badulescu, Ezzeddine Soltan, Ari-Pekka Hameri, Naoufel Cheikhrouhou

IET collaborative intelligent manufacturing, 6, 1, e12096

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Collaborative Networked Organisations (CNOs) are increasingly recognised for their ability to harness cooperation and complementary competencies, outperforming indi- vidual efforts in pursuing business opportunities. However, the criticality of selecting the right long‐term partner for a CNO has been understated, especially considering the evolving landscape of sustainability perceptions. This research addresses the issue of time inconsistency within the context of sustainable CNO partner selection by employing the Fuzzy Analytical Hierarchical Process with the Technique for Order of Preference by Similarity to Ideal Solution. Time inconsistency refers to a situation where preferences or decisions change over different points in time, leading to inconsistencies in choices or actions. Specifically, the study focuses on a Swiss Manufacturing CNO, examining how the evaluation of potential partners' environmental criteria changes over time. The findings reveal the presence of time inconsistency in environmental criterion evaluation between two time periods. This inconsistency stems from the evolving perception of environmental conditions and the increasing social and governmental pressures sur- rounding environmental standards. As a consequence, improper partner choices in CNOs can be made, potentially undermining the collaborative's overall sustainability goals. The study sheds light on the importance of considering dynamic sustainability factors in partner selection for CNOs, emphasising the need for a more comprehensive and adaptive approach to secure fruitful and lasting collaborations

A glimpse of the future sustainable digital omnichannel retailing emerges :

Article scientifique ArODES

a systematic literature review

Manjunath S. Vhatkar, Rakesh D. Raut, Ravindra Gokhale, Naoufel Cheikhrouhou, Akarte Milind

Journal of cleaner production, 2024, vol. 442, Article no 141111

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With an emphasis on digitalization, distribution, logistics, and transportation, omnichannel retailing is essential in supply chain management (SCM). With this retailing strategy, sustainability and profitability in e-commerce may be addressed in a creative and cooperative manner. This article examines the relationship between digitization and Omnichannel retail and highlights sustainability concerns. The articles from WoS and Scopus between 2017 and October 2023 are included in the study. After excluding conference papers, journals not listed by the ABDC, and non-English publications, the 400 articles first found via a keyword search were reduced to 99. Using a range of criteria, including publication year, journal, nation, ABDC category, citations, and research methods, the content analysis of the chosen articles is part of the systematic literature review. This classification makes it possible to compare things, which shows how digitization and omnichannel retailing can help with sustainability. The results show a few studies relating sustainability with omnichannel retailing. While statistical modelling is used in Omnichannel Retailing to address consumer satisfaction, game theory modelling is commonly used for price decisions. Nevertheless, a few literature address environmental issues with theory and coordinated strategy. To address sustainability concerns and further our understanding of the relationship between digitization, sustainability, and omnichannel retailing, this analysis proposes interesting directions for future research.

Preface to intelligent systems for smart cities

Chapitre de livre ArODES

Anand J. Kulkarni, Naoufel Cheikhrouhou

Dans Cheikhrouhou, Naoufel, Kulkarni, Anand J., Intelligent systems for smart cities : select proceedings of the 2nd International Conference, ICISA 2023 (1 p.). 2024, Singapore : Springer

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2023

What should be the best retail strategy to deal with an unequal shipment from an unreliable manufacturer ?

Article scientifique ArODES

Soumya Kanti Hota, Biswajit Sarkar, Santanu Kumar Ghosh, Naoufel Cheikhrouhou, Gerardo Treviño-Garza

Journal of retailing and consumer services, 76, 103576

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Résumé:

Unreliability of the manufacturer is a challenging issue for a retailer in order to provide service to consumers and meet the market demand. Due to the unreliability of the manufacturer, the lead time increases, causing shortages. In turn, the retailer faces huge shortages and losses. The lead time can be minimized by reducing the flow time during work-in-process. To reduce the holding cost of the retailer under an increasing demand, the single-setup-multi-unequal-increasing-delivery is introduced by the unreliable manufacturer. But delivered products to the retailer variable demand are lower in volume than the ordered products. Due to the variable demand that is selling price and service dependent, the number of shipments during transportation increases for the single-setup-multi-unequal-increasing-delivery policy. The main goal of this research is to manage unequal shipments from the unreliable manufacturer for gaining more profit. The stochastic optimization approach is considered for the analytical solution. The quasi-closed-form solution is determined for the decision variables of the model. The study is illustrated both numerically and graphically. Results prove that the retailer can still control the profit if the manufacturer can reduce the flow time of the production and maintain a perfect retailing strategy. The research shows that the single-setup-multi-unequal-increasing-delivery policy is 1.14% more profitable than the single-setup-multi-delivery policy, and 8.53% more profitable than the single-setup-single-delivery policy.

Attaining sustainable development goals through embedding circular economy principles :

Article scientifique ArODES

evidence from food processing small- and medium-sized enterprises in India

Sudipta Gosh, Rakesh D. Raut, Naoufel Cheikhrouhou, Sudipta Sinha, Amitava Ray

Business strategy and the environment,

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Various industries are gradually sifting away from the linear economy and toward the circular economy (CE), but its advancement is crawling far behind the environmental contamination by the food processing industry in developing countries. Hardly any research analyzes the CE performance of food processing small- and medium-sized enterprises (FPSMEs) in an emerging economy context. Hence, developing a CE performance evaluation framework is imperative to facilitate this transition. This research proposes a comprehensive framework for evaluating CE performance based on three real-world cases of Indian FPSMEs. Initially, 15 essential criteria are short-listed from the literature and refined by the experts. Afterward, data are collected through questionnaires administered to experts and structured interviews. Next, this research employs a multi-criteria decision-making (MCDM)–based approach, in which the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to compute the objective weights of criteria and the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is used to determine the performances of FPSMEs and rank them accordingly. The results reveal that “investment in Corporate Social Responsibility (CSR),” “use of renewable energy,” “increase in scrap recycling rate,” “total CO2emission,”and“total water consumption ”are the top five criteria for CE performance. Investment in CSR emerges as the most influential criterion for strategic corporate transformation, which blends the notions of CE and CSR as a feasible solution for designing circular business processes.

Blockchain adoption challenges in the healthcare sector :

Article scientifique ArODES

a waste management perspective

Sarthak Dhingra, Rakesh D. Raut, Vinay Surendra Yadav, Naoufel Cheikhrouhou, B. Koteswara Rao Naik

Operations management research,

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The proposed study aims to identify the major challenges for blockchain adoption to manage reverse logistics activities of recyclable hospital waste in the Indian healthcare sector, in the COVID era. Fifteen challenges are identified through literature review and experts’ views and are prioritized and analyzed for cause-and-effect relationships using a hybrid approach combining Best–Worst Method (BWM) and Decision-Making Trial and Evaluation Laboratory (DEMATEL). A sensitivity analysis is performed to evaluate the results’ robustness. The results reveal that the Technological and Regulatory challenges category plays the most influential role consisting of Lack of Government Support and Policies, Lack of Strategic Planning, Lack of Knowledge and Qualified Expertise, Lack of Standards and Regulations, High Cost Involved, and Lack of Top Management Support are the most significant challenges affecting blockchain adoption. This study can support healthcare stakeholders, policymakers, government, and researchers in planning the strategic removal of the challenges to blockchain adoption in the Indian healthcare sector. The identification of the mutual interaction among the challenges will help healthcare decision makers address strategic questions of waste management from a holistic point of view. Since the work is achieved in the Indian healthcare context, generalization of the results must be carefully considered. The present study contributes significantly to discussing blockchain’s potential in healthcare waste management. The study’s findings can aid decision making process of managers, policymakers, and benefit researchers in this field.

Adaptive dynamic jumping particle swarm optimization for buffer allocation in unreliable production lines

Article scientifique ArODES

Khelil Kassoul, Naoufel Cheikhrouhou, Nicolas Zufferey

IEEE Access, Vol. 11, pp. 90410-90420

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Over the past five decades, the buffer allocation problem in production lines has been the topic of continuous interest. This paper proposes an adaptive simulation-optimization approach relying on particle swarm optimization (PSO) to solve the buffer allocation problem for unreliable serial production lines. The objective is to maximize the production rate of the production line. The key idea is to integrate a jumping strategy based on logarithmic and exponential functions into the velocity equation of the PSO algorithm using dynamic parameters to achieve quickly (near-)optimal solutions. To evaluate the effectiveness of the proposed method, extensive numerical experiments are conducted using several configurations of production lines, ranging from 3 to 100 machines. Additionally, benchmark algorithms from the literature are employed for comparison purposes. The results indicate that the proposed adaptive approach outperforms the benchmark algorithms regarding efficiency and solution quality.

Leveraging openness for refugees’ higher education :

Article scientifique ArODES

a freiran perspective to foster open cooperation

Barbara Class, Thierry Agagliate, Abdeljalil Akkari, Naoufel Cheikhrouhou, Moussa Sagayar

Open praxis, 2023, vol. 15, no. 1, pp. 49-59

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Research in the field of Higher Education in Emergencies (HEiE) starts to question the imposed Global North-centred perspective which arrives with ready-made solutions, considering refugees as objects of intervention rather than subjects of transformation. Leveraging the broader topics of Open Science and Open Education, this paper pioneers a new approach to scientific cooperation, fostering values of Openness in refugee higher education. It specifically addresses HEiE in Niger, Africa, in a training of trainers’ programme. It is designed in a participatory manner involving academics from the Global South and Global North, refugees who are themselves educators, and NGOs. Taking the form of a Certificate of Open Studies (COS), the training empowers refugees as enabled change agents, capable of making sense of diverse knowledge systems to transform their reality. Preliminary understanding of Open Cooperation is shared through a conceptual framework, Empowering refugees through liberation-oriented education. It addresses sustainability at the ontological and epistemic levels and relies on four main dimensions : Epistemologies of the South, Openness, Common good and Education as empowerment.

MUTRISS :

Article scientifique ArODES

a new method for material selection problems using MUltiple-TRIangles scenarios

Shervin Zakeri, Prasenjit Chatterjee, Naoufel Cheikhrouhou, Dimitri Konstantas, Yingjie Yang

Expert systems with applications, 2023, vol. 228, article no. 120463

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This paper proposes a new Multiple-criteria decision-making (MCDM) method called MUltiple-TRIangles ScenarioS (MUTRISS) with two scenarios respecting different levels of access to complete information for material selection problems. MUTRISS calculates the areas occupied by alternatives in n-dimensional space, employing analytic geometry and converting each alternative into n-edges forms. The paper applies MUTRISS to three material selection case studies, with Ti-6Al-4V, Material 4, and AISI 4140 Steel- UNS G41400 emerging as the best materials for the three examples with the highest overall scores of 0.036, 4.540 and 0.427 respectively. The results are compared with various MCDM methods through four statistical measures, including relative closeness ratio, robustness analysis, compromise ranking coefficient, and similarity degree. The measures focus on different aspects of MCDM methods in solving problems and their results. The paper concludes that MUTRISS offers a more robust and reliable approach for material selection problems compared to other MCDM methods, with the first scenario of MUTRISS being more reliable than the second scenario. The paper also emphasizes the importance of validating results in material selection problems due to the potential irreversible consequences of selecting the wrong material.

A hybrid multi-criteria decision-making approach for hospitals’ sustainability performance evaluation under fuzzy environment

Article scientifique ArODES

Hajar Regragui, Naoufal Sefiani, Hamid Azzouzi, Naoufel Cheikhrouhou

International journal of productivity and performance management, To be published

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Purpose – Hospital structures serve to protect and improve public health; however, they are recognized as a major source of environmental degradation. Thus, an effective performance evaluation framework is required to improve hospital sustainability. In this context, this study presents a holistic methodology that integrates the sustainability balanced scorecard (SBSC) with fuzzy Delphi method and fuzzy multi-criteria decision-making approaches for evaluating the sustainability performance of hospitals. Design/methodology/approach – Initially, a comprehensive list of relevant sustainability evaluation criteria was considered based on six SBSC-based dimensions, in line with triple-bottom-line sustainability dimensions, and derived from the literature review and experts’ opinions. Then, the weights of perspectives and their respective criteria are computed and ranked utilizing the fuzzy analytic hierarchy process. Subsequently, the hospitals’ sustainable performance values are ranked based on these criteria using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution. Findings – A numerical application was conducted in six public hospitals to exhibit the proposed model’s applicability. The results of this study revealed that “Patient satisfaction,” “Efficiency,” “Effectiveness,” “Access to care” and “Waste production,” respectively, are the five most important criteria of sustainable performance. Practical implications – The new model will provide decision-makers with management tools that may help them identify the relevant factors for upgrading the level of sustainability in their hospitals and thus improve public health and community well-being. Originality/value – This is the first study that proposes a new hybrid decision-making methodology for evaluating and comparing hospitals’ sustainability performance under a fuzzy environment.

Simultaneous allocation of buffer capacities and service times in unreliable production lines

Article scientifique ArODES

Khelil Kassoul, Naoufel Cheikhrouhou, Nicolas Zufferey

International journal of production research, To be published

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Simultaneous allocation of service times and buffer capacities in manufacturing systems in a random environment is a NP-hard combinatorial optimisation problem. This paper presents a sophisticated simulation-based optimisation approach for the design of unreliable production lines to maximise the production rate. The proposed method allows for a global search using a Genetic Algorithm (GA), which is coupled with Finite Perturbation Analysis (FPA) as a local search technique. Traditional techniques based on perturbation analysis optimise decision variables of the same nature (e.g. service time only, buffer capacity only), whereas the proposed technique simultaneously provides an allocation of service times and buffer capacities. One of the main focuses of this paper is the investigation of the persistence or absence of the buffer and service rate allocation patterns which are among the most essential insights that come from designing production lines. The results show the superiority of the combined GA-FPA approach regarding GA and FPA in terms of solution quality and convergence behaviour. Moreover, considering instances ranging from 3 to 100 machines, our numerical experiments are in line with the literature for small instances (as similar allocation patterns are identified in our work), but important differences are highlighted for medium/large instances.

2022

Forward and reverse logistics network design with sustainability for new and refurbished products in e-commerce

Article scientifique ArODES

Yash Daultani, Naoufel Cheikhrouhou, Saurabh Pratap, Dhirendra Prajapati

Operations and supply chain management : an international journal, 2022, Vol. 15, no. 4, pp. 540-550

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There has been an enormous growth in the availability of refurbished goods in the online marketplace. These days, consumers can buy either the new products or refurbished products based on their budget and individual preferences. As a result, e-commerce firms need to redesign their existing forward and reverse logistics networks while focusing on supply chain sustainability. This paper proposes a novel forward and reverses logistics network design (FRLND) along with a consumer pickup and demand facility within the promised time window while addressing the complexities related to e-commerce platforms, suppliers, manufacturers, third-party logistics providers, retailers, and customer tiers. A mixed-integer non-linear programming (MINLP) model is developed to minimize the overall anticipated cost that consists of costs related to procurement, production, inventory holding, shortages, material for return units, recycling, repairing, disposal, and transportation cost, across the entire supply chain network. The problem under consideration is NP-hard in nature. The special challenges of the problem in consideration are to consider all pickup and distribution nodes of retailers/customers within the range of promising time horizons. For solution purposes, the Block-based Genetic Algorithm, Fruit-fly Algorithm, and CPLEX are used. Computational experiments show the comparative charts and trends that are put on to an extensive range of practical scenarios. The experiments reveal that the Genetic Algorithm performs well than the Fruit Fly algorithm in terms of rate of convergence and solution quality in all cases of interest. CPLEX solution provides the minimum optimal value.

Lean–agile–resilience–green practices adoption challenges in sustainable agri-food supply chains

Article scientifique ArODES

Atul Kumar Sahu, Rakesh D. Raut, Vidyadhar V. Gedam, Naoufel Cheikhrouhou, Anoop Kumar Sahu

Business strategy and the environment, To be published

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Supply chain practitioners are striving to improve the performance of agri-food supply chains (AFSC) due to the lack of understanding of the mutual impacts of lean, agile, resilience, and green (LARG) practices in improving the performance of AFSC. There is a lack of specific methods to assess the power of their subsequent attributes. This paper identifies 12 unique challenges related to the implementation of LARG practices under the context of AFSC. The identification of challenges and the interdependency relationships among the LARG challenges are developed using a multistage approach. The multistage approach is composed of the generalized interval-valued trapezoidal fuzzy numbers (GIVTFNs), the degree of similarity method, and the decision-making trial and evaluation laboratory (DEMATEL) method. The finding of the study indicates that “Lack of understanding between the customer and other stakeholder requirements ”and“ Lack of transparency and trust” are the most significant challenges in the cause group and are the driving elements for implementing LARG practices. Further-more, “Lack of competitive advantages ”and “Lack of monitoring and auditing the ongoing supply chain activities ”fall under the effect category, which are influenced by the cause groups' challenges. The identified challenges can be controlled and handled strategically on a priority basis for successfully implementing LARG practices in the agri-food industry. The finding of study will help practitioners to overcome the LARG challenges and to improve the overall performance of AFSC.

To be more independent or more dependent ? :

Article scientifique ArODES

the evolution mechanism of co-innovation between digital platforms and content creators

Kuan Yan, Enjun Xia, Dimitri Konstantas, Naoufel Cheikhrouhou, Jieping Huang

Journal of the operational research society, To be published

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Résumé:

Co-innovation between digital platforms and complementors is motivated by their interactions, especially on content creation platforms that emphasise creativity. With the platform monopoly, creators are increasingly dependent on the platform thus making the interaction directional. As the long-term effect of the dependency effect on co-innovation under multi-agent networks is currently under-researched, a novel asymmetric NK model is proposed in this paper to evaluate creators’ dependence on the platform through agent-based simulation. The results show that the internal interaction of creators has an inverted U-shaped effect on co-innovation, and the external dependency effect has a negative effect on co-innovation. Further results considering global complexity constraints show that there is a substitution effect between internal interaction and external dependency and that relying on a platform can facilitate co-innovation by reducing potential external risks under high environmental complexity. Moreover, exploratory innovation is equally conducive to co-innovation and enables creators to be less dependent. This study extends a new model for digital platform research and responds to discussions between interaction, exploration, and innovation in the literature.

Big data-driven optimization for sustainable reverse logistics network design

Article scientifique ArODES

Mohammad Amin Khoei, Seyed Sina Aria, Hadi Gholizadeh, Mark Goh, Naoufel Cheikhrouhou

Journal of ambient intelligence and humanized computing, To be published

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The reverse logistics network (RLN) design for sustainable supply chain management is a strategic decision in network configuration, and is higher influenced by uncertainty. This paper applies a bi-level stochastic multi-objective model to design an RLN for a disposable product recycling management system. The goal is to balance the overall network cost against the associated environmental risks. An LP-metric based sample average approximation is formulated to solve the optimization problem. The model is validated numerically through a disposable product firm.

Integrated blockchain and internet of things in the food supply chain :

Article scientifique ArODES

adoption barriers

Shashank Kumar, Rakesh D. Raut, Nishant Agrawal, Naoufel Cheikhrouhou, Mahak Sharma, Tugrul Daim

Technovation, 2022, vol. 118, article no 102589, pp. 1-15

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Résumé:

Blockchain (BLC) and the Internet of Things (IoT) are two emerging technologies that have become popular among practitioners for improving the transparency, adaptability, and safety of any industry. This is especially critical for food security, as COVID-19 highlighted the vulnerability of food supply chain (FSC). However, Indian organizations are experiencing problems in implementing the integrated form of BLC-IoT due to limited knowledge and insufficient research. The current study aims to propose a conceptual framework to reduce the impact of adoption barriers against BLC-IoT in FSC. Thirteen key barriers were identified after a thorough literature review and consultation with experts. The relationship among barriers was established using Interpretive structural modeling (ISM) and Decision-making trial and evaluation laboratory (DEMATEL) methods. The analysis shows that the lack of government regulation and workers' low competency significantly influence BLC-IoT adoption. The results also indicate the intricacy of decision-making by demonstrating that 9 of the 13 barriers were a part of the linkage cluster. The study outcome will help practitioners in developing and planning strategies for effective adoption of BLC-IoT in FSC.

Exponential particle swarm optimization for global optimization

Article scientifique ArODES

Khelil Kassoul, Nicolas Zufferey, Naoufel Cheikhrouhou, Samir Brahim Belhaouari

IEEE access, 2022, vol. 10, pp. 78320 - 78344

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Résumé:

Nature-inspired metaheuristics have been extensively investigated to solve challenging optimization problems. Particle Swarm Optimization (PSO) is one of the most famous nature-inspired algorithms owing to its simplicity and ability to be used in a wide range of applications. This paper presents an extended PSO variant, namely, Exponential Particle Swarm Optimization (ExPSO). To effectively explore the whole search space, the proposed algorithm divides the swarm population into three equal subpopulations and employs a new search strategy based on an exponential function (permitting particles to make leaps in the search space) and an adapted control of the velocity range of each particle (to balance the exploration and exploitation search phases). The leaping strategy is integrated into the velocity equation and a new linear decreasing cognitive parameter (including a dynamic inertia weight strategy) is integrated into the proposed method. The developed algorithm allows large jumps at the beginning of the search, and then small jumps for further improvements in specific regions of the solution search space. Our variant approach, ExPSO, has been intensively tested through a comparison with eight other well-known heuristic search algorithms, over 29 benchmark problems, and real optimization engineering problems. The Wilcoxon signed-rank test and Friedman rank have been applied to analyze the search performance of the algorithms. The comparisons and statistical results show that the exponential search strategy significantly contributes to the search process and proves the superiority of ExPSO in terms of the convergence velocity and optimization accuracy.

Designing a food supply chain for enhanced social sustainability in developing countries

Article scientifique ArODES

D.G. Mogale, Abhijeet Ghadge, Naoufel Cheikhrouhou, Manoj Kumar Tiwari

International journal of production research, To be published

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Résumé:

The food grain production in India has progressively risen in the past few decades, whereas the storage capacity has remained limited. The policymakers in India are attempting to close this capacity gap while addressing sustainability objectives. However, the quantification and integration of multiple social sustainability factors have remained a challenge. To improve the overall sustainability, the study attempts to develop a mathematical model considering procurement, transportation, inventory, and location-related issues. Several supply chain network factors are integrated and assessed while focussing on the social sustainability dimension. Three cases of India's largest food grain-producing and consuming states are analysed with the help of two Pareto-based algorithms. Multiple relationships between variations in supply, demand, and the capacity of silos with three defined objectives are evaluated. It is observed that, the demand significantly influences the economic and environmental objectives compared with the supply and silo capacity. The capacity of silos has a more significant impact on social objectives than economic and environmental objectives. Results reveal the importance of establishing a sufficient number of modernised silos, which reduces environmental impact and improves social factors such as farmers’ economic condition and welfare, balanced economic development, number of jobs created, and public health level.

Ranking based on optimal points and win-loss-draw multi-criteria decision-making with application to supplier evaluation problem

Article scientifique ArODES

Shervin Zakeri, Prasenjit Chatterjee, Naoufel Cheikhrouhou, Dimitri Konstantas

Expert systems with applications, 2022, vol. 191, article no. 116258, pp. 1-20

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Résumé:

Supplier evaluation is a complex multi-criteria decision-making (MCDM) problem that deals with assessment of suppliers as the potential alternatives against various types of criteria. We consider the context where decision makers (DMs) have complete information about the suppliers and criteria. To address the needs of decision makers, a multi-criteria evaluation method named Ranking based on optimal points (RBOP) is developed in this paper. By imitating and simulating human decision-making behavioural patterns, the developed MCDM method selects the best alternative that is closer to what the DM desires. Furthermore, a novel subjective MCDM weighting methods called win-loss-draw (WLD) method is also developed, which is also based on human behavioural pattern. A real case study of domestic cheese brands is considered to apply the developed methods to select the best cheese supplier for an Iranian hypermarket. Compared to other MCDM methods, outputs of the RBOP method show some differences due to the impact of WLD method, which intensified divergence and optimal points during the decision-making process.

The grey ten-element analysis method :

Article scientifique ArODES

a novel strategic analysis tool

Shervin Zakeri, Dimitri Konstantas, Naoufel Cheikhrouhou

Mathematics, 2022, vol. 10, no. 5, article no. 846, pp. 1-22

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Résumé:

In this paper, a new strategic analysis method is introduced, called the ten-element analysis (TEA) method to determine the firm’s strategic position in the market. The new method is grounded on the computation of the reflections of the external factors on the firm’s internal factors through the changes of the values of the internal factors throughout the time when a lack of complete information regarding the environmental factors exists. The TEA method takes ten effective key elements of the firm into account and investigates their changes through a maximum of nine periods and a minimum of two periods. To conduct the model, the paper is mainly focused on four main rubrics, including the detection of the reflection of the firm’s environmental factors on the internal factors, deriving the strategic position of the firm from the reflections, the capability of the existing strategic models in determining the strategic position from the reflections in presence of uncertainty and incomplete information of the external factors. The method is applied to a dairy company in order to find its strategic position in the market. The results showed that the output of the TEA method and SWOT analysis is similar which makes the new method reliable to employ. The TEA method is developed under the grey environment to harness the uncertainty where a new grey comparison method is introduced to compare the grey numbers.

A grey approach for the computation of interactions between two groups of irrelevant variables of decision matrices

Chapitre de livre ArODES

Shervin Zakeri, Naoufel Cheikhrouhou, Dimitri Konstantas

Dans Kulkarni, Anand J., Multiple criteria decision making : techniques, analysis and applications (Pp. 193-222). 2022, Cham : Springer

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Résumé:

In this chapter, we aim to find a mathematical solution to compute the impact between two irrelevant decision matrices in a complex decision-making problem using multiple-criteria decision-making (MCDM) methods. The existing MCDM methods merely provide solutions for the one-stage decision-making procedure and do not take other effective variables outside of the decision matrix into account, while in real-world processes, the decisions always impact by the variables where they appear to be irrelevant. To demonstrate our proposed approach, it is applied to a case of supplier selection and firm’s strategies in which the interaction of selected strategies has been investigated on the selection of the best supplier. In order to handle the uncertainty that emerge during the process, this four-section approach is implemented as a grey framework and deals with grey Entropy, grey-TOPSIS, and the grey strategies interaction model. With comparison of rankings in computation with impact of selected strategies and without them, results indicated essentially the difference between these two cases.

2021

Sustainable agriculture supply chain network design considering water-energy-food nexus using queuing system :

Article scientifique ArODES

a hybrid robust possibilistic programming

Komeyl Baghizadeh, Naoufel Cheikhrouhou, Kannan Govindan, Mahboubeh Ziyarati

Natural resource modeling, 2022, vol. 35, no. 1, article e12337

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Résumé:

Due to the nature of the agricultural and food industry, the management of production, storage, transportation, waste disposal and environmental effects of their production, are of great importance. To deal with the sustainability issues linked to their supply chains, we propose in this study a mathematical model to design a sustainable supply chain of highly perishable agricultural product (strawberry). The model is a multiperiod, multiproduct multiobjective MINLP mathematical program that takes into consideration economic, social and environmental objectives to cover all aspects of sustainability. In addition, a G/M/S/M queuing system is developed for the transportation of harvested products between facilities for the first time. Since real-world problems related to industries such as food and agriculture are inherently uncertain, in this model, the important parameters of the problem are considered uncertain using fuzzy sets theory and a hybrid robust possibilistic programming model is developed. In addition, the Epsilon constraint approach converts the multiobjective mathematical model into a single-objective one and the Lagrangian relaxation method is used to effectively solve the model on a large scale. A case study in Iran is provided to investigate the results and discuss the solutions. Finally, a sensitivity analysis is performed to identify the impacts of important parameters on the solution. According to the analysis, equipping greenhouses with drip irrigation system and using solar panels in greenhouses, respectively, have the greatest impact on improving all target functions.

The multi-period multi-level capacitated lot-sizing and scheduling problem in the dairy soft-drink industry

Article scientifique ArODES

Abderrahmene Mediouni, Nicolas Zufferey, Mansour Rached, Naoufel Cheikhrouhou

Supply chain forum : an international journal, 2022, vol. 23, no. 3, pp. 272-284

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Résumé:

Lot-sizing and scheduling are a well-known problem. However, there are only few works addressing the problem in the food industry. We propose a mixed-integer-programming model for the lot-sizing and scheduling problem in a dairy production process. The latter is composed of standardisation tanks that can store different types of flavours and ultra-high temperature (UHT) lines. The model considers limited shelf life of liquid flavours in standardisation tanks and production bottleneck that can alternate between stages. Ten representative instances using real data are addressed with an exact method and a relax-and-fix heuristic. The results show that the assignment strategies increase the production costs. Moreover, these assignment strategies do not only reveal the total production capacity related to some industrial settings but also show the limit at which the bottleneck capacity of a given setting moves from the UHT line to the tanks.

The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries

Article scientifique ArODES

Vaibhav S. Narwane, Rakesh D. Raut, Vinay Surendra Yadav, Naoufel Cheikhrouhou, Balkrishna E. Narkhede, Pragati Priyadarshinee

Journal of enterprise information management, 2021, vol. 34, no. 5, pp. 1452-1480

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Résumé:

Purpose : Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors. Design/methodology/approach : A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis. Findings : Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption. Research limitations/implications : This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics. Originality/value : For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

Blockchain drivers to achieve sustainable food security in the Indian context

Article scientifique ArODES

Vinay Surendra Yadav, A.R. Singh, Rakesh D. Raut, Naoufel Cheikhrouhou

Annals of operations research, To be published

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Résumé:

Blockchain has the potential to improve sustainable food security due to its unique features like traceability, decentralized and immutable database, and smart contract mechanisms. However, blockchain technology is still in the early stages of adoption in particular in agricultural applications. In this context, this article aims to identify blockchain drivers to achieve sustainable food security in the Indian context and model them using an integrated MCDM (Multiple Criteria Decision Making) approach. The blockchain adoption drivers are identified through an exhaustive literature review and opinions from domain experts from industry, academia, and Agriculture Supply Chain (ASC) stakeholders. Subsequently, the integrated MCDM approach is developed by combining Total Interpretive Structural Modelling (TISM) and Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), which does not only investigate the interrelation between the identified constructs and builds hierarchy but also determines the intensity of the causal interrelationships. At a later stage, Fuzzy Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) is used to cluster the identified drivers to evaluate the importance of each driver. The results reveal that Traceability, Real-time information availability to agro-stakeholder, and Decentralized and immutable database are the most significant drivers. Policymakers, governmental organizations and other relevant stakeholders may utilize the information about the interrelationship between these drivers and their influential power, to frame suitable strategies for enhancing the adoption rate of blockchain in the Indian ASC.

The vital-immaterial-mediocre multi-criteria decision-making method

Article scientifique ArODES

Shervin Zakeri, Fatih Ecer, Dimitri Konstantas, Naoufel Cheikhrouhou

Kybernetes, To be published

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Résumé:

Purpose : This paper proposes a new multi-criteria decision-making method, called the vital-immaterial-mediocre method (VIMM), to determine the weight of multiple conflicting and subjective criteria in a decision-making problem. Design/methodology/approach : The novel method utilizes pairwise comparisons, vector-based procedures and a scoring approach to determine weights of criteria. The VIMM compares alternatives by the three crucial components, namely the vital, immaterial and mediocre criteria. The vital criterion has the largest effect on the final results, followed by the mediocre criterion and then the immaterial criterion, which is the least impactful on the prioritization of alternatives. VIMM is developed in two forms where the first scenario is designed to solve one-goal decision-making problems, while the second scenario embraces multiple goals. Findings : To validate the method’s performance and applicability, VIMM is applied to a problem of sustainable supplier selection. Comparisons between VIMM, analytic hierarchy process (AHP) and best-worst method (BWM) reveal that VIMM significantly requires fewer comparisons. Moreover, VIMM works well with both fractional and integer numbers in its comparison procedures. Research limitations/implications : As an implication for research, we have added the development of the VIMM under fuzzy and grey environments as the direction for optimization of the method. Practical implications : As managerial implications, VIMM not only provides less complex process for the evaluation of the criteria in the managerial decision-making process, but it also generates consistent results, which make VIMM a reliable tool to apply to a large number of potential decision-making problems. Originality/value : As a novel subjective weighting method, there exist five major values that VIMM brings over AHP and BWM methods: VIMM requires fewer comparisons compared with AHP and BWM; it is not sensitive to the number of criteria; as a goal-oriented method, it exclusively takes the decision-making goals into account; it keeps the validity and reliability of the Decision-Makers’ (DMs’) opinions and works well with integer and fractional numbers.

Design of multi-objective sustainable food distribution network in the Indian context with multiple delivery channels

Article scientifique ArODES

Vinay Surendra Yadav, A.R. Singh, Rakesh D. Raut, Naoufel Cheikhrouhou

Computers industrial engineering, 2021, vol. 160, article no 107549, pp. 1-12

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Résumé:

The paper addresses the design of a sustainable multiple-channel fresh food distribution network, which serves three purposes. Firstly, it addresses the changing preferences of consumers for online retailing. Secondly, the model investigates the distribution network of Buy Online Pickup in Store (BOPS) in the context of food supply chain. Thirdly, the model formulates new farming laws passed by the Indian government which allows the farmers to sell their produce as per their choice and removes the constraint of selling in the government regulated Mandis. To address the problem, a multiple-channel multi-objective fresh food distribution network model is developed. The model takes sustainability into consideration by formulating economic (total cost minimization), environmental (emission minimization) and social (delivery time minimization) objectives. A combination of an epsilon constraint and linear programming (LP) metrics method is used to solve the model. The applicability of the model is verified through a case study of a fresh tomato supply chain in India. Moreover, a sensitivity analysis is carried out to evaluate the different distribution strategies. Results show that demand ratio plays an important role in the identification of the optimal design with respect to the three objectives considered.

Sustainable partner selection for collaborative networked organisations with risk consideration in the context of COVID-19

Article scientifique ArODES

Yvonne Badulescu, Ari-Pekka Hameri, Naoufel Cheikhrouhou

Journal of global operations and strategic sourcing, To be published

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Résumé:

Collaborative networked organisations (CNO) are a means of ensuring longevity and business continuity in the face of a global crisis such as COVID-19. This paper aims to present a multi-criteria decision-making method for sustainable partner selection based on the three sustainability pillars and risk. A combined analytic hierarchy process (AHP) and fuzzy AHP (F-AHP) with Technique for Order of Preference by Similarity to Ideal Solution approach is the methodology used to evaluate and rank potential partners based on known conditions and predicted conditions at a future time based on uncertainty to support sustainable partner selection. It is integral to include risk criteria as an addition to the three sustainability pillars: economic, environmental and social, to build a robust and sustainable CNO. One must combine the AHP and F-AHP weightings to ensure the most appropriate sustainable partner selection for the current as well as predicted future period. The approach proposed in this paper is intended to support existing CNO, as well as individual firms wanting to create a CNO, to build a more robust and sustainable partner selection process in the context of a force majeure such as COVID-19. This paper presents a novel approach to the partner selection process for a sustainable CNO under current known conditions and future uncertain conditions, highlighting the risk of a force majeure occurring such as COVID-19.

Buffer allocation design for unreliable production lines using genetic algorithm and finite perturbation analysis

Article scientifique ArODES

Khelil Kassoul, Naoufel Cheikhrouhou, Nicolas Zufferey

International journal of production research, To be published

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Résumé:

The buffer allocation problem in production lines is an NP-hard combinatorial optimisation problem. This paper proposes a new hybrid optimisation approach (using simulation) relying on genetic algorithm (GA) and finite perturbation analysis (FPA). Unlike the infinitesimal perturbation analysis, which deals with small (infinitesimal variation) perturbations for estimating gradients of the performance measure, FPA deals with larger (finite) or more lasting perturbations. It is an extension specifically dedicated to discrete decision variables and applicable to most discrete-event dynamic systems. The proposed method allows a global search using GA, with refinement in specific solution-space regions using FPA. The main objective is to maximise the average production rate of a production line with unreliable machines, by allocating the total buffer capacity in locations between machines. Extensive numerical experiments show that: (1) the proposed hybrid GA-FPA method clearly outperforms the state-of-the-art methods from the literature; (2) combining FPA and GA is beneficial when compared to employing GA or FPA independently.

Big data analytics :

Article scientifique ArODES

implementation challenges in Indian manufacturing supply chains

Rakesh D. Raut, Vinay Surendra Yadav, Naoufel Cheikhrouhou, Vaibhav S. Narwane, Balkrishna E. Narkhede

Computers in Industry, 2021, vol. 125, article 103368, pp. 1-13

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Résumé:

Big Data Analytics (BDA) has attracted significant attention from both academicians and practitioners alike as it provides several ways to improve strategic, tactical and operational capabilities to eventually create a positive impact on the economic performance of organizations. In the present study, twelve significant barriers against BDA implementation are identified and assessed in the context of Indian manufacturing Supply Chains (SC). These barriers are modeled using an integrated two-stage approach, consisting of Interpretive Structural Modeling (ISM) in the first stage and Decision-Making Trial and Evaluation Laboratory (DEMATEL) in the second stage. The approach developed provides the interrelationships between the identified constructs and their intensities. Moreover, Fuzzy MICMAC technique is applied to analyze the high impact (i.e., high driving power) barriers. Results show that four constructs, namely lack of top management support, lack of financial support, lack of skills, and lack of techniques or procedures, are the most significant barriers. This study aids policy-makers in conceptualizing the mutual interaction of the barriers for developing policies and strategies to improve the penetration of BDA in manufacturing SC.

Evaluating demand forecasting models using multi-criteria decision-making approach

Article scientifique ArODES

Yvonne Badulescu, Ari-Pekka Hameri, Naoufel Cheikhrouhou

Journal of advances in management research, 2021, vol. 18, no. 5, pp. 661-683

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Résumé:

Evaluating appropriate error measures to determine demand forecast accuracy is essential in model selection, however there is no approach that simultaneously evaluates different model classes and several inter-dependent error measures. Furthermore, error measures may yield conflicting results making it more difficult to select the ‘best’ forecasting model when considering several error measures simultaneously. This paper proposes a novel process of evaluation of demand forecasting models using the analytical network process combined with the technique for order of preference by similarity to ideal solution (ANP-TOPSIS) which incorporates interdependence amongst error measures. The methodology is validated through an implementation case of a plastic bag manufacturer demonstrating that the use of the ANP-TOPSIS approach, avoided the selection of an inappropriate forecasting model due to conflicting error measurements. Moreover, a sensitivity analysis finds that the interdependence between the error measures is found to impact the relative closeness to the ideal solution, even though it plays a minimal role in the final ranking of the forecasting models.

2020

The orbital systems :

Article scientifique ArODES

theory paradigm

Shervin Zakeri, Naoufel Cheikhrouhou

International journal of applied decision sciences, 2021, vol. 14, no. 3, pp. 274-302

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Résumé:

In this paper, a new theory is introduced called orbital systems theory to handle the uncertainty of the natural phenomena, the complicated real-world problems, and the human's decision-making process which naturally creates the inconstancy and uncertainty in each process it involves. The philosophy of the new theory is established based on this hypothesis that every component of the universe is a box that carries the information and each one is constructed by the particular information concepts that move along specific orbits. The orbital system is an integral part of the new theory. As a restricted numeric system, its core is the concept of reality. An orbital system is developed based on five numerical spectra surrounded by four parallel dimensions of reality and certain reality. With considering “time” as an element, each dimension adds entropy to the system, and increases/decreases the level of uncertainty.

Identification and ranking of key factors impacting efficiency of Indian shipping logistics sector

Article scientifique ArODES

Dhirendra Prajapati, Yash Daultani, Naoufel Cheikhrouhou

OPSEARCH, 2020, vol. 57, no. 3, pp. 765–786

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Résumé:

Shipping logistics is one of the very important criteria which can directly and indirectly affect the economy and GDP of any country. Shipping logistics depends on various factors which have been addressed by several authors in their previous studies. Studies in this literature are focused on selecting the most impactful factors among all the criteria. Methods used in this literature are fuzzy Analytical hierarchy process (AHP) and fuzzy Technique for order of Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision analysis. These methods also helped in this literature to develop a new hybrid method “fuzzy TOPSIS AHP”. There have been no studies involving maritime logistics with comparative analysis of multi-criteria decision making i.e., fuzzy AHP and fuzzy TOPSIS AHP. The literature involved large number of expert opinions on the factor prioritization of maritime logistics. Factors selected for prioritization are Environmental Sustainability, Supply and Demand, Operations and Port Selection. However, the research showed that the comparative analysis of the results was quite opposite to one another and proposed a new way for researchers to use the hybrid method of fuzzy TOPSIS AHP method in future research. The study aimed to improve the existing maritime model which can help professionals to get connected with the maritime logistics firms. The study also aims to contribute this model for researchers in their study related to maritime logistics.

Special issue :

Article scientifique ArODES

new paradigms in the management of healthcare networks - the 9th conference on management and engineering of healthcare systems GISEH

Brigitte Rorive-Feytmans, Carlos Cordon, Philippe Garnerin, Naoufel Cheikhrouhou

Supply Chain Forum, 2020, vol. 21, no 2, pp. 67-68

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2019

Fit between humanitarian professionals and project requirements :

Article scientifique ArODES

hybrid group decision procedure to reduce uncertainty in decision-making

Abderrahmen Mediouni, Nicolas Zufferey, Nachiappan Subramanian, Naoufel Cheikhrouhou

Annals of Operations Research, 2019, vol. 283, pp. 471–496

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Résumé:

Choosing the right professional that has to meet indeterminate requirements is a critical aspect in humanitarian development and implementation projects. This paper proposes a hybrid evaluation methodology for some non-governmental organizations enabling them to select the most competent expert who can properly and adequately develop and implement humanitarian projects. This methodology accommodates various stakeholders’ perspectives in satisfying the unique requirements of humanitarian projects that are capable of handling a range of uncertain issues from both stakeholders and project requirements. The criteria weights are calculated using a two-step multi-criteria decision-making method: (1) fuzzy analytical hierarchy process for the evaluation of the decision maker weights coupled with (2) technique for order preference by similarity to ideal solution to rank the alternatives which provide the ability to take into account both quantitative and qualitative evaluations. Sensitivity analysis have been developed and discussed by means of a real case of expert selection problem for a non-profit organisation. The results show that the approach allows a decrease in the uncertainty associated with decision-making, which proves that the approach provides robust solutions in terms of sensitivity analysis.

Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system

Article scientifique ArODES

Amir Mohammad Fathollahi-Fard, Mehdi Ranjbar-Bourani, Mostafa Hajiaghaei-Keshteli, Naoufel Cheikhrouhou

Computers Industrial Engineering, 2019, vol. 137, article no 106103, pp. 1-15

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Résumé:

The truck scheduling problem is one of the most challenging and important types of scheduling with a large number of real-world applications in the area of logistics and cross-docking systems. This problem is formulated to find an optimal condition for both receiving and shipping trucks sequences. Due to the difficulty of the practicality of the truck scheduling problem for large-scale cases, the literature has shown that there is a chance, even with low possibility, for a new optimizer to outperform existing algorithms for this optimization problem. Already applied successfully to solve similar complicated optimization problems, the Social Engineering Optimizer (SEO) inspired by social engineering phenomena, has been never applied to the truck scheduling problem. This motivates us to develop a set of novel modifications of the recently-developed SEO. To validate these optimizers, they are evaluated by solving a set of standard benchmark functions. All the algorithms have been calibrated by the Taguchi experimental design approach to further enhance their optimization performance. In addition to some benchmarks of truck scheduling, a real case study to prove the proposed problem is utilized to show the high-efficiency of the developed optimizers in a real situation. The results indicate that the proposed modifications of SEO considerably outperform the state of the art algorithms and provide very competitive results.

Modelling of sustainable food grain supply chain distribution system :

Article scientifique ArODES

a bi-objective approach

D.G. Mogale, Naoufel Cheikhrouhou, Manoj Kumar Tiwari

International Journal of Production Research, 2020, vol. 58, no. 18, pp. 5521-5544

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Résumé:

Growing food demand, environmental degradation, post-harvest losses and the dearth of resources encourage the decision makers from developing nations to integrate the economic and environmental aspects in food supply chain network design. This paper aims to develop a bi-objective decision support model for sustainable food grain supply chain considering an entire network of procurement centres, central, state and district level warehouses, and fair price shops. The model seeks to minimise the cost and carbon dioxide emission simultaneously. The model covers several problem peculiarities such as multi-echelon, multi-period, multi-modal transportation, multiple sourcing and distribution, emission caused due to various motives, heterogeneous capacitated vehicles and limited availability, and capacitated warehouses. Multiple realistic problem instances are solved using the two Pareto based multi-objective algorithms. Sensitivity analysis results imply that the decision makers should establish a sufficient number of warehouses in each producing and consuming states by maintaining the suitable balance between the two objectives. Various policymakers like Food Corporation of India, logistics providers and state government agencies will be benefited from this research study.

Multi-objective mathematical modeling for sustainable supply chain management in the paper industry

Article scientifique ArODES

Taha Vafaeenezhad, Reza Tavakkoli-Moghaddam, Naoufel Cheikhrouhou

Computers and Industrial Engineering, 2019, vol. 135, pp. 1092-1102

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Résumé:

This work develops a multi-objective linear programming model for a multi-echelon, multi-product, multi-period supply chain planning. This model considers all dimensions of sustainable development paradigm simultaneously and expands six objective functions for making the model realistic. To solve this model and large-scale sustainable supply chain management, an improved version of the augmented ε-constraint method (AUGMECON2) is used. Computational results illustrate the proposed model admitted various progresses in all the three pillars of sustainable development; namely remarkable progress, mildly progress and irregular one, respectively. In addition, important managerial insights are provided. Trade-off interactions between multi-objectives are perceived by the obtained Pareto solutions that represents the cost of being sustainable from the point of optimizing the social factors and environmental impacts. An application to the paper industry is presented and discussed.

Extended distribution-free newsvendor models with demand updates using experts’ judgment

Article scientifique ArODES

Madhukar Nagare, Pankaj Dutta, Naoufel Cheikhrouhou

International Transactions in Operational Research, 2021, vol. 28, no. 6, pp. 3536-3576

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Résumé:

Retailers of short life cycle products, such as [life and style goods are required to commit an order with their suppliers far ahead of their selling seasons inclusive scant demand information. Most of the time, they practice preseason two-stage ordering (instants) that provide an opportunity to modify an initial order based on updated demand forecast obtained at a later stage. The present paper utilizes expert judgment to assess potential impact(s) of contextual information acquired between two instants, in order to revise demand forecast. Additionally, the scant demand information available may not reveal the underlying demand distribution. In this context, we develop inventory models under distribution free newsvendor framework to determine optimal order quantity and weight factor considering also the revised demand forecast. The models consider bidirectional changes in demand and three cases of demand variability: a constant variance case (“CVC”), a constant coefficient of variation case (“CCVC”), and General Case (“GC”). The models developed in the first instance without constraints are subsequently extended by enforcing constraints for practical consideration such as limited storage space or maintenance of pre-defined service level. Moreover, these single-item models are extended to multi-items case to improve their practical utility. The closed form expressions are obtained for decision variables and lower bound of expected profit and their results are discussed using numerical examples. Results show economic benefits in revising the demand forecast using expert judgment and/or negative impact of constraints and/or negative role of demand variability. In addition, a case study is presented to illustrate the potential demand impact assessment and the application of the proposed models within real life circumstances.

A hybrid decision support system for analyzing challenges of the agricultural supply chain

Article scientifique ArODES

Bhaskar B. Gardas, Rakesh D. Raut, Naoufel Cheikhrouhou, Balkrishna E. Narkhede

Sustainable Production and Consumption, 2019, vol. 18, pp. 19-32

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Résumé:

Agricultural supply chain management includes all the events involved in moving products of the agricultural sector from the field to the customer, and is a crucial aspect ensuring the rich contribution of the agricultural sector to the economic growth of the nation. The purpose of this paper is to add value to the present knowledge base by ascertaining the challenges of the agricultural supply chain in India on the basis of a thorough literature survey and the Delphi technique. Following this, the decision-making trial and evaluation laboratory approach was used to model the identified challenges, explore the cause–effect interrelationship, and to develop the systematic hierarchical structures of challenges through an interpretive structural modeling methodology. The implementation of the approach in the Indian context led to the inference that two factors, namely limited integration among the national agricultural markets, and limited agricultural market infrastructure were the most important ones. The integrated model obtained as an output of this study intends to guide the agricultural policy- and decision-makers to improve the performance of the agricultural supply chain in India. Also, some essential recommendations have been given to improve the efficiency of the agricultural supply chain management.

La logistique et la supply chain, un métier du futur

Article professionnel ArODES

Naoufel Cheikhrouhou

Focus, 2019, mars, p. 2

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Replenishment behavior in sequential supply chains

Article scientifique ArODES

Amin Kaboli, Glardon Rémy, Nicolas Zufferey, Naoufel Cheikhrouhou

International Journal of Logistics Systems and Management, 2019, vol. 32, no 3-4, pp. 322-345

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Résumé:

Inventory managers do not predominantly follow normative optimization models. At best, they introduce a level of bounded rationality in their inventory replenishment decisions. This paper examines the behavior of inventory decision-makers under continuous review in a decentralized supply chain, using an experimental approach with unknown market demand and local information availability. The analysis reveals that not only the magnitude and the variability of order quantity tend to be larger, but also that the order-time intervals is lengthen and highly variable while moving upstream along the supply chain. The role of the inventory managers replenishment decisions on the echelon holding, backorder, and total costs, is also investigated. Finally, a normative model is designed and its solutions are compared to the experimental results. It is observed that humans do not operate in a perfectly optimal way, but are generally reluctant to risk increasing backorder costs and reducing inventory carrying cost, even if this would lead to lower total cost.

2018

Optimization of sample size and order size in an inventory model with quality inspection and return of defective items

Article scientifique ArODES

Naoufel Cheikhrouhou, Biswajit Sarkar, Baishakhi Ganguly, Asif Iqbal Malik, Rafael Batista, Young Hae Lee

Annals of Operations Research, 2018, vol. 271, no 2, pp. 445–467

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Résumé:

To ensure all products as perfect, inspection is essential, even though it is not possible to inspect all products after producing them like some special type products as plastic joint for the water pipe. In this direction, this paper develops an inventory model with lot inspection policy. With the help of lot inspection, all products need not to be verified still the retailer can decide the quality of products during inspection. If retailer founds products as imperfect quality, the products are sent back to supplier. As it is lot inspection, mis-clarification errors (Type-I error and Type-II error) are introduced to model the problem. Two possible cases are discussed for sending back products as defective lots are immediately withdrawn from the system and send back to supplier with retailer’s payment and for second case, retailer sends defective products during receiving next lot from supplier with supplier’s investment, like in food industry or in hygiene product industry. The model is solved analytically and results indicate that optimal order size and sample size are intrinsically linked and maximize the total profit. Numerical examples, graphical representations, and sensitivity analysis are given to illustrate the model. The results suggest that sending defective products maintaining the first case is the more profitable than the second case.

The dial-a-ride problem with electric vehicles and battery swapping stations

Article scientifique ArODES

Mohamed Amine Masmoudi, Manar Hosny, Emrah Demir, Konstantinos N. Genikomsakis, Naoufel Cheikhrouhou

Transportation research part E: logistics and transportation review, 2018, vol. 118, pp. 392-420

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Résumé:

The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for customers with special needs and/or disabilities. The DARP with Electric Vehicles and battery swapping stations (DARP-EV) concerns scheduling a fleet of EVs to serve a set of pre-specified transport requests during a certain planning horizon. In addition, EVs can be recharged by swapping their batteries with charged ones from any battery-swap stations. We propose three enhanced Evolutionary Variable Neighborhood Search (EVO-VNS) algorithms to solve the DARP-EV. Extensive computational experiments highlight the relevance of the problem and confirm the efficiency of the proposed EVO-VNS algorithms in producing high quality solutions.

A study on the heterogeneous fleet of alternative fuel vehicles :

Article scientifique ArODES

reducing CO2 emissions by means of biodiesel fuel

Mohamed Amine Masmoudi, Manar Hosny, Emrah Demir, Naoufel Cheikhrouhou

Transportation Research Part D: Transport and Environment, 2018, vol. 63, pp. 137-155

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Résumé:

In the context of home healthcare services, patients may need to be visited multiple times by different healthcare specialists who may use a fleet of heterogeneous vehicles. In addition, some of these visits may need to be synchronized with each other for performing a treatment at the same time. We call this problem the Heterogeneous Fleet Vehicle Routing Problem with Synchronized visits (HF-VRPS). It consists of planning a set of routes for a set of light duty vehicles running on alternative fuels. We propose three population-based hybrid Artificial Bee Colony metaheuristic algorithms for the HF-VRPS. These algorithms are tested on newly generated instances and on a set of homogeneous VRPS instances from the literature. Besides producing quality solutions, our experimental results illustrate the trade-offs between important factors, such as CO2 emissions and driver wage. The computational results also demonstrate the advantages of adopting a heterogeneous fleet rather than a homogeneous one for the use in home healthcare services.

2017

Equilibrium analysis in multi-echelon supply chain with multi-dimensional utilities of inertial players

Article scientifique ArODES

Gajanan Panchal, Vipul Jain, Naoufel Cheikhrouhou, Matthias Gurtner

Journal of revenue and pricing management, 2017, vol. 16, no. 4, pp 417–436

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Résumé:

In a supply chain, the importance of information elicitation from the supply chain players is vital to design supply chain network. The rationality and self-centredness of these players causes the information asymmetry in the supply chain and thus situation of conflict and non-participation of the players in the network design process. The supply chain players’ non-participation or reluctance to participate in supply chain contract is due to the dynamics of the system evolving with many competitive players in supply chain. In this paper, a game theoretical dynamic pricing model has been proposed to elicit the information from the players. With the objective of maximizing the social utility, efforts have been made to value behavioural issues of supply chain. On the other hand, the reluctance of player due to the information asymmetry is measured in the form of inertia experienced by the players due to anxiety and dynamics in the market. Distinguishing cases based on level of risk and variety of product are considered to show the effect of inertia on social utility and corresponding output for each echelon of supply chain. The paper provides supply chain managers an efficient decision making ability to achieve conflict-free outcome with the maximum utility.

Sustainability in the banking industry :

Article scientifique ArODES

a strategic multi-criterion analysis

Raut Rakesh, Naoufel Cheikhrouhou, Kharat Manoj

Business strategy and the environment, 2017, vol. 26, issue 4, pp. 550–568

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Résumé:

The current paper aims to develop an effective and integrated MCDM model for the evaluation of the sustainability practices in the banking services, employing a multi-stage, fuzzy MCDM model that integrates the Balanced Scorecard, fuzzy AHP and fuzzy TOPSIS. The approach aims to evaluate sustainability from the following four perspectives: financial stability, customer relationship management, internal business process and environment-friendly management system. A real implementation dealing with the six largest commercial banks in India is discussed. The results highlights the critical aspects of the evaluation criteria and the issues in improving sustainable banking performances. Regarding the sustainability issues, it is shown that the environment-friendly management system takes a back seat compared with the other criteria. Furthermore, the results show that there is a misunderstanding of the role that corporate social responsibility plays with respect to environmental issues. The developed evaluation model offers a valuable management tool for banks' administrators by assisting them in strategic choices in order to achieve their objective of sustainability and sustainable banking. Moreover, it offers a measuring tool with unique features that complements the emerging trend of integrated reporting considering uncertainty.

An integrated decision support system for berth and ship unloader allocation in bulk material handling port

Article scientifique ArODES

Saurabh Pratap, Ashutosh Nayak, Akhilesh Kumar, Naoufel Cheikhrouhou, Manoj Kumar Tiwari

Computers industrial engineering, 2017, vol. 106, pp. 386–399

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Résumé:

Berth allocation and material handling problems in ports are generally solved independently. This article provides a framework for aligning allocation decisions of berth and ship un-loader in an integrative manner. The ultimate goal of these decisions is to minimize the waiting time, operating time and ships priority deviation. As the sojourn time of a ship in port is costly, and given the scale and the complexity of the problem, a Decision Support System (DSS) is developed for the port authority. Two different approaches have been considered in this paper: (1) Solving the problem sequentially by decomposing the problem into two sub-problems- the berth allocation and the dynamic allocation of ship un-loaders in different berths (2) solving the problem by integrating berth allocation and dynamic allocation problem. Controlled Elitist Non-dominated Sorting Genetic Algorithm and Chemical Reaction Optimization are proposed in designing the DSS. Computational experiments are conducted on information provided from an Indian port. Results show that integrating berth and ship un-loader allocation achieves significant cost savings by considerably reducing the ship sojourn time in port.

A multi-objective optimization model for cooperative supply chain planning

Article scientifique ArODES

Wafa Ben Yahia, Naoufel Cheikhrouhou, Omar Ayadi, et al.

International Journal of Services and Operations Management, 2017, vol. 26, no. 2, pp. 211-237

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Résumé:

Generally, each member of a supply chain (SC) optimizes his own individual objective and accordingly, plans his activities (e.g. production operations, inventories) without considering a global perspective. The goal of this work is the development of a multiobjective optimization model for cooperative planning between different manufacturing plants belonging to the same SC. The model aims at minimizing simultaneously the total production cost and the average of inventory level for several items and over a multi-period horizon. To solve this problem, a non-dominated sorting elitist genetic algorithm (NSGA-II) is developed to derive the Pareto frontsolutions. Several tests are developed to show the performance of the solution method and the behavior of the cooperative planning model with respect to different demand patterns. The proposed model shows high performance in the tested cases with comparison to the literature.

2016

Optimal ordering policy for newsvendor models with bidirectional changes in demand using expert judgment

Article scientifique ArODES

Madhukar Nagare, Pankaj Dutta, Naoufel Cheikhrouhou

Opsearch, 2016, vol. 53, no. 3, pp. 620–647

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Résumé:

Demand forecast is a critical determinant of order quantity under newsvendor problem (NVP) framework and warrants major revision in the event of changing circumstances or happening of some unforeseen events having potential to alter the demand. Retailers of single period products such as fashion apparels are required to pass their orders far ahead of selling seasons and apply preseason two-stage ordering procedure, where an initial order (first stage) is followed by a final confirmed order (second stage). The enterprise forecasting experts may get additional information related to the occurrence of some unforeseen events that may significantly impact the initial demand estimation. In this paper, the potential impact of such events is combined using a weight factor to obtain revised demand forecasts. In this context, this paper develops inventory models under NVP framework to determine the optimal order quantity and weight factor on the basis of revised forecasts. Considering the bidirectional changes in demand, we formulate a unique objective function that operates as a profit maximization function for the positive demand adjustment and turns into a cost minimization function for the negative demand adjustment. Models developed without constraints at first instance are extended subsequently by incorporating constraints of budget limits, storage space capacity and required service level. Near closed form expressions of decision variables for four demand distributions with multiplicative demand forms are presented. The results demonstrate economic benefits of using revised demand through models developed, negative impact of constraints, and role of demand distribution entropy in determining the order size and expected profit.

An approximation approach to a trade-off among efficiency, efficacy, and balance for relief pre-positioning in disaster management

Article scientifique ArODES

Mohammad Rezaei-Malek, Reza Tavakkoli-Moghaddam, Naoufel Cheikhrouhou, Alireza Taheri-Moghaddam

Transportation research part E : logistics and transportation review, 2016, Vol. 93, pp. 485-509

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Résumé:

This work develops a multi-objective, two-stage stochastic, non-linear, and mixed-integer mathematical model for relief pre-positioning in disaster management. Improved imbalance and efficacy measures are incorporated into the model based on a new utility level of the delivered relief commodities. This model considers the usage possibility of a set of alternative routes for each of the applied transportation modes and consequently improves the network reliability. An integrated separable programming-augmented e-constraint approach is proposed to address the problem. The best Pareto-optimal solution is selected by PROMETHEE-II. The theoretical improvements of the presented approach are validated by experiments and a real case study.

2015

Review of full truck load (TL) transportation service procurement

Article scientifique ArODES

Ramanatan Jothi Basu, Nachiappan Subramanian, Naoufel Cheikhrouhou

Transport reviews, Vol. 35, no. 5, pp. 599-621

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Résumé:

The aim of this work is to review the literature on full truck load transportation service procurement and identify the gaps from the points of view of researchers and practitioners. Full truck load procurement is particularly considered for review since it is encountered more in freight movement than others forms and also it has many challenges. A framework is developed to have a systematic review of literature and the findings are discussed in detail. Some key findings include the simplistic assumption of demand pattern, less focus on nonprice variables, a limited number of case studies, a lesser consideration of sustainability aspects and the lack of detailed studies on emerging economies.

2024

Implicit attitudes towards risk :

Conférence ArODES

influences on newsvendor inventory decisions

Yvonne Badulescu, Felicia Soulikhan, Naoufel Cheikhrouhou

Proceedings of the 10th International Conference on Control, Decision and Information Technologies (CoDIT)

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Résumé:

This study addresses the role of implicit attitudes toward risk in inventory management, specifically in the context of the newsvendor problem (NVP). Using the Implicit Association Test (IAT), we explore how implicit attitudes toward risk influence newsvendors’ ordering decisions, focusing on deviations from profit-maximising solutions. Our methodology combines the NVP inventory exercise with IAT measurements. We find a negative correlation between participants’ implicit attitudes toward risk and the absolute deviation from the optimal order quantity, indicating that individuals with implicit attitudes toward risk are closer to neutrality deviate less from the profit-maximising solution. This supports our hypothesis that implicit risk attitudes impact such deviations. Our findings emphasise the importance of considering implicit attitudes toward risk in decision support systems for inventory management. Future research directions should explore the interplay of implicit attitudes toward risk with cognitive factors and its applicability in diverse contexts.

2023

Associations between social media attributes for demand forecasting of new products

Conférence ArODES

Yvonne Badulescu, Khelil Kassoul, Naoufel Cheikhrouhou

Proceedings of the 9th International Conference on Control, Decision and Information Technologies

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Résumé:

Data from social media is increasingly being utilized to better understand consumer preferences and prospective future demand. In this paper, the Interpretive Structural Modeling (ISM) and the Decision Making Trial and Evaluation Laboratory (DEMATEL) approaches are used to identify the interdependencies and cause-effect between social media attributes centered around better understanding the impact of attributes on product sales. The methodology is demonstrated on a the social media and sales data from a large food and beverage company. Results show that the “followers” and “comments” are interdependent and influenced by the “posts”, “impressions” and “videos”. The ISM and DEMATEL results are validated with Pearson's correlation coefficient.

Buffer allocation in unreliable production lines using infinitesimal perturbation analysis and genetic algorithm

Conférence ArODES

Khelil Kassoul, Rakesh D. Raut, Samir Brahim Belhaouari, Naoufel Cheikhrouhou

Intelligent systems for smart cities : select proceedings of the 2nd International Conference, ICISA 2023

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Résumé:

This paper presents an approach based on Genetic Algorithm (GA) and Infinitesimal Perturbation Analysis (IPA) technique to maximize the production rate in unreliable production lines. Unlike Traditional optimization techniques based on simulation which require a large number of simulations runs to find the optimal solutions, the proposed approach uses a long and unique simulation. Indeed, through this single simulation, IPA which forms the heart of the GA-IPA, gives a reliable estimate of the gradient of the production rate, and where the input solutions are provided by GA. This gradient is then integrated into a stochastic optimization algorithm that runs simultaneously with the simulation to select the optimal buffer allocation. Computational experiments on various production lines are presented and discussed. The average Production Rate (PR) is calculated with 5 runs for the largest problem and up to 50 runs for the smallest problem, showing on one hand that the developed GA-IPA algorithm clearly outperforms the seven benchmark algorithms taken from the literature, and proving on the other hand the rapid convergence of our algorithm.

Particle swarm optimization-based variables decomposition method for global optimization

Conférence ArODES

Khelil Kassoul, Samir Brahim Belhaouari, Naoufel Cheikhrouhou

Proceedings of the 8th International Arab Conference on Mathematics and Computations (IACMC 2023)

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Résumé:

The Particle Swarm Optimization (PSO) algorithm is a well-known nature-inspired technique used to tackle complex optimization problems, widely used by researchers and practitioners due to its simplicity and effectiveness. This paper introduces an improved version of PSO, called Particle Swarm Optimization-based Variables Decomposition Method (PSO-VDM), which utilizes a de-composition technique and a semi-random initialization strategy to divide the problem into subproblems, enhancing exploration and exploitation of the search space. To evaluate the proposed algorithm, a comparison with seven other well-known algorithms is conducted across 13 benchmark problems. The search performance of the algorithms is analyzed using both the test of Wilcoxon signed-rank and Friedman rank. The results of the comparisons and statistical analyses demonstrate that the strategies employed in the PSO-VDM algorithm make a significant contribution to the search process. These comparisons indicate that the PSO-VDM algorithm outperforms other state-of-the-art optimization algorithms in terms of solution quality, highlighting its potential to effectively tackle challenging optimization problems.

2022

Enhancing the design of a supply chain network framework for open education

Conférence ArODES

Barbara Class, Sandrine Favre, Felicia Soulikhan, Naoufel Cheikhrouhou

Computer supported education : 13th International Conference, CSEDU 2021, virtual event, April 23-25, 2021 : revised selected papers

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Résumé:

This article addresses the issue of education in the knowledge society. More precisely, it suggests conceptualizing Open Education as a supply chain in the form of a network of responsible citizens who switch roles and participate meaningfully in all education endeavours. This results in co-designing learning paths and creating common goods in the form of knowledge commons. These insights are gathered through a reflection conducted using a method of Scholarship of Teaching and Learning, a theoretical framework based on value creation and epistemologies of absences and emergences, and a case study

MCDM approach to select IoT devices for the reverse logistics process in the Clinical Trials supply chain

Conférence ArODES

Yvonne Badulescu, Manoj Kumar Tiwari, Naoufel Cheikhrouhou

Proceedings of the 10th IFAC Conference on Manufacturing Modelling, Management and Control

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Résumé:

Clinical trials companies are increasingly digitalising their reverse logistics flow supply chains to increase visibility and monitoring of their specimens using Internet of Things (IoT) devices, however the large number and diverse range of IoT solutions make it difficult to select the most appropriate one for the closed-loop supply chain of clinical trials which operates in reverse logistics flow. This paper identifies the criteria with which to evaluate IoT devices for the reverse logistics flow of clinical trials supply chains and proposes Analytic hierarchy process (AHP) combined with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach for the evaluation and selection of the best alternative. The approach is demonstrated on a real case and a sensitivity analysis and comparison to the AHP method validates the robustness of the solution.

2021

The competitive game table

Conférence ArODES

Shervin Zakeri, Naoufel Cheikhrouhou, Dimitri Konstantas

Proceedings of the MACSPro conference 2021

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Résumé:

In this paper, a new strategic game is proposed, called the competitive game table (CGT) to simulate the realworld battles and to predict the results by considering the interactions and interchanging data during the battles. CGT is a symmetric game that is developed to embrace the battles between two players. The game is grounded on twelve rules which conducts the game to predict the strategic posture of players in four scenarios including the aggressive, competitive, conservative, and defensive postures. The main objective of this study is to propose a comprehensive paradigm for modeling and predicting players’ behavior during and after battles. In addition, this paper is focused on modeling the market’s competitions by CGT to unearth the competitors’ behavioral patterns in a marketplace in order to determine the most efficient strategies.

A framework integrating internet of things and blockchain in clinical trials reverse supply chain

Conférence ArODES

Yvonne Badulescu, Naoufel Cheikhrouhou

Proceedings of advances in production management systems. Artificial intelligence for sustainable and resilient production systems. APMS 2021

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Résumé:

Efficiency and resilience of the clinical trials supply chain are of particular prevalence in the current global context. The unique characteristic of the reverse logistics flow in the supply chains for clinical trials is the foundation for the digital transformation framework presented in this paper. This paper proposes a novel framework that integrates internet of things (IoT) and blockchain technology for the reverse logistics supply chain for clinical trials. The framework is implemented in a Contract Research Organisation operating clinical trials in Europe and North Africa and results are discussed. The main contribution of the proposed novel framework is the integration and interaction of both IoT and blockchain in a reverse logistics process.

Implicit measurement method for consumer engagement in online brand communities

Conférence ArODES

Felicia Soulikhan, Bart Norré, Naoufel Cheikhrouhou

Proceedings of the 2021 IEEE International Conference on Social Sciences and Intelligence Management (SSIM)

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Résumé:

This paper investigates attitudinal and behavioral factors for Consumer Engagement (CE) in Online Communities of Fortnite. We develop an approach based on an online experimental survey and the Response Time Testing (RTT) method, which allows measuring the level of accessibility of attitudes. The results reveal that the affective dimension of consumer attitude is the key indicator for consumer engagement in online communities of Fortnite, that the frequency of member’s participation in online communities of Fortnite defines the intensity level of consumer engagement toward the community, and that strongly engaged consumers are more likely to recommend the community. This study provides the first known implicit experimental investigation of consumer brand engagement in online brand communities.

A framework for an open education supply chain network

Conférence ArODES

Barbara Class, Felicia Soulikhan, Sandrine Favre, Naoufel Cheikhrouhou

Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021)

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Résumé:

Open Education (OE) as a concept has been around for some years. Yet, a part from Open Educational Resources and Open Science, teachers and researchers are usually not aware of it. The aim of this paper is to conceptualise OE from the perspective of supply chain management (SCM), implicitly positioning it in the world of opens, the commons, the state and the market. Within a design-based approach, the concepts related to OE and SCM are presented, discussed and integrated in a novel framework dealing with the management of OE ecosystem. Findings show that keywords of the Open Education Supply Chain are cocreation, agile design and authority. The framework invites to create value from resources in a holistic way, balancing the commons, the state and the market in each stakeholder.

Dynamic cognitive-social particle swarm optimization

Conférence ArODES

Khelil Kassoul, Samir Brahim Belhaouari, Naoufel Cheikhrouhou

Proceedings of the 7th International Conference on Automation, Robotics and Applications (ICARA)

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Résumé:

Particle Swarm Optimization (PSO) is a heuristic optimization algorithm based on the modeling of the behavior of fishes and birds flock. This paper proposes a better version of PSO, named Dynamic Cognitive-Social PSO “DCS-PSO”, for global minima search by introducing optimal and dynamic cognitive and social scaling parameters without taking into consideration the inertia term. Furthermore, the velocity of each particle is controlled systematically at each iteration to avoid local minimum traps and to converge quickly and reliably towards the global minimum. The proposed algorithm is more suitable for high dimensional optimization problems and it has gotten over the limitations of classical Particle Swarm Optimization. Several experiments have been carried out, using the proposed DCS-PSO, to optimize thirteen benchmark functions and an important improvement has been observed, not only in terms of reaching the best global solutions but also in terms of convergence speed, compared to the existing benchmark approaches.

2019

Expert selection for humanitarian projects development :

Conférence ArODES

a group decision making approach with incomplete information relations

Abderrahmen Mediouni, Naoufel Cheikhrouhou

Proceedings of the 9th IFAC/IFIP/IFORS/IISE/INFORMS Conference Manufacturing Modelling, Management and Control MIM 2019

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Résumé:

This paper proposes a methodology to select an expert for the development of humanitarian and social projects based on multi-criteria decision-making with incomplete information relations to add more objectivity to the recruitment process. The proposed approach starts with assessing the decision makers (DMs) by means of Fuzzy Analytic Hierarchy Process (AHP). Then, Analytic Network Process (ANP) is used to weigh the criteria in order to take into account the interdependencies between criteria. In the recruiting process, an expert could be unable to assess all criteria. Therefore, the incomplete preference relations is used when a DM is unable to express his judgment. At a later stage, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is addressed to rank the different candidates. The proposed methodology is applied to a situation where an extra DM evaluates four DMs, and six criteria are used to select one candidate among five. The obtained criteria weights and the final ranking of the candidates are analysed and compared to an approach where there is no lack of information in the decision maker’s preferences.

2018

Evaluation of forecasting approaches using hybrid multicriteria decision-making models

Conférence ArODES

Yvonne Badulescu, Naoufel Cheikhrouhou

Proceedings of International Conference on Time Series and Forecasting (ITISE 2018)

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Résumé:

The demand forecast is the most influential input into enterprise activities planning thus creating a challenging issue for Demand Planning experts in model selection. Models including quantitative, qualitative and hybrid forecasting methods have been developed and are widely used. The literature reveals the use of several case-dependent error measures to evaluate forecasting accuracy, however, these performance indicators may at times differentiate in results making it more difficult in determining the most appropriate forecasting model for the users’ needs. This paper presents the development of two hybrid multi-criteria decision making approaches, AHP-TOPSIS and ANP-TOPSIS, to evaluate and rank the relative performances based on error measures of alternate forecasting models. Validation is provided through an industrial application using empirical data from a plastic bag manufacturer based on five models; three regression forecast models and two hybrid demand forecast models using expert judgement. Results illustrate that subjective adjustment by experts of mathematical forecasts consistently gives a higher ranking due to proximity to the ideal solution, and that collaborative adjustment limits the risk of outliers due to forecasting errors that could be done by a single decision maker.

Heterogeneous vehicle routing problems with synchronization :

Conférence ArODES

application to homecare scheduling routing problem

Mohamed Amine Masmoudi, Naoufel Cheikhrouhou

Actes GISEH 2018

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Résumé:

Home healthcare and home care depots are facing increasing demands and costs all over the world, because the increase of the number of dependent people that constitutes an important percentage of population and the growing necessity of some patients with special needs for support. Researchers are attracted by this issue that presents interesting customized scheduling and routing aspects. The objective is to optimize the assignment of visits to home caregivers and the sequence of visits execution. In reality, lunch break for caregivers is mandatory and heterogeneous fleet of vehicles are considered to services the patients. Thus, we introduce in this paper a new variant by taking into account breaks and heterogeneous fleet vehicles in addition to time windows and synchronization constraints. We call this specific problem as the Heterogeneous Vehicle Routing Problem with Synchronisation visit and Break (HVRPSB). We provided an Adaptive Large Neighboorhood search to solve this new variant. Numerical results on generated instances are provided to show the effectiveness of our developed algorithm to solve the HVRPSB.

Développement d’une démarche Lean pour la réorganisation et la planification des activités au sein d’un centre médical ambulatoire

Conférence ArODES

Christophe Compondu, Naoufel Cheikhrouhou, Isabelle Décosterd

Actes GISEH 2018

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Ontological model of competence management

Conférence ArODES

Chahinaze Fikri Benbrahim, Jaber Elbouhdidi, Hajira Bakkali, Naoufel Cheikhrouhou, Naoufal Sefiani, Kamal Reklaoui

Proceedings of the 11th International Colloquium of Logistics and Supply Chain Management LOGISTIQUA 2018

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Résumé:

Dans cet article, nous proposons une nouvelle approche concernant le modèle ontologique de la compétence basé sur la roue de Deming afin de faciliter la communication et la compréhension de l'information qui assure une bonne gestion des ressources au sein de l'entreprise. Nous choisissons l'ontologie car elle permet de définir et de gérer les compétences et les connaissances dans les entreprises en assurant une modélisation d'un ensemble de connaissances dans un domaine donné. Dans ce travail, nous développons un modèle qui décrit le processus du pilotage des compétences afin d'améliorer le traitement et le partage de l'information.

2017

Collaborative demand forecasting with integration of event-based judgements

Conférence ArODES

Naoufel Cheikhrouhou

Proceedings of Conference on innovation decision support using structured expert judgement

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Résumé:

Mathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes. Since forecasters have partial knowledge of the context and of future events, grouping and structuring the fragmented implicit knowledge, in order to be easily and fully integrated in final demand forecasts is the objective of this work. This paper presents a judgemental collaborative approach for demand forecasting in which the mathematical forecasts, considered as the basis, are adjusted by the structured and combined knowledge from different forecasters. The approach is based on the identification and classification of four types of particular events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence of the results. To validate the approach, two case studies were developed with forecasters from a plastic bag manufacturer and a distributor belonging to the food retailing industry. The results show that by structuring and combining the judgements of different forecasters to identify and assess future events, companies can experience a high improvement in demand forecast accuracy.

2016

Big data empowered logistics services platform

Conférence ArODES

Naoufel Cheikhrouhou, Paul de Vrieze, Emanuele Giovannetti, Shaofeng Liu, Ying Xie, Lai Xu, Hongnian Yu

27th European Regional Conference of the International Telecommunications Society

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Résumé:

Logistics section is one of the most important industrial sections to contribute to European economy. To improving efficiency and energy efficient of logistics, European Commission call new research theme ‘smart, green and integrated transport’ in its H2020 program. The paper presents a version on providing a cloud based platform for supporting big data empowered logistics services to respond this call. The research is supported by inter-disciplinary approaches, which brings experts from telecommunication, cloud computing, sensor networking, service-oriented computing, data analysis, transportation, and logistics areas to work together to provide real-world solutions for future logistics. The research questions and challenges of the platform are highlighted. Overall architecture and data collection are presented.

Estimate Supply Chain robustness using asymmetric loss functions

Conférence ArODES

Luca Canetta, Naoufel Cheikhrouhou, Rémy Glardon

Proceedings of the 2016 International Conference on Engineering, Technology and Innovation / IEEE lnternational Technology Management Conference (ICE/ITMC), June 13-15 2016, Trondheim

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Résumé:

Supply Chains have to be designed and managed holding simultaneously into account many different performance measures. Moreover, modern Supply Chains have to ensure satisfying performances despite an increasing degree of complexity and market uncertainty as well as be capable to limit the negative impacts of disruptive events. A multi-criteria robustness evaluation framework is proposed to deal with these challenges. The proposed approach allows to separately assessing the impact of various performance measures specifying tailor loss functions, being able to deal with non-linearity and asymmetric impacts. Moreover, an original Robustness Index is defined, in order to provide reliable estimations even in the presence of outliers and integrating information about kurtosis and skewness in the robustness estimation. The proposed framework is applied to a fictive industrial case to demonstrate its utilization and show the kind of analysis that can be done on the basis of the obtained results. The approach, simply requiring the definition of some parameters and the description of the characteristics of the Supply Chain configurations to be evaluated, is meant to be easily used by practitioners.

2015

The robust quay crane allocation for a discrete bulk material handling port

Conférence ArODES

Pratap Saurabh, Manoj Kumar B., Naoufel Cheikhrouhou, Prabla Pratap, Manoj Kumar Tiwari

2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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Résumé:

This study investigates the quay crane allocation problem with respect to vessel assigned to a particular discrete berth at a bulk material handling port. In the proposed model,vessels at the anchorage are berthed on a First in, first out (FIFO) basis at the port, and then the quay cranes are assigned to the berth dynamically before berthing and during unloading of the vessel. To solve the model, we used the Block Based Genetic Algorithm (BBGA) and Genetic Algorithm (GA). Computational study is conducted using the real data provided by a port located on theEastern Coast of India.

Decision support system for discrete robust berth allocation

Conférence ArODES

Saurabh Pratap, Ashutosh Nayak, Naoufel Cheikhrouhou, Manoj Kumar Tiwari

IFAC Papers Online : proceedings of the 15th IFAC Symposium on Information Control Problems in Manufacturing

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Résumé:

This paper aims to develop a decision support system for bulk material handling ports in relation to ship scheduling and discrete berth allocation. Ship scheduling according to available discrete berths and to customer priority is a complex problem. A multiobjective formulation is then proposed to model the problem in minimizing ship waiting times and deviation of customer priority. An modified Non-sorting Genetic Algorithm (Mod-NSGA II) is proposed to solve the problem in large-scale realistic environments. Utility of the developed decision support system in achieving good utilization of the available berths and resources is demonstrated using illustrative scenarios inspired from a real port management case.

The influence of forecast information sharing on behavioral inventory management in supply chains

Conférence ArODES

Naoufel Cheikhrouhou, Sylvain Hirth, Remy Wagner, Philippe Wieser

International work-conference on time series

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Résumé:

The literature reveals the complexity of identifying the attributes, the factors and the mechanisms that would influence trust between supply chain partners. Moreover, the way trust influences the performance of a supply chain is still a subject that remains to be thoroughly researched since trust is a subjective issue that has several dimensions. This paper focuses on the influence of forecast sharing on trust and the way the latter itself influences the supply chain performances. An experimental approach is developed to simulate the supply chain environment and the related decisions. The objective is to control the environment, so that, the trust level can be assessed by manipulating the information sharing attributes of forecasts. Moreover, we assess the relationship between trust and performance. Results show strong correlations not only between trust and supply chain performance, but also between trust and the attributes of the information shared on forecast.

Réalisations

PEOPLE@HES-SO
Annuaire et Répertoire des compétences

Cheikhrouhou Naoufel

Professeur HES ordinaire

Compétences principales

supply chain

Logistique

Operations Management

Recherche quantitative

Modélisation de systèmes complexes

System simulation

Professeur HES ordinaire

Chargé de cours

PEOPLE@HES-SO Annuaire et Répertoire des compétences

Cheikhrouhou Naoufel

Professeur HES ordinaire

Compétences principales

supply chain

Logistique

Operations Management

Recherche quantitative

Modélisation de systèmes complexes

System simulation

Professeur HES ordinaire

Chargé de cours

PEOPLE@HES-SO
Annuaire et Répertoire des compétences