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A bottleneck-based heuristic for minimizing makespan in a flexible flow line with unrelated parallel machines

机译:基于瓶颈的启发式算法,可在不相关的并行机器的情况下最大程度地减少柔性流水线中的制造时间

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摘要

This study developed a bottleneck-based heuristic (BBFFL) to solve a flexible flow line problem with a bottleneck stage, where unrelated parallel machines exist in all the stages, with the objective of minimizing the makespan. The essential idea of BBFFL is that scheduling jobs at the bottleneck stage may affect the performance of a heuristic for scheduling jobs in all the stages. Therefore, in BBFFL, a variant of Johnson's rule is used to develop a bottleneck-based initial sequence generator (BBISG). Then, a bottleneck-based multiple insertion procedure (BBMIP) is applied to the initial sequence to control the order by which jobs enter the bottleneck stage to be the same as that at the first stage. Five experimental factors were used to design 243 different production scenarios and 10 test problems were randomly generated in each scenario. These test problems were used to compare the performance of BBFFL with several well-known heuristics. Computational results show that the BBFFL significantly outperforms all the well-known heuristics.
机译:这项研究开发了一种基于瓶颈的启发式算法(BBFFL),以解决瓶颈阶段的柔性流水线问题,在瓶颈阶段,所有阶段都存在无关的并行机,目的是最大程度地缩短制造时间。 BBFFL的基本思想是在瓶颈阶段调度作业可能会影响在所有阶段调度作业的启发式方法的性能。因此,在BBFFL中,使用Johnson规则的一种变体来开发基于瓶颈的初始序列生成器(BBISG)。然后,将基于瓶颈的多重插入过程(BBMIP)应用于初始序列,以控制作业进入瓶颈阶段的顺序与第一阶段的顺序相同。使用五个实验因素来设计243个不同的生产方案,并且在每个方案中随机产生了10个测试问题。这些测试问题用于将BBFFL的性能与几种著名的启发式算法进行比较。计算结果表明,BBFFL的性能明显优于所有知名的启发式算法。

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