Multiobjective Ecological Strategy Optimization for Two-Stage Disassembly Line Balancing With Constrained-Resource

Abstract

Effective disassembly of obsolete products is critical for remanufacturing, recycling and recycling. However, existing disassembly studies have few or no resource constraints, such as limited numbers of disassembly operators and tools. Two-stage disassembly model is used to improve the flexibility to adapt to remanufacturing market demand. Based on existing references, this paper establishes a multiobjective two-stage disassembly model based on two-stage data envelopment analysis (two-stage DEA). The resource-constructed two-stage DEA model in this paper fully considers the dynamic configuration information of the output factors in each stage. To avoid the problem where the overall efficiency is optimal, the research constructs a resource constrained efficiency two-stage disassembly model. First, with the goal of minimum disassembly time, economy, energy consumption and environment, this study proposes eNSGA-II. By using mixed mutation and ecological evolution strategies, a well-distributed noninferior solution set is obtained. Second, the group of 16 disassembly schemes is used as decision-making units (DMUs) of the two-stage DEA. A comparison with Chen's model rankings shows that the model in this paper performs better. DMU 6 has the highest efficiency ranking, which is the best disassembly solution. Finally, the effectiveness of the proposed algorithm is verified by examples. Compared with NSGA-II and SPEA-II, eNSGA-II shows better performance and effectiveness. INDEX TERMS Disassembly, eNSGA-II, remanufacturing, resource constrained, two-stage DEA.

Bi-objective optimization-based multi-criteria decision-making framework for disassembly line balancing and employee assignment problem

“…Two studies, in which the Pareto optimal solutions obtained by solving the multi-objective mathematical models in the DALBP problem with any scalarization approach, are listed using MCDM techniques carried out by Yang et al (2019) and Yuan et al (2020). Yang et al (2019) used fuzzy Analytic Hierarchy Process (AHP) to evaluate the Pareto optimal solution and obtain the most satisfactory disassembly order.…”

“…Yang et al (2019) used fuzzy Analytic Hierarchy Process (AHP) to evaluate the Pareto optimal solution and obtain the most satisfactory disassembly order. On the other hand, the Pareto optimal solutions obtained by the non-dominant sequencing genetic algorithm approach were ranked by Data Envelopment Analysis (DEA) (Yuan et al, 2020). In the RDALBP literature, only Liu et al (2020) used Analytic Network Process (ANP) to determine the weights of the objectives.…”

“…Two studies, in which the Pareto optimal solutions obtained by solving the multi-objective mathematical models in the DALBP problem with any scalarization approach, are listed using MCDM techniques carried out by Yang et al. (2019) and Yuan et al. (2020).…”