A hybrid metaheuristic algorithm for a data driven leagile sustainable closed-loop supply chain model under disruption risks

Abstract

In the current situation, taking into consideration the environmental and social issues are related with the production and distribution of products in supply chain systems, due to the global concerns related with emitting lots of greenhouse gases within the manufacturing process and overlooking the major needs of public. This paper proposes a new multi objective model in the area of closed loop supply chain problem integrated with lot sizing by considering lean, agility and sustainability factors simultaneously. Next, a robust possibilistic programming approach is applied to handle the uncertainty of the model. To increase the responsiveness of the system, a fuzzy c-means clustering method is applied to select the potential locations based on the proximity to local customers. In the following, a new hybrid metaheuristic algorithm is developed to deal with large size problems efficiency and to assess the impact of using a single-based initial solution as the income for the second phase of the proposed hybrid algorithm. To ensure about the effectiveness of the proposed algorithm, another well-known metaheuristic algorithm is developed too. The results reached from experiments on different test problems approve the superiority of the hybrid metaheuristic algorithm to find better solutions.

Ranking Agility Factors to Reliably Sustain a Green Industrial Supply Chain Using the Fuzzy Analytic Network Process and Ordinal Priority Approach

“…Aghamohamadi-Bosjin et al [21] proposed a multi-objective model to deal with the closed-loop supply chain problem considering the agility, sustainability, and lean factors simultaneously. A robust possibilistic programming method handled the model's uncertainty.…”