Designing a multi-echelon closed-loop supply chain with disruption in the distribution centers under uncertainty

Abstract

<p style='text-indent:20px;'>According to the need for further cost reduction and improving the process of the organization in the direction of customer demand, the concept of the supply chain has become increasingly significant and the organizations seek to expand this concept within their organizational framework. In this regard, efficient planning of distribution of products in the supply chain by considering disruption has received more attention recently. In this study a multi-objective mixed-integer linear programming model is developed for a green multi-echelon closed-loop supply chain network design under uncertainty. Moreover, a partial disruption is considered for distribution centers where has not been studied enough in previous works. The fuzzy credibility constraint approach is applied to cover uncertainty. In the following, the ε-constraint method is presented to solve and validate the model in small-sized instances. Moreover, a Non-dominated Sorting Genetic Algorithm is developed for solving the large-sized problems. Results show that uncertainty has a crucial impact on objective functions where the increase of objective functions by increasing the level of uncertainty, which was observed in all categories. Furthermore, the proposed NSGA-Ⅱ is the best tool to deal with large-size problems where the EC method lacks the necessary efficiency.</p>

Application of fuzzy methods in green and sustainable supply chains: critical insights from a systematic review and bibliometric analysis

“…Future research can incorporate the concepts of reliability of the facilities and data uncertainties while designing the SC network, especially for solving large-sized problems. Non-dominated Sorting Genetic Algorithm (NSGA-II) is suggested to be used to solve the large-sized problems than the constraint method (Zadeh et al. , 2023).…”

“…ⅲ) another interesting research area can be to explore the barriers or drivers or success factors of GSCM and SSCM using various combinations of fuzzy methods such as fuzzy Delphi factor analysis followed by empirical research, case studies, fuzzy ISM, fuzzy MICMAC, fuzzy DEMATEL, etc. ⅳ) Some researchers have directed to use or compare the metaheuristics method like Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic Algorithm (NSGA) in multi-objective optimization problems (Zadeh et al, 2023). Besides these indicative research directions, the keyword analysis, integrated framework and classification of literature in seven categories have provided several topics and areas that can be studied in the future.…”

A scenario-based sustainable dual-channel closed-loop supply chain design with pickup and delivery considering social conditions in a natural disaster under uncertainty: a real-life case study

“…Like Akbari-Kasgari et al ( 2022 ) presented a MILP model in their study to create improvements in three economic, social, and environmental goals, which considered the CO 2 emissions for each vehicle per unit distance. Also, Zadeh et al ( 2023 ) developed a multi-objective mixed integer linear programming model for designing a green multi-layer CLSCN under uncertainty but considered the CO2 emissions in their model for transporting each product unit between different centers.…”

Supply Chain Management for Pre-Teacher Preparation of Higher Education in Thailand Model

“…Supply chain management involves the management of relationships both upstream and downstream between suppliers and customers to deliver superior value to the customers Jafari and Safarzadeh (2023). A typical supply chain encompasses various levels, including suppliers, manufacturers, distributors, retailers, and consumers, all working towards enhancing the organization's processes and effectively planning the distribution of goods within the supply chain Zadeh et al, (2023). Based on a study of academic research documents, the key elements of the supply chain can be summarized as presented in Table 2 below.…”