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Optimisation of a machine loading problem using a genetic algorithm-based heuristic

机译:使用基于遗传算法的启发式算法优化机器装载问题

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摘要

In the present work, apart from operating on the structure of a conventional genetic algorithm (GA), a heuristic which uses techniques like differential mutation probability, elitism and local search is used to produce near optimal solutions for large machine loading problems with less computational intensity. Two variants of the machine loading problem are analysed in the present work: single batch model and the multiple batch models. The sensitivity of the problem with respect to the tool capacity constraint is evaluated to find that moderately restricted problems requiring greater computational resources in comparison to lesser restricted and tightly restricted class of problems. The performance of various dispatching rules was compared to infer that the least slack principle fares better than the other tested dispatching rules. It is observed from the results, that the proposed heuristic is efficient in handling large and complex machine loading problems.
机译:在当前的工作中,除了在常规遗传算法(GA)的结构上进行操作外,还使用启发式技术(如差分变异概率,精英主义和局部搜索)来为计算量较小的大型机器负载问题提供接近最优的解决方案。在当前的工作中,分析了机器装载问题的两个变体:单批模型和多批模型。评估了该问题相对于工具能力约束的敏感性,以发现与较少约束和严格约束的问题类别相比,中等约束的问题需要更多的计算资源。通过比较各种调度规则的性能,可以推断出最小松弛原则的性能要优于其他经过测试的调度规则。从结果可以看出,所提出的启发式方法可以有效地处理大型和复杂的机器负载问题。

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