Designing a data-driven leagile sustainable closed-loop supply chain network

ABSTRACT

Nowadays, there is a great deal of interest in applying sustainability concepts for logistics and supply chain management. 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. In this regard, responsiveness, environmental, social and economic aspects are regarded in the model in addition to the capacity and service-level constraints. Most importantly, strategic and operational backup decisions are developed to increase the resiliency of the system against disruption of the facilities and routes simultaneously. In the following, a new hybrid metaheuristic algorithm comprised a parallel Multi-Objective Particle Swarm Optimization (PMOPSO) algorithm and a multi objective social engineering optimizer (MOSEO) is developed to deal with large size problems efficiency. To ensure about the effectiveness of the proposed hybrid algorithm, the results of this algorithm are compared with a Non-dominated Sorting Genetic Algorithm (NSGA-II).

KEYWORDS:

• Leagility
• sustainability
• supply chain
• disruption risks
• data mining
• multi-objective optimization

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

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