Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm
Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm
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DOI：
10.1016/j.future.2020.02.019
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发表时间：
2020-07-01
影响因子：
7.5
通讯作者：
Hu, Jinglu
中科院分区：
文献类型：
--
作者：
Luo, Jia;El Baz, Didier;Hu, Jinglu
将节能与生产效率相结合被认为是现代工业实践中的一个重要因素。许多处理制造过程中能源效率问题的研究仅仅集中在静态场景中建立数学模型。然而，在物理世界中，车间调度问题是动态的，意外事件可能会导致在开始时间后的原始时间表的变化。本文研究了在有新的紧急工件到达的车间中，最小化总拖期、总能量费用和对原计划的干扰。针对该问题的NP难性，提出了一种基于事件驱动策略的双异构岛并行遗传算法。为了在动态场景中达到快速响应，我们提出的方法是用两级并行化来实现的，其中较低的级别适合于GPU或多核CPU内的并发执行，而来自两侧的代码可以在较高的级别同时执行。最后，数值试验表明，所提出的方法可以有效地解决问题。同时，平均结果得到了改善，执行时间显著减少。(C)2020 Elsevier B.V.保留所有权利。
Integrating energy savings into production efficiency is considered as one essential factor in modern industrial practice. A lot of research dealing with energy efficiency problems in the manufacturing process focuses solely on building a mathematical model within a static scenario. However, in the physical world shop scheduling problems are dynamic where unexpected events may lead to changes in the original schedule after the start time. This paper makes an investigation into minimizing the total tardiness, the total energy cost and the disruption to the original schedule in the job shop with new urgent arrival jobs. Because of the NP hardness of this problem, a dual heterogeneous island parallel genetic algorithm with the event driven strategy is developed. To reach a quick response in the dynamic scenario, the method we propose is made with a two-level parallelization where the lower level is appropriate for concurrent execution within GPUs or a multi-core CPU while codes from the two sides can be executed simultaneously at the upper level. In the end, numerical tests are implemented and display that the proposed approach can solve the problem efficiently. Meanwhile, the average results have been improved with a significant execution time decrease. (C) 2020 Elsevier B.V. All rights reserved.