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An imperialist competitive algorithm mixed model assembly line sequencing problem on just in time system

机译:实时系统的帝国竞争算法混合模型流水线排序问题。

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Mixed-model lines are used to produce several kinds of models in small lots without carrying large inventories. The production sequence for the mixed-model sequencing problem depends on the goals of the production facility. In order to enjoy the useful application of these lines, it is vital to devise a schedule for assembling the different products to be determined. Based on the NP-hardness of the problem, this present paper introduces an imperialist competitive algorithm (ICA) in three phases so as to solve a just-in-time (JIT) sequencing problem where the diversity of production rates to be optimized.Performance of theICAwas compared against two other search heuristicsgenetic algorithm (GA) and simulated annealing (SA) in small, medium and large problems.To compare presented algorithm with previous ones, an extensive computational study on 3 sets of benchmark problems has been conducted. Experimental results showed that our algorithm outperforms the previous algorithms, in respect of comparison metric.
机译:混合模型生产线用于少量生产几种模型而不会携带大量库存。混合模型排序问题的生产顺序取决于生产设备的目标。为了享受这些生产线的有用应用,至关重要的是制定一个计划,以组装要确定的不同产品。基于问题的NP难点,本文分三个阶段介绍了帝国主义竞争算法(ICA),以解决需要优化生产率的实时性(JIT)排序问题。在小,中,大问题上,将ICA的算法与其他两种搜索启发式遗传算法(GA)和模拟退火算法(SA)进行了比较。为了与现有算法进行比较,对3组基准问题进行了广泛的计算研究。实验结果表明,在比较指标方面,我们的算法优于以前的算法。

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