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An energy-aware scheduling algorithm under maximum power consumption constraints
Journal of Manufacturing Systems ( IF 12.2 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.jmsy.2020.09.004
Ywh-Leh Chou , Ju-Min Yang , Cheng-Hung Wu

Abstract This research investigates the production scheduling problems under maximum power consumption constraints. Probabilistic models are developed to model dispatching-dependent and stochastic machine energy consumption. A multi-objective scheduling algorithm called the energy-aware scheduling optimization method is proposed in this study to enhance both production and energy efficiency. The explicit consideration of the probabilistic energy consumption constraint and the following factors makes this work distinct from other existing studies in the literature: 1) dispatching-dependent energy consumption of machines, 2) stochastic energy consumption of machines, 3) parallel machines with different production rates and energy consumption pattern, and 4) maximum power consumption constraints. The proposed three-stage algorithm can quickly generate near-optimal solutions and outperforms other algorithms in terms of energy efficiency, makespan, and computation time. While minimizing the total energy consumption in the first and second stages, the proposed algorithm generates a detailed production schedule under the probabilistic constraint of peak energy consumption in the third stage. Numerical results show the superiority of the scheduling solution with regard to quality and computational time in real problems instances from manufacturing industry. While the scheduling solution is optimal in total energy consumption, the makespan is within 0.6 % of the optimal on average.

中文翻译:

最大功耗约束下的能量感知调度算法

摘要 本研究研究了最大功耗约束下的生产调度问题。开发概率模型来对依赖于调度的随机机器能耗进行建模。本研究提出了一种称为能量感知调度优化方法的多目标调度算法,以提高生产和能源效率。对概率能耗约束和以下因素的明确考虑使这项工作与文献中的其他现有研究不同:1)与调度相关的机器能耗,2)机器的随机能耗,3)具有不同产量的并行机器费率和能耗模式,以及 4) 最大功耗限制。所提出的三阶段算法可以快速生成接近最优的解决方案,并在能源效率、制造时间和计算时间方面优于其他算法。在最小化第一阶段和第二阶段的总能耗的同时,所提出的算法在第三阶段峰值能耗的概率约束下生成详细的生产计划。数值结果显示了调度解决方案在制造行业的实际问题实例中在质量和计算时间方面的优越性。虽然调度解决方案在总能耗方面是最优的,但完工时间平均在最优值的 0.6% 以内。在最小化第一阶段和第二阶段的总能耗的同时,所提出的算法在第三阶段峰值能耗的概率约束下生成详细的生产计划。数值结果显示了调度解决方案在制造行业的实际问题实例中在质量和计算时间方面的优越性。虽然调度解决方案在总能耗方面是最优的,但完工时间平均在最优值的 0.6% 以内。在最小化第一阶段和第二阶段的总能耗的同时,所提出的算法在第三阶段峰值能耗的概率约束下生成详细的生产计划。数值结果显示了调度解决方案在制造行业的实际问题实例中在质量和计算时间方面的优越性。虽然调度解决方案在总能耗方面是最优的,但完工时间平均在最优值的 0.6% 以内。数值结果显示了调度解决方案在制造行业的实际问题实例中在质量和计算时间方面的优越性。虽然调度解决方案在总能耗方面是最优的,但完工时间平均在最优值的 0.6% 以内。数值结果显示了调度解决方案在制造行业的实际问题实例中在质量和计算时间方面的优越性。虽然调度解决方案在总能耗方面是最优的,但完工时间平均在最优值的 0.6% 以内。
更新日期:2020-10-01
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