Development and comparison of multiple genetic algorithms and heuristics for assembly production planning
Development and comparison of multiple genetic algorithms and heuristics for assembly production planning
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用于装配生产规划的多种遗传算法和启发式的开发和比较
DOI：
10.1093/imaman/dpu016
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发表时间：
2016-04
影响因子：
1.7
通讯作者：
Wang K.（王恺）
中科院分区：
文献类型：
--
作者：
Lu, H.;He, L.;Huang, G. Q.;Wang K.（王恺）
在物料清单结构中具有树结构优先约束的装配作业是仅包含行结构优先约束的传统作业的广义版本。装配作业车间调度问题处理的是装配作业，而作业车间调度只处理传统作业。本研究探讨了不同遗传算法解决装配作业车间调度问题的能力。目标是最小化一组给定装配作业的最大完工时间（最大完成时间）。随机密钥GAs的不同主要体现在三个方面：解码、调度合理和个体重排。这三个因素分别有2级、7级和2级，从而产生28种不同的GAs。具体来说，我们使用前向/后向解码进行了GAs的全因子设计，0-6个局部验证步骤，有/没有单独的重排。目的是使用不同的因素设置来测试GAs的性能。作为基准，提出了两个启发式方法。Lingo，一个线性和非线性优化问题的软件工具也被用于解决，将时间限制为30分钟。实验结果表明，上述三个因素对气体的性能有显著影响。
Assembly jobs with tree-structured precedence constraints in their bill-of-materials structure are a generalized version of traditional jobs involving only line-structured precedence constraints. The assembly job shop scheduling problem deals with assembly jobs, in contrast to job shop scheduling which deals with only traditional jobs. This research explores the ability of different genetic algorithms (GAs) to solve the assembly job shop scheduling problem. The objective is to minimize the makespan (maximum completion time) of a given set of assembly jobs. Random key GAs are proposed which differ using three factors: decoding, schedule justification and individual rearrangement. The three factors have two, seven and two levels, respectively, resulting in 28 different GAs. Specifically, we have conducted a full factorial design of GAs using forward/backward decoding, 0–6 local steps of justification, with/without individual rearrangement. The aim is to test the performance of GAs using different factor settings. As benchmarks, two heuristics have been proposed. Lingo, a software tool for linear and non-linear optimization problems is also used for solution by setting the time limit to 30 min. The experiments have revealed significant effects of the aforesaid three factors on the performance of the GAs.