Price-based demand response stimulates factories to adapt their power consumption patterns to time-sensitive electricity prices, so that a rise in energy cost is prevented without affecting production on the shop floor. This paper introduces a multiobjective optimization (MOO) model that jointly schedules job processing, machine idle modes, and human workers under real-time electricity pricing. Beyond existing models, labor is considered due to a common tradeoff between energy cost and labor cost. An adaptive multiobjective memetic algorithm (AMOMA) is proposed to fast converge toward the Pareto front without loss in diversity. It leverages feedback of cross-dominance and stagnation in a search and a prioritized grouping strategy. In this way, adaptive balance remains between exploration of the nondominated sorting genetic algorithm II and exploitation of two mutually complementary local search operators. A case study of an extrusion blow molding process in a plastic bottle manufacturer and benchmarks demonstrate the MOO effectiveness and efficiency of AMOMA. The impacts of production-prohibited periods and relative portion of energy and labor costs on MOO are further analyzed, respectively. The generalization of this method was further demonstrated in a multimachine experiment. The common tradeoff relations between the energy and labor costs as well as between the makespan and the sum of the two cost parts were quantitatively revealed.

中文翻译：

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使用自适应多目标模因算法进行工业需求响应的能源和劳动力感知生产调度


基于价格的需求响应刺激工厂根据时间敏感的电价调整其用电模式，从而在不影响车间生产的情况下防止能源成本上升。本文介绍了一种多目标优化（MOO）模型，该模型在实时电价下联合调度作业处理、机器空闲模式和人力。除了现有模型之外，由于能源成本和劳动力成本之间的共同权衡，劳动力也被考虑在内。提出了一种自适应多目标模因算法（AMOMA），可以在不损失多样性的情况下快速收敛到 Pareto 前沿。它利用搜索中的交叉主导和停滞的反馈以及优先分组策略。这样，在非支配排序遗传算法 II 的探索和两个相互补充的局部搜索算子的利用之间保持了自适应平衡。塑料瓶制造商的挤出吹塑工艺案例研究和基准证明了 AMOMA 的 MOO 有效性和效率。进一步分析了禁产期以及能源和劳动力成本相对比例对MOO的影响。该方法的通用性在多机实验中得到了进一步证明。定量揭示了能源成本和劳动力成本之间以及完工时间与两个成本部分之和之间的常见权衡关系。