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Flowshop sequence-dependent group scheduling with minimisation of weighted earliness and tardiness

机译:流程序列依赖性组调度,最小化加权和迟到

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In this research, we approach the flowshop sequence-dependent group scheduling problem with minimisation of total weighted earliness and tardiness as the objective for the first time. A mixed integer linear programming model is developed to solve the problem optimally. Since the proposed research problem is proven to be NP-hard, a hybrid meta-heuristic algorithm based on the particle swarm optimisation (PSO) algorithm, enhanced with neighbourhood search is developed to heuristically solve the problem. Since the objective is a non-regular, a timing algorithm is developed to find the best schedule for each sequence provided by the metaheuristic algorithm. A lower bounding method is also developed by reformulating the problem as a Dantzig-Wolf decomposition model to evaluate the performance of the proposed PSO algorithm. The computational results, based on using available test problems in the literature, demonstrate that the proposed PSO algorithm and the lower bounding method are quite effective, especially in the instances with loose due date.
机译:在这项研究中,我们在最小化总加权和第一次目的的情况下最小化了流程序列依赖性组调度问题。开发了混合整数线性编程模型以最佳地解决问题。由于拟议的研究问题被证明是NP-HARD,因此开发了一种基于粒子群优化(PSO)算法的混合元 - 启发式算法,并利用邻域搜索增强,以启发式解决问题。由于目标是非常规,开发了一个定时算法来查找由成群质算法提供的每个序列的最佳计划。还通过将问题重新制定为Dantzig-Wolf分解模型来开发较低的限制方法,以评估所提出的PSO算法的性能。基于在文献中使用的可用测试问题的计算结果证明了所提出的PSO算法和较低的边界方法非常有效,尤其是在截止日期松散的情况下。

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