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Identical Parallel-Machine Scheduling and Worker Assignment Problem Using Genetic Algorithms to Minimize Makespan

机译:使用遗传算法将相同的并行机调度和工人分配问题最小化MakEspan

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Identical parallel machine scheduling problem for minimizing the makespan is a very important production scheduling problem which has been proven to be NP-hard. The problem further compounds with additional constraints. Genetic algorithms (GA) have shown great advantages in solving the combinatorial optimization problem in view of its characteristic that has high efficiency and that is fit for practical application. In this chapter we present a spreadsheet based GA approach for minimizing the makespan for scheduling of a set of tasks for identical parallel machines and worker assignment to machines. The results obtained from the proposed approach are compared with two sets of benchmark problems consisting of 100 problems each. It has been demonstrated that the performance of proposed approach is superior to the results that have been obtained earlier. The proposed approach produces optimal solution for almost 95% of the problems demonstrating the effectiveness of the proposed approach. An empirical analysis of GA parameters has also been carried out to see the effect on the performance of the proposed algorithm.
机译:最小化MakEspan的相同并行机器调度问题是一个非常重要的生产调度问题,已被证明是NP-HARD。该问题进一步具有额外约束的化合物。遗传算法(GA)鉴于其具有高效率的特性和适合实际应用的特性,对组合优化问题表示了很大的优点。在本章中,我们介绍了一种基于电子表格的GA方法,可最大限度地减少MapEspan,以便调度一组与机器的相同并行机和工人分配的任务。从所提出的方法获得的结果与两组基准问题进行比较,每个基准问题包括100个问题。已经证明,所提出的方法的性能优于前面获得的结果。拟议的方法为近95%的问题产生了最佳解决方案,证明了所提出的方法的有效性。还进行了对GA参数的实证分析,以便看到对所提出的算法性能的影响。

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