Adaptive Large Neighborhood Search for Cost-Effective and Efficient Decentralized Remanufacturing

Abstract

Centralized manufacturing is increasingly facing challenges from technological advancements and global economic integration. In response, decentralized manufacturing has become a viable solution that reduces production, storage, and transportation costs by providing products to consumers. This article proposes an optimized multi-factory remanufacturing process that integrates dismantling factories, manufacturing factories, dismantling workstations, and third-party logistics to improve overall system performance. It focuses on dismantling line balancing, efficient transportation and route planning, as well as minimizing the cost of dismantling plant workstations. This article introduces a multi-objective optimization method that improves existing disassembly schemes and enhances delivery and transportation stages through Adaptive Large Neighborhood Search (ALNS). This method aims to optimize the overall execution profit and transportation efficiency within the dismantling plan. In addition, the study introduced a mixed integer programming model to achieve maximum profit and improve the overall performance of the reverse supply chain. The proposed mathematical model has been validated using the CPLEX solver to confirm its accuracy and feasibility.