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An ant colonial optimisation approach for no-wait permutation flow shop scheduling

机译:无等级排列流程商店调度的蚂蚁殖民优化方法

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This research aims to address the applications of variants of ant colony optimisation (ACO) approach to solve no-wait flow shop scheduling problem (NW-FSSP). The most suitable ACO algorithm out of basic algorithms has been selected and modified to achieve more purified results. The algorithm was coded in visual basic. The varied algorithm has been applied to the bench mark problems and results were compared with the results achieved previously by other researchers using different meta-heuristics. The research covers detailed steps carried out for application of basic ACO algorithms on bench mark problems, comparison of results achieved by application of basic ACO algorithms, selection of best out of basic algorithms, modification of selected basic algorithm and generation of varied ACO algorithm. The varied ACO algorithm gave reasonably good results for almost all the problems under consideration and was able to handle fairly large sized problems with far less computational time. Comparative analysis depicted that the proposed ACO algorithm performed better than genetic algorithm on large sized problems and better than Rajendran heuristic in almost all problems under considerations.
机译:本研究旨在解决蚁群优化(ACO)方法的变体来解决不等待流量店调度问题(NW-FSSP)的应用。选择并修改了基本算法中最合适的ACO算法以实现更多纯化的结果。该算法在Visual Basic中编码。多种算法已应用于基准标记问题,并将结果与​​使用不同元启发式的其他研究人员进行了比较。该研究介绍了在基准标记问题上应用基本ACO算法的详细步骤,通过应用基本ACO算法,选择最佳基本算法的选择,选择基本算法的修改以及各种ACO算法的产生的比较。各种ACO算法对于几乎所有正在考虑的问题给出了相当好的结果,并且能够处理相当大的大小问题,并且计算时间远远较低。比较分析所描绘的是,所提出的ACO算法比大型问题的遗传算法更好地表现优于遗传算法,而不是在考虑所考虑的几乎所有问题中的rajendran启发式。

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