Multi-objective Optimization for Partial Disassembly Line Balancing with Goaldriven Discrete Cuckoo Search

2018 ◽  
Vol 30 (4) ◽  
pp. 681
Author(s):  
Liuke Li ◽  
Zeqiang Zhang ◽  
Chao Guan ◽  
Lin Jia
2019 ◽  
Vol 11 (24) ◽  
pp. 6969 ◽  
Author(s):  
Jianhua Cao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Zelin Zhang ◽  
Xiang Liu

Disassembly is an indispensable part in remanufacturing process. Disassembly line balancing and disassembly mode have direct effects on the disassembly efficiency and resource utilization. Recent researches about disassembly line balancing problem (DLBP) either considered the highest productivity, lowest disassembly cost or some other performance measures. No one has considered these metrics comprehensively. In practical production, ignoring the ratio of resource input and value output within remanufacturing oriented disassembly can result in inefficient or pointless remanufacturing operations. To address the problem, a novel multi-efficiency DLBP optimization method is proposed. Different from the conventional DLBP, destructive disassembly mode is considered not only on un-detachable parts, but also on detachable parts with low value, high energy consumption, and long task time. The time efficiency, energy efficiency, and value efficiency are newly defined as the ultimate optimization objectives. For the characteristics of the multi-objective optimization model, a dual-population discrete artificial bee colony algorithm is proposed. The proposed model and algorithm are validated by different scales examples and applied to an automotive engine disassembly line. The results show that the proposed model is more efficient, and the algorithm is well suited to the multi-objective optimization model.


Author(s):  
Yilin Fang ◽  
Hanke Zhang ◽  
Quan Liu ◽  
Zude Zhou ◽  
Bitao Yao ◽  
...  

Abstract In the disassembly line balancing problem, the disassembly time of task is usually uncertain due to the influence of various factors. Interval number theory is very suitable to solve this problem. In this paper, a new interval mathematical model is proposed and the objectives are to minimize the cycle time and the total energy consumption of robots. To solve this problem, an evolutionary algorithm named γ based-NSGA-II for the interval multi-objective optimization is proposed. This algorithm directly solve the original interval multi-objective optimization problem by using interval Pareto dominance and interval crowding distance, rather than transforming the problem into a determined parameter optimization problem, which can retain the uncertain information, making the solution more reliable. And the local search operator is proposed to strength the local search ability of the algorithm. Experiment is executed in the three scale problems. By comparing the value of HV-U and HV-D, the influence of γ on the convergence, distribution and uncertainty of the algorithm is analyzed, and the optimal value of γ for this problem is found. On this basis, the performance of the proposed γ based-NSGA-II is compared with NSGA-II and MOEA / D by the value of IGD. The results show that the proposed algorithm has good performance in the small and medium scale problems.


2020 ◽  
Vol 56 ◽  
pp. 484-500 ◽  
Author(s):  
Yuanjun Laili ◽  
Yulin Li ◽  
Yilin Fang ◽  
Duc Truong Pham ◽  
Lin Zhang

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