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An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem

机译:解决分布置换流水车间调度问题的有效分布算法估计

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摘要

In this paper, an effective estimation of distribution algorithm (EDA) is proposed to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, the earliest completion factory rule is employed for the permutation based encoding to generate feasible schedules and calculate the schedule objective value. Then, a probability model is built for describing the probability distribution of the solution space, and a mechanism is provided to update the probability model with superior individuals. By sampling the probability model, new individuals can be generated among the promising search region. Moreover, to enhance the local exploitation, some local search operators are designed based on the problem characteristics and utilized for the promising individuals. In addition, the influence of parameter setting of the EDA is investigated based on the Taguchi method of design of experiments, and a suitable parameter setting is suggested. Finally, numerical simulations based on 420 small-sized instances and 720 large-sized instances are carried out. The comparative results with some existing algorithms demonstrate the effectiveness of the proposed EDA in solving the DPFSP. In addition, the new best-known solutions for 17 out of 420 small instances and 589 out of 720 large instances are found.
机译:为了解决分布式置换流水车间调度问题(DPFSP),提出了一种有效的分布估计算法。首先,将最早的完成工厂规则用于基于置换的编码,以生成可行的计划并计算计划目标值。然后,建立了一个概率模型来描述解空间的概率分布,并提供了一种机制来更新具有较高个人的概率模型。通过对概率模型进行采样,可以在有前途的搜索区域中生成新的个体。而且,为了增强本地开发,根据问题的特征设计了一些本地搜索运算符,并将其用于有前途的个人。此外,基于田口实验设计方法研究了EDA参数设置的影响,并提出了合适的参数设置方法。最后,基于420个小型实例和720个大型实例进行了数值模拟。与一些现有算法的比较结果证明了所提出的EDA在解决DPFSP方面的有效性。此外,找到了针对420个小型实例中的17个和720个大型实例中的589个的新的最著名解决方案。

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