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.

Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

“…Xing et al [19] used the disassembly time model to consider execution time and preparation time to solve the asynchronous parallel disassembly sequence planning problem. Liu et al [20] formulated a profit model to plan the disassembly sequence. Liang et al [21] proposed the energy consumption model for disassembly activities for a two-sided disassembly line balance.…”

Task allocation and planning for product disassembly with human–robot collaboration

“…An automated unfastening method was proposed in [30] to remove screws from a turbocharger cost-efficiently. There are other suggestions in the literature for maximizing profits with disassembly sequencing planning [213]- [215]; while these studies do not explicitly consider HRC, the proposed profit-maximizing plans could be adapted to the case of disassembly with HRC.…”

Integrating meta-heuristics and a Sarsa algorithm for disassembly scheduling problems with cycle time and hazard coefficients

“…ZMeta-heuristics are gradually applied for solving disassembly scheduling problems due to their excellent performance in balancing the computation cost and solution quality [28][29][30] , including particle swarm optimization (PSO) [31][32][33][34][35][36] , artificial bee colony (ABC) [37][38][39][40] , genetic algorithm (GA) [41][42][43][44][45][46] , and variable neighborhood search (VNS) [40,[47][48][49][50] . Kalayci et al propose a PSO based on variable neighborhood mutation operators to solve DLBPs [31] .…”