Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards

Abstract

End-of-life (EOL) electromechanical products often have multiple failure characteristics and material hazard attributes. These factors create uncertain disassembly task sequences and affect the remanufacturing cost, environmental sustainability, and disassembly efficiency of the remanufacturing disassembly line system. To address this problem, a novel multi-constraint remanufacturing disassembly line balancing model (MC-RDLBM) is constructed in this article, which accounts for the failure characteristics of the parts and material hazard constraints. To assign the disassembly task reasonably, a disassembly priority decision-making model was presented to describe the relationship between the failure layer, the material hazards layer, and the economic feasibility layer. Furthermore, the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) optimization for the MC-RDLBM is improved. To increase the convergence speed of the algorithm, an initial population construction method is designed, which includes the component failure and material hazards. Moreover, a novel genetic algorithm evolution rule with a Pareto non-dominant relation and crowded distance constraint is established, which expands the search scope of the chromosome’s autonomous evolution and avoids local convergence. Furthermore, a Pareto grade-based evaluation strategy for non-dominant solutions is proposed to eliminate the invalid remanufacturing disassembly task sequences. Finally, a case study verified the effectiveness and feasibility of the proposed method.

Energy-Efficient Optimization of Two-Sided Disassembly Line Balance Considering Parallel Operation and Uncertain Using Multiobjective Flatworm Algorithm

“…The symbol (A l − B u ) ∨ 0 in Equation (2) represents the parameter at this position, taking the maximum value between A l − B u and 0. Meanwhile, in order to compare the size of a group of fuzzy numbers, a popular defuzzification technique is adopted to convert fuzzy numbers into crisp real numbers [30,33], the defuzzification method can be computed by Equation (5).…”

“…For example, in Figure 2, the fuzzy disassembly time of task 8 is (2,4,8) and that of task 16 is (2,4,6). Although the most likely disassembly time in the middle is 4 units, the results are not the same when using Equation (5) to defuzzify them into real numbers because their upper and lower bounds are different, and be converted into 4.5 and 4.0 units, respectively.…”

“…The "circular economy" and "remanufacturing" industries are in the ascendant, the recycling of end-of-life products such as black and white home appliances and automobiles has received increasing attention. The latest data showed that the number of EOL automobiles in China was about 2.615 million in 2020, the total weight of EOL vehicles was nearly 6.267 million tons, and each ton of the resources can recover 72.0% of waste steel, 6.0% of waste nonferrous metals, 6.3% of waste plastics, 4.4% of waste rubber and 1.7% of waste glass; if they are directly discarded, it will not only waste resources but also pollute the environment [2]. Disassembly, as an important stage after recycling and before reuse, plays an important role in the process from waste to available resources [3].…”

Parameter Optimization and Effect Analysis of Low-Pressure Abrasive Water Jet (LPAWJ) for Paint Removal of Remanufacturing Cleaning

“…Large amounts of end-of-life products scrapped directly have led to excessive waste of resources over recent years, which can be significantly improved by remanufacturing [1][2][3]. Remanufacturing is an industrial process whereby used products can be restored to useful life [4], including design [5], disassembly [6], cleaning [7], inspection [8], reconditioning [9], etc., among which cleaning is a particularly essential procedure because the cleanliness of cleaned substrate has a significant influence on the following procedures. The ideal state of remanufacturing cleaning is to remove all the contaminants on the surface without affecting the substrate [10].…”

An Algorithm for Arranging Operators to Balance Assembly Lines and Reduce Operator Training Time

“…Many ALBP methods have been examined in the study of manufacturing systems, including the heuristic algorithm [16][17][18][19][20][21][22][23][24], artificial bee colony algorithm [25][26][27], genetic algorithm [28][29][30], branch-and-bound algorithm [31][32][33][34], imperialist competitive algorithm [35], Pareto Greedy algorithm [36], memetic ant algorithm [37], whale optimization algorithm [38], recursive algorithm [39], fuzzy linear programming [40], swarm algorithm [41,42], simulated annealing algorithm [43], and data envelopment analysis [44]. The genetic algorithm [45,46] and artificial bee colony algorithm [47] have been used to solve the ALBP in remanufacturing systems. However, studies have seldom discussed how to arrange operators and group similarly skilled operators at the same workstation.…”