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Scheduling jobs in flowshops with the introduction of additional machines in the future

机译:通过将来引入更多机器来安排流水车间的作业

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The problem of scheduling jobs to minimize total weighted tardiness in flowshops, with the possibility of evolving into hybrid flowshops in the future, is investigated in this paper. As this research is guided by a real problem in industry, the flowshop considered has considerable flexibility, which stimulated the development of an innovative methodology for this research. Each stage of the flowshop currently has one or several identical machines. However, the manufacturing company is planning to introduce additional machines with different capabilities in different stages in the near future. Thus, the algorithm proposed and developed for the problem is not only capable of solving the current flow line configuration but also the potential new configurations that may result in the future. A meta-heuristic search algorithm based on tabu search is developed to solve this NP-hard, industry-guided problem. Six different initial solution finding mechanisms are proposed. A carefully planned nested split-plot design is performed to test the significance of different factors and their impact on the performance of the different algorithms. To the best of our knowledge, this research is the first of its kind that attempts to solve an industry-guided problem with the concern for future developments.
机译:本文研究了调度作业以最大程度地减少流程车间中总加权拖尾率的问题,并且有可能在将来演变为混合流程车间。由于这项研究受到行业中实际问题的指导,因此所考虑的流程车间具有相当大的灵活性,从而刺激了这项研究的创新方法的发展。目前,流水车间的每个阶段都有一台或几台相同的机器。但是,制造公司计划在不久的将来在不同阶段引入具有不同功能的其他机器。因此,针对该问题提出和开发的算法不仅能够解决当前的流线配置,而且还能够解决将来可能出现的潜在新配置。开发了一种基于禁忌搜索的元启发式搜索算法,以解决这一NP困难的行业指导问题。提出了六种不同的初始解寻找机制。执行精心计划的嵌套拆分图设计,以测试不同因素的重要性及其对不同算法性能的影响。据我们所知,这项研究是尝试解决与未来发展相关的行业指导问题的同类研究。

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