Optimizing a Disassembly Sequence Planning with Success Rates of Disassembly Operations via a Variable Neighborhood Search Algorithm

Abstract

Disassembly planning is an effective way to recycle valuable parts or materials from end-of-life products. Due to the parts are easily affected by some unpredictable factors in the real disassembly process (such as defective parts and human factors), there may be failure operations that cannot successfully disassemble the corresponding parts in the real world. Therefore, a partial disassembly sequence planning with uncertainty is proposed in the paper, where the success rates of disassembly operations are formulated to model an expected profit-based disassembly sequence planning. Also, a general variable neighborhood search algorithm is developed to optimize the mathematical model. Meanwhile, in the approach, a heuristic procedure is utilized to obtain a high-quality initial solution, and four different neighborhood structures are introduced to improve the disassembly solution. Its superiority and effectiveness are well illustrated by two case studies and comparison with two existing metaheuristics, i.e., artificial bee colony algorithm and genetic algorithm.