Retrieval of optimal solution(s) for a permutation flowshop scheduling problem within a reasonable computational timeframe has been a challenge till yet. The problem includes optimization of various criterions like makespan, total flowtime, earliness, tardiness, etc and obtaining a Pareto solution for final decision making. This paper remodels a discrete artificial bee colony algorithm for permutation flowshop scheduling problem executed through three different scenarios raised the analysis of time complexity measure. To enhance the search procedure, we have explored the alternative and combined use of two local search algorithms named as: iterated greedy search algorithm and iterated local search algorithm in our discrete artificial bee colony algorithm and the results are summarized with respect to completion time, mean weighted tardiness, and mean weighted earliness. The two algorithms are prioritised on insertion and swap of neighbourhood structures which will intensify the local optima in the search space. Further the performance of the algorithm is compared with the test results of multi-objective artificial bee colony algorithm. The result of our optimization process concludes with a set of non-dominated solutions lead to different Pareto fronts. Finally, we propose a chaotic based technique for order of preference by similarity to ideal solution (chaotic-TOPSIS) using a suitable chaotic map for criteria adaptation in order to enhance the decision accuracy in the multi-objective problem domain.

中文翻译：

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求解流水车间调度问题和决策的多目标人工蜂群算法和混沌TOPSIS方法

在合理的计算时间范围内检索置换流水车间调度问题的最佳解决方案一直是一个挑战。该问题包括优化各种标准，如完工时间、总流动时间、提前期、延迟等，并获得帕累托解决方案以进行最终决策。本文针对置换流水车间调度问题，重构了离散人工蜂群算法，通过三种不同的场景执行，提出了时间复杂度测度的分析。为了增强搜索过程，我们探索了两种局部搜索算法的替代和组合使用，称为：迭代贪婪搜索算法和迭代局部搜索算法在我们的离散人工蜂群算法中，结果总结了完成时间，平均加权迟到，和平均加权提前期。这两种算法优先考虑邻域结构的插入和交换，这将加强搜索空间中的局部最优。进一步将算法的性能与多目标人工蜂群算法的测试结果进行比较。我们的优化过程的结果是一组非支配解决方案导致不同的帕累托前沿。最后，我们提出了一种基于混沌的优先顺序技术，通过与理想解决方案的相似性（混沌-TOPSIS）使用合适的混沌图进行标准适应，以提高多目标问题域中的决策准确性。进一步将算法的性能与多目标人工蜂群算法的测试结果进行了比较。我们的优化过程的结果是一组非支配解决方案导致不同的帕累托前沿。最后，我们提出了一种基于混沌的优先顺序技术，通过与理想解决方案的相似性（混沌-TOPSIS）使用合适的混沌图进行标准适应，以提高多目标问题域中的决策准确性。进一步将算法的性能与多目标人工蜂群算法的测试结果进行比较。我们的优化过程的结果是一组非支配解决方案导致不同的帕累托前沿。最后，我们提出了一种基于混沌的优先顺序技术，通过与理想解决方案的相似性（混沌-TOPSIS）使用合适的混沌图进行标准适应，以提高多目标问题域中的决策准确性。