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Supply Chain management

Description

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Membres

Jihene Tounsi Jihene Tounsi
Chaima Romdhani Chaima Romdhani

Publications

  • 2023
    Chaima Romdhani, Jihene Tounsi, Said Gattoufi

    Lateral Transshipment in Two-Echelon Inventory Control for Sustainable Pharmaceutical Supply Chain

    Conference: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), 2023

    Résumé

    Efficient inventory management (IM) presents an
    important key driver for supply chain (SC) sustainability.
    This latter becomes a crucial concern for decision-makers and
    managers in all domains, particularly in the matter of sensitive
    areas that affect human well-being, namely the pharmaceutical
    industry. Medicines IM for a sustainable Pharmaceutical Sup-
    ply Chain (PSC) brought further particularities compared to
    the traditional SCs. Besides the economic preoccupation, social
    and environmental issues might be considered. In this work, we
    assess the impact of the Lateral Transshipment (LT) strategy on
    the sustainability of the IM process. We compare the total costs
    of two cases, IM with and without LT strategy. We propose an
    IM model that seeks the optimal replenishment order quantity
    of multiple types of products and the shipment time in a
    two-echelon PSC under a centralized setting. The considered
    PSC consists of a pharmaceutical company (PC), a Pharma-
    distributor (PD), and multiple hospitals. The mathematical
    model takes into account the transportation costs including LT
    costs -in the case when LT is included- as well as shortage,
    and products with high deterioration rate costs. We attempt
    to minimize unused medicines leftover by minimizing the
    deterioration rate of products at both distributor and hospital
    sites.

  • Malek Abbassi, Abir Chaabani, Lamjed Ben Said

    An efficient chemical reaction algorithm for multi-objective combinatorial bi-level optimization

    Engineering Optimization, 54(4), 665-686, 2022

    Résumé

    The Bi-Level Optimization Problem (BLOP) is defined as a mathematical program with two nested optimization tasks. Although many applications fit the bi-level framework, however, existing resolution methods were most proposed to solve single-objective bi-level problems. Regarding Multi-objective BLOPs (MBLOPs), there do not exist too many previous studies because of the difficulties associated with solving these complex problems. Additionally, a recently proposed metaheuristic, called Non-dominated sorting Chemical Reaction Optimization (NCRO), has been successfully applied to solve single-level Multi-Objective Problems (MOPs). NCRO applies a quick-non-dominated sorting technique that makes it one of the most powerful search algorithms in solving MOPs. Based on these observations, a new Bi-level Multi-objective CRO method, called BMCRO, is proposed in this article for solving MBLOPs. The main idea behind BMCRO is to come up with good solutions in an acceptable execution time within the bi-level framework. Experimental results on well-established benchmarks reveal the outperformance of the proposed algorithm against a bi-level variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) which is developed for this purpose.

    Malek Abbassi, Abir Chaabani, Lamjed Ben Said

    An elitist cooperative evolutionary bi-level multi-objective decomposition-based algorithm for sustainable supply chain

    International Journal of Production Research, 60(23), 7013-7032, 2022

    Résumé

    Many real-life applications are modelled using hierarchical decision-making in which: an upper-level optimisation task is constrained by a lower-level one. Such class of optimisation problems is referred in the literature as Bi-Level Optimisation Problems (BLOPs). Most of the proposed methods tackled the single-objective continuous case adhering to some regularity assumptions. This is at odds with real-world problems which involve mainly discrete variables and expensive objective function evaluations. Besides, the optimisation process becomes exorbitantly time-consuming, especially when optimising several objectives at each level. For this reason, the Multi-objective variant (MBLOP) remains relatively less explored and the number of methods tackling the combinatorial case is much reduced. Motivated by these observations, we propose in this work an elitist decomposition-based evolutionary algorithm to solve MBLOPs, called ECODBEMA. The basic idea of our proposal is to handle, decomposition, elitism and multithreading mechanisms to cope with the MBLOP's high complexity. ECODBEMA is applied to the production–distribution problem and to a sustainable end-of-life products disassembly case-study based on real-data of Aix-en-Provence French city. We compared the optimal solutions of an exact method using CPLEX solver with near-optimal solutions obtained by ECODBEMA. The statistical results show the significant outperformance of ECODBEMA against other multi-objective bi-level optimisation algorithms.

    Chaima Romdhani, Jihene Tounsi, Said Gattoufi

    Two-echelon Inventory Management for Sustainable Pharmaceutical Supply Chain through Waste Reduction

    10th IFAC Manufacturing Modelling, Management and Control ConferenceAt: Nantes, France, 2022

    Résumé

    Improving sustainability in Pharmaceutical Supply Chain (PSC) becomes theprimary concern for its involved members. It lends major challenges to its management as it haseconomic, social, and environmental responsibilities more weighed than other supply chains.Providing the day-to-day need for medicines must be satisfied while taking into account theuse of the economic resource, customer satisfaction, and the impact of pharmaceutical wasteon the environment. Medicines waste affects healthcare expenses and harms the environment.Therefore, avoiding unused medication leftover through the pharmaceutical chain presents anefficient approach to attaining a sustainable supply of medicines. This article aims to deal withthe sustainability of a PSC by minimizing the deterioration rate of medicines at both distributorand hospitals sites. We propose an inventory management model based on a mixed-integernon-linear program (MINLP) that seeks the optimal replenishment order quantity of multipletypes of products and the shipment time in a two-echelon PSC consisting of a pharmaceuticalcompany (PC), a central pharmacy (CP), and multiple hospitals over a planning horizon, whileconsidering shipment costs, perishability, and shortage constraints.

    Maha Elarbi, Chaima Elwadi, Slim Bechikh, Zied Bahroun, Lamjed Ben Said

    An Evolutionary Multi-objective Approach for Coordinating Supplier–Producer Conflict in Lot Sizing

    International Journal of Information Technology & Decision Making, 21(02), 541-575, 2022

    Résumé

    Context. This paper deals with bilateral joint decision making in supply chains, and more specifically focuses on coordinating the decisions taken by the supplier and the producer in lot sizing. Research gap. Previous existing works in lot sizing have modeled the coordination task as a bi-level optimization problem. Unfortunately, the bi-level model causes a hierarchy between the two actors by making the leader imposing the decisions that suits his/her interests to the follower. This induces a significant conflict of interest between the two stakeholders because the leaders benefit is always greater than the follower’s one. Objective. The main goal of this work is to attenuate the conflict of interest issue between both actors by proposing a multi-objective model that alleviates the hierarchy and creates a win–win situation. Method. We propose an effective multi-objective lot sizing model, called Supplier-Producer Multi-Objective Lot Sizing (SP-MOLS); that alleviates the hierarchy between the actors’ objectives by assigning them the same importance degree and hence optimizing them simultaneously. The resolution of our SP-MOLS model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), as an effective meta-heuristic search engine, provides a set of trade-off solutions, each expressing a compromise degree between the two actors: the supplier and the producer. Results. To validate our approach, we use five test problems each containing 100 instances with a planning horizon of 10 periods and we analyze the obtained trade-off solutions using the compromise degree and the gap between costs as main consensus metrics. The obtained results reveal that a small sacrifice in the leader’s benefit could produce a significant improvement in the follower’s one. For instance, a 10% increase of the producer’s cost may generate a 42% decrease in the supplier’s one. Reciprocally, a 0.4% increase of the supplier’s cost may generate a 49% decrease in the producer’s cost. Method algorithmic improvement. As solutions of interests for both stakeholders are usually located within the extreme regions of the Pareto front, we propose NSGA-II with Focus on Extreme Regions (NSGA-II-FER) as a new variant of NSGA-II that focuses the search in the extreme regions of the Pareto front thanks to a modified crowding measure that is adaptively managed during the evolution process. This variant has shown its ability to eliminate dominance-resistant solutions and thus to come up with better extreme regions. Based on the experimental results, NSGA-II-FER is shown to have the ability to provide the decision makers with more convergent and more diversified extreme non-dominated solutions, expressing better trade-off degrees between both actors’ costs. Managerial implications. The promising results obtained by our proposal encourage decision makers’ to adopt a multi-objective approach rather than a bi-level one. From our personal perspective, we recommend running the three models (the multi-objective model and the two bi-levels ones); then analyzing the solutions of all models in terms of compromise degrees and logistic costs. This would allow both actors to observe how the hierarchy incurred by the bi-level models increases conflicts, while the multi-objective one generates solutions with much improved consensus degrees. Such observations will convince the supply chain stakeholders to adopt our multi-objective approach, while keeping an eye on the bi-level models’ solutions and the consensus degrees. Finally, we also recommend focusing on the extreme regions of the Pareto front since they contain rich solutions in terms of consensus. Such solutions are more convincing in the negotiation process and thus could lead to better win–win situations.

  • Malek Abbassi, Abir Chaabani, Lamjed Ben Said

    An investigation of a bi-level non-dominated sorting algorithm for production-distribution planning system

    In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA AIE’19, china, 819- 826, 2019

    Résumé

    Bi-Level Optimization Problems (BLOPs) belong to a class of challenging problems where one optimization problem acts as a constraint to another optimization level. These problems commonly appear in many real-life applications including: transportation, game-playing, chemical engineering, etc. Indeed, multi-objective BLOP is a natural extension of the single objective BLOP that bring more computational challenges related to the multi-objective hierarchical decision making. In this context, a well-known algorithm called NSGA-II was presented in the literature among the most cited Multi-Objective Evolutionary Algorithm (MOEA) in this research area. The most prominent features of NSGA-II are its simplicity, elitist approach and a non-parametric method for diversity. For this reason, in this work, we propose a bi-level version of NSGA-II, called Bi-NSGA-II, in an attempt to exploit NSGA-II features in tackling problems involving bi-level multiple conflicting criteria. The main motivation of this paper is to investigate the performance of the proposed variant on a bi-level production distribution problem in supply chain management formulated as a Multi-objective Bi-level MDVRP (M-Bi-MDVRP). The paper reveals three Bi-NSGA-II variants for solving the M-Bi-MDVRP basing on different variation operators (M-VMX, VMX, SBX and RBX). The experimental results showed the remarkable ability of our adopted algorithm for solving such NP-hard problem.