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首页> 外文期刊>International Journal of Production Research >Guided genetic algorithms for solving a larger constraint assembly problem
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Guided genetic algorithms for solving a larger constraint assembly problem

机译：指导遗传算法解决更大的约束组装问题

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摘要

Assembly planning calls for the subtle consideration of certain limitation factors such as geometric features and tools so as to work out a specific assembly sequence. From the assembly sequence, all parts will be turned into a product. It is evident that the degree of complexity of the assembly problem will increase when the number of constraints is larger. Using Genetic Algorithms (GAs) to solve the assembly sequence features speed and flexibility can fit the requirements of various domains. In the case of larger constraint assembly problems, however, GAs will generate a large number of infeasible solutions in the evolution procedure, thus reducing the efficiency of the solution-searching process. Traditionally, using GAs is a random and blind-searching procedure in which it is not always the case that the offspring obtained through the evolutionary mechanism will meet the requirements of all limitations. In this study, therefore, Guided-GAs are proposed wherein the proper initial population and the alternation of crossover and mutation mechanisms are covered to overcome assembly planning problems that contain large constraints. The optimal assembly sequence is obtained through the combination of Guided-GAs and the Connector-based assembly planning context as previously suggested. Finally, practical examples are offered to illustrate the feasibility of Guided-GAs. It is found that Guided-GAs can effectively solve the assembly planning problem of larger constraints.
机译：组装计划要求对某些限制因素（例如几何特征和工具）进行细微考虑，以制定出特定的组装顺序。从组装顺序开始，所有零件都将变成产品。显然，当约束的数量更大时，组装问题的复杂程度将增加。使用遗传算法（GA）求解装配序列特征的速度和灵活性可以满足各个领域的要求。但是，在约束组装问题较大的情况下，遗传算法会在演化过程中生成大量不可行的解决方案，从而降低了解决方案搜索过程的效率。传统上，使用遗传算法是一种随机且盲目的搜索程序，在这种情况下，通过进化机制获得的后代并不总是能够满足所有局限性的要求。因此，在这项研究中，提出了引导式遗传算法，其中涵盖了适当的初始种群以及交叉和突变机制的交替，以克服包含较大约束的装配计划问题。如前所述，通过将Guided-GA与基于连接器的装配计划环境相结合，可以获得最佳的装配顺序。最后，提供了实际示例来说明Guided-GA的可行性。发现Guided-GA可以有效解决约束较大的装配计划问题。

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