峰值能耗下随机订单的模型预测控制

Model Predictive Control of Stochastic Orders with Peak Energy Consumption Constraints

针对带峰值能耗约束的随机订单并行机调度问题,即所有机器总功耗不得超过给定阈值,提出了基于模型预测控制的算法来最小化订单的期望生产周期。研究难点在于通过优化能源分配,在峰值能耗约束的前提下,提高设备的工作效率,并采用合理的滚动调度决策的方式处理由客户订单到达时间和需求量随机变化所引起的不确定性。研究对优化策略、生产周期、产品差异、机器速度等因素的影响,进行了全面的理论分析,并通过一系列数值实验,验证了所设计算法的有效性,挖掘了其中的有益管理启示来更好地指导实践。

Heterogeneous parallel machines with peak energy consumption constraints are associated with stochastic customer order scheduling problems,i.e.,the total energy consumption of all machines must not exceed a given threshold.Thus,in this study,we propose an algorithm based on model predictive control to minimize the expected cycle time of customer orders.The proposed algorithm effectively allocates energy,thus improving equipment efficiency within peak energy constraints.It also manages proper rolling scheduling of demands to deal with uncertainties in incoming customer orders.The model is then theoretically analyzed with respect to optimization strategy,order cycle time,product difference,and machine speed.Our experiment results verify the effectiveness of the proposed algorithm and excavate useful managerial insights to guide practical applications better.