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A hybrid method for flowshops scheduling with condition-based maintenance constraint and machines breakdown

机译:基于条件维护约束和机器故障的流水车间调度混合方法

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One of the most important assumptions in production scheduling is that the machines are permanently available without any breakdown. In the real world of scheduling, machines can be made unavailable due to various reasons such as preventive maintenance and unpredicted breakdown. In this paper, we explore flowshop configuration under the assumption of condition-based maintenance to minimize expected makespan. Furthermore, we consider a condition-based maintenance (CBM) strategy which could be used in most industrial settings. The proposed algorithm is designed for non-resumable flowshop state where the processing of jobs after preventive maintenance is restarted from the beginning. We propose a hybrid algorithm based on genetic algorithm and simulated annealing. Additionally, we conduct an extensive parameter calibration with the utilization of Taguchi method and select the optimal levels of the algorithm's performance influential factors. The preliminary results indicate that the proposed method provides significantly better results compared with other high performing algorithms in the literature.
机译:生产计划中最重要的假设之一是机器永久可用而无任何故障。在实际的调度世界中,由于各种原因(例如,预防性维护和不可预测的故障),可能导致机器不可用。在本文中,我们在基于状态维护的假设下探索Flowshop配置,以最大程度地减少预期的制造时间。此外,我们考虑了基于条件的维护(CBM)策略,该策略可用于大多数工业环境。所提出的算法设计用于不可恢复的Flowshop状态,在该状态下,预防性维护后的作业处理将从一开始就重新开始。我们提出了一种基于遗传算法和模拟退火的混合算法。此外,我们利用Taguchi方法进行了广泛的参数校准,并选择了算法性能影响因素的最佳水平。初步结果表明,与文献中的其他高性能算法相比,该方法可提供更好的结果。

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