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A BRKGA-DE algorithm for parallel-batching scheduling with deterioration and learning effects on parallel machines under preventive maintenance consideration
Annals of Mathematics and Artificial Intelligence ( IF 1.2 ) Pub Date : 2018-09-27 , DOI: 10.1007/s10472-018-9602-1
Min Kong , Xinbao Liu , Jun Pei , Hao Cheng , Panos M. Pardalos

This paper introduces a parallel-batching scheduling problem with deterioration and learning effects on parallel machines, where the actual processing time of a job is subject to the phenomena of deterioration and learning. All jobs are first divided into different parallel batches, and the processing time of the batches is equal to the largest processing time of their belonged jobs. Then, the generated batches are assigned to parallel machines to be processed. Motivated by the characteristics of machine maintenance activities in a semiconductor manufacturing process, we take the machine preventive maintenance into account, i.e., the machine should be maintained after a fixed number of batches have been completed. In order to solve the problem, we analyze several structural properties with respect to the batch formation and sequencing. Based on these properties, a hybrid BRKGA-DE algorithm combining biased random-key genetic algorithm (BRKGA) and Differential Evolution (DE) is proposed to solve the parallel-batching scheduling problem. A series of computational experiments is conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.

中文翻译:

考虑预防性维护的并行机退化和学习影响并行批处理调度的BRKGA-DE算法

本文介绍了并行机上具有退化和学习效应的并行批处理调度问题,其中作业的实际处理时间受退化和学习现象的影响。所有作业首先被分成不同的并行批次,批次的处理时间等于其所属作业的最大处理时间。然后,将生成的批次分配给并行机进行处理。受半导体制造过程中机器维护活动的特点的启发,我们将机器预防性维护考虑在内,即机器应在完成固定数量的批次后进行维护。为了解决这个问题,我们分析了与批次形成和测序相关的几个结构特性。基于这些特性,提出了一种结合有偏随机密钥遗传算法(BRKGA)和差分进化(DE)的混合BRKGA-DE算法来解决并行批处理调度问题。进行了一系列的计算实验来证明所提出算法的有效性和效率。
更新日期:2018-09-27
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