An MCDM-Based Multiobjective General Variable Neighborhood Search Approach for Disassembly Line Balancing Problem

Abstract

Due to the rapid technology advancement and market changes, products are becoming outdated and subsequently discarded faster than ever before. Recovery, recycling, and remanufacturing of end-of-life (EOL) products are getting more attention. Disassembly is indispensable to recycle and remanufacture EOL products, and a disassembly line is an efficient way to perform it. A disassembly line balancing problem (DLBP) aims at streamlining the disassembly activities such that the total disassembly time consumed at each workstation is approximately the same and approaching the cycle time. However, the assignment of disassembly operations to workstations in a disassembly shop should ensure the recovery of valuable components and reduce undesirable impact on the environment as much as possible. In this paper, a novel heuristic technique combining multicriterion decision making (MCDM) and general variable neighborhood search (GVNS) is proposed to solve the DLBP. Based on the characteristics of the DLBP, an innovative MCDM method based on fuzzy set theory, grey relational analysis, and Choquet fuzzy integral is developed to evaluate the performance scores and determine the ranking of disassembly tasks. Subsequently, an improved GVNS algorithm is employed to further balance a disassembly line with three objectives, in which a new metric is formulated to integrate with the ranking from MCDM. The proposed method not only takes a comprehensive objective system into consideration but effectively generates a good enough tradeoff disassembly solution. Finally, the proposed approach is illustrated with an example and compared with two other heuristics to show its efficacy in solving the DLBP.