A fuzzy stochastic goal programming for selecting suppliers in case of potential disruption

Abstract

ABSTRACTDue to globalization and the new characteristics of the business, companies face various challenges to ensure their continuity and competitive advantages. COVID-19 pandemic can be an extreme event that will eventually force many businesses and all industries to redesign and transform their global supply chain model? Challenges concerning mainly reducing the operating cost which is based on selecting the optimal suppliers to provide a reliable product. This study contributes to solving a supplier selection problem under disruption risk due to the lack of literature reviews with a lack of multi-methodological perspective for the fuzzy stochastic notions and quantitative techniques for the quantification of risk alternatives. Prior studies are neglecting to consider the value of risk and prefer to discover chances for optimizing anticipated costs or profits. This study proposed a fuzzy stochastic goal programming approach for selecting the optimal supplier under disruption risk. The proposed model incorporates multiple criteria such as capacity, stochastic demand, and probability of disturbance. The problem of stochastic combinatorial optimization obtained is presented as a program of fuzzy random aim by integrating techniques of value at risk and conditional risk value. Numeric samples and calculation results are included. The results of the models help the decision-maker to optimize the selection of suppliers in the event of a disturbance risk problem by an estimated value at risk and by simultaneously minimizing the conditional value of the risk and demonstrate the efficacy and acceptability of the created risk-averse technique as well as the effects of risk factors on our model behavior.KEYWORDS: Screening supplierrisk of disturbancefuzzy stochastic objectiveConditional value of riskrisk aversion Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationNotes on contributorsFaiza HamdiFaiza Hamdi is an Assistant Professor at the College of Business, University of Jeddah in Saudi Arabia, where she is an integral member of the Supply Chain Management Department. Dr. HAMDI brings a diverse academic background to her role, having earned a Ph.D. in Quantitative Methods from the University of Sfax, Tunisia, and another in Industrial Engineering from the University of Toulouse, France. Her academic pursuits align closely with her research interests, which encompass a broad spectrum of subjects within the field of Supply Chain Management (SCM). Dr. HAMDI's expertise extends to areas such as logistics, optimization, inventory management, simulation, lean manufacturing, and green supply chain practices. Currently, her research is particularly focused on the intricate realms of optimization in supply chain management and the dynamic landscape of risk management within this domain.Laila MessaoudiLaila Messaoudi serves as an Assistant Professor at Gabes University in Tunisia. She earned her Ph.D. in quantitative methods from the University of Sfax, Tunisia. Her primary areas of research interest encompass Fuzzy Stochastic Optimization and Portfolio Management. Her ongoing research is centered around the fields of Optimization, Data Science, and Machine Learning.Jalel EuchiJalel Euchi serves as an Assistant Professor at Gafsa University, Tunisia, where he actively contributes to the academic landscape. He earned his Ph.D. in Computer Science in 2011, specializing in optimization and transportation problems, from Le Havre University in France. Additionally, Jalel Euchi is an Associate Researcher affiliated with the OLID Laboratory at Sfax University, Tunisia. His research portfolio showcases a wealth of publications in esteemed journals such as 4OR, IJOR, Energy Reports, Logistics, Energy Systems, Renewable and Sustainable Energy Reviews, Swarm and Evolutionary Computation, Management Decision, JORS, and Vehicular Communications. He also dedicates his expertise to peer-reviewing articles for prominent publishers like Springer, IEEE, Taylor and Elsevier. Furthermore, Jalel Euchi is a dedicated life member of the Operational Research Society of Tunisia. His primary research interests encompass a wide spectrum of topics, including complex vehicle routing problems, the development of heuristics and meta-heuristic algorithms to tackle NP-Hard problems, computational operations research, decision-making processes, and research related to logistics and supply chains.