Asynchronous parallel disassembly sequence planning method of complex products using discrete multi-objective optimization

异步通信 序列(生物学) 计算机科学 过程(计算) 算法 产品(数学) 人口 优化设计 数学优化 工程类 数学 生物 遗传学 操作系统 机器学习 社会学 人口学 计算机网络 几何学
作者
Lemiao Qiu,Liangyu Dong,Zili Wang,Shuyou Zhang,Pengcheng Xu
出处
期刊:Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture [SAGE Publishing]
卷期号:236 (11): 1466-1482 被引量:8
标识
DOI:10.1177/09544054221077769
摘要

Disassembly is a necessary link to realize the integrity of product life cycle. Asynchronous Parallel Disassembly (APD) is an important way to achieve efficient disassembly. Due to the asynchronous start of disassembly tasks by operators in APD, the calculation complexity of disassembly sequence planning optimization increases more obviously with the increase of product complexity. Further, the disassembly sequence is a discrete numerical value, which is difficult to be efficiently realized by existing optimization algorithms. To overcome these difficulties, we introduced a complex products APD description. It helped to describe the APD problem with multiple disassembly resources. Based on the APD resources overall matrix, a three-objective, viz., the total disassembly time-consuming, the disassembly direction change times, and the disassembly tools replacement times, optimization model was constructed. To obtain the optimal disassembly planning sequence, the improved discrete NSGA-II (IDNSGA-II) was proposed, which introduced a novel population restart mechanism. The proposed method was verified in a bevel gearbox disassembly process from the EAS4633 rescue inspection equipment. The optimal results showed the APD sequence planning multi-objective optimized results obtained by the IDNSGA-II algorithm can be close to the single object optimized results. For the rest objects, the optimal solution obtained from the multi-objective genetic algorithm is obviously better.
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