Multi-objective Optimizer with Collaborative Resource Allocation Strategy for U-shaped Stochastic Disassembly Line Balancing Problem

2021 ◽  
Author(s):  
Song Xu ◽  
XiWang Guo ◽  
ShiXin Liu ◽  
Liang Qi ◽  
Shujin Qin ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Line Balancing ◽  
Allocation Strategy ◽  
Multi Objective ◽  
Disassembly Line Balancing ◽  
Resource Allocation Strategy ◽  
Disassembly Line

Model review and algorithm comparison on multi-objective disassembly line balancing

2020 ◽  
Vol 56 ◽  
pp. 484-500 ◽  
Author(s):  
Yuanjun Laili ◽  
Yulin Li ◽  
Yilin Fang ◽  
Duc Truong Pham ◽  
Lin Zhang
Keyword(s):  
Line Balancing ◽  
Multi Objective ◽  
Algorithm Comparison ◽  
Disassembly Line Balancing ◽  
Model Review ◽  
Disassembly Line

scholarly journals A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem

2019 ◽  
Vol 11 (24) ◽  
pp. 6969 ◽  
Author(s):  
Jianhua Cao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Zelin Zhang ◽  
Xiang Liu
Keyword(s):  
Optimization Model ◽  
Optimization Method ◽  
High Energy ◽  
Line Balancing ◽  
Multi Objective Optimization ◽  
Automotive Engine ◽  
Multi Objective ◽  
Proposed Model ◽  
Disassembly Line Balancing ◽  
Disassembly Line

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.

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