An Improved GA with Matrix-Coding for Optimizing a Complex Disassembly Sequence Problem on ELV

Abstract

Optimal disassembly sequencing is an NP-hard problem and has always been an ambition for industry production. In the context of increasing public concerns over environmental impacts, in addition to the feasibility of a disassembly sequence, dismantling enterprises have to consider the relationship between potential profits and the impacts. Thus, an ideal disassembly sequence should weight these three factors comprehensively. Up to now, an appropriate ELV disassembly sequence still mainly relies on people’s intuitive experience and seeking an optimal disassembly sequencing method assumes enormous importance. This paper aims to address the optimal disassembly sequencing problem of ELVs by means of an improved genetic algorithm, in which a matrix coding mechanism and an elite strategy are employed. The weight of different factors can be adjusted according to the actual conditions of factories. The paper gives a case and a series of Pareto fronts are obtained. The effects of population size and maximum evolutionary time on the Pareto solutions were investigated. Ultimately, the optimal Pareto disassembly sequence corresponding to balanced profit and environmental impact is achieved, thereby providing an appropriate disassembly depth defined by the aforementioned disassembly sequence. This can contribute to timely disassembly decisions for end-of-life vehicle (ELV) dismantling enterprises, achieving a cost-effective disassembly process for survival in the context of growing environmental concerns. This paper seeks to offer a viable decision-making approach prior to real disassembly of ELVs by detailing a Pareto disassembly depth and sequence.