改进麻雀搜索算法求解带削峰需求响应的混合流水车间调度问题

Improved Sparrow Search Algorithm for Hybrid Flow-shop Scheduling Problemwith Peak Clipping Demand Response

电力需求响应是用电高峰时期维护电网供需平衡的重要手段,而削峰是智能电网实现电力需求响应的主要方式。为了使采用混合流水车间生产的企业更好地参与削峰需求响应,优化生产调度,在混合流水车间调度问题中引入了削峰需求响应,建立了新的问题模型,并提出了一种改进麻雀搜索算法用于模型求解。针对标准麻雀搜索算法易陷入局部最优的问题,所提算法通过加入K-均值聚类替换策略改进了标准麻雀搜索算法的局部搜索能力。实验结果表明,所提模型和算法能够提供较好的削峰生产调度方案,满足企业实施削峰需求响应调度的需要。

Power demand response is an important means to maintain the balance of power supply and demand in the peak period,and peak clipping is the main way of smart grid to achieve power demand response.In order to enable enterprises using hybrid flow-shop production to better participate in peak clipping demand response(PCDR)and optimize production scheduling,this paper introduces PCDR in the hybrid fow-shop scheduling problem,establishes a new problem model,and proposes an improved Sparrow Search Algorithm(ISSA)for model solving.Aiming at the problem that the standard SSA is prone to local optimization,the ISSA improves the local search ability of SSA by adding K-Means clustering replacement strategy.The experimental results show that the proposed model and algorithm can provide a better peak clipping production scheduling scheme,meeting the needs of enterprises to implement PCDR scheduling.