Optimization of Multi-Factory Remanufacturing Processes with Shared Transportation Resources Using the ALNS Algorithm

Abstract

Decentralized manufacturing addresses growing challenges in centralized production by reducing costs for production, storage, and transportation through proximity to consumers. This study aims to optimize a multi-factory remanufacturing process, incorporating disassembly plants, manufacturing facilities, disassembly lines, and third-party logistics. The primary objective is to enhance system performance by balancing disassembly lines, optimizing transportation and routing, and minimizing workstation costs.\par

Given the NP-hardness and computational complexity of the disassembly line balancing problem (DLBP) and the vehicle routing problem with pickup and delivery (VRPPD), this paper proposes a multi-objective optimization framework. The framework builds on existing disassembly strategies and employs the adaptive large neighborhood search (ALNS) algorithm to improve delivery and transportation efficiency while maximizing execution profit.  To enhance practical applicability, the problem is systematically decomposed into two independent subproblems. A mixed-integer programming model is developed to optimize reverse supply chain performance and maximize profit. The model's feasibility and effectiveness are validated using the CPLEX solver, demonstrating its capability to address complex remanufacturing challenges.