摘要
针对电能计量器具物流配送的需求量预测、配送中心选址以及客户分配等多个问题,提出一种基于改进免疫遗传算法和Holt-Winters模型的电能计量器具配送网络优化策略。首先,采用Holt-Winters模型来预测未来一年的月度需求量,黄金分割法可用于搜索最优平滑系数,提高预测的准确性;然后,采用高斯投影法将客户的经纬度转换为平面直角坐标,建立配送运输成本最小的目标函数,并基于种群的相似性和矢量距,对免疫遗传算法的抗体浓度进行改进;最后,以某省网计量中心—配送中心—客户二级电能表配送问题为例,采用改进免疫遗传算法求解目标函数,筛选最优的配送中心位置和客户分配方案。结果表明,改进免疫遗传算法具有更高的收敛效率和避免局部收敛的能力,本文所提电能计量器具配送网络优化策略对减小配送成本具有一定的参考价值。
The logistics and distribution problems of electric energy measuring instruments include demand prediction,distribution center location,customer distribution,etc.An optimization strategy for electric energy measuring instru-ment distribution network is proposed based on an improved immune genetic algorithm and Holt-Winters model in this paper.Firstly,the Holt-winters model is applied to predict the monthly demand in the next year.The golden section method can be utilized to search for the optimal smoothing coefficient to improve prediction accuracy.Then,the Gauss projection method is employed to transform the longitude and latitude of customers into Cartesian coordinates,and the objective function of minimum transportation costs is constructed.The antibody concentration of the immune genetic algorithm is improved based on the similarity and vector distance between populations.Finally,the power meter dis-tribution problem of metering center-distribution center-customer in a provincial power grid is included as an example.The improved immune genetic algorithm solves the objective function and selects the optimal distribution center loca-tion and customer distribution scheme.It is shown that the improved immune genetic algorithm has higher conver-gence efficiency and the ability to avoid local convergence.The proposed optimization strategy of the electric energy measuring instrument distribution network has a certain reference value to reduce the distribution costs.
作者
王杰
孙林
郑直
张嘉浩
黄雷
WANG Jie;SUN Lin;ZHENG Zhi;ZHANG Jiahao;HUANG Lei(Nari Technology Development Co., Ltd., Nanjing 210032, China;Jinxiandai Information Industrial Corporation, Jinan 250101, China;Collage of Electrical and Information Engineering, Hunan University, Changsha 410082, China)
出处
《电力科学与技术学报》
CAS
北大核心
2022年第2期156-163,共8页
Journal of Electric Power Science And Technology
基金
国家自然科学基金(52077068)
国电南瑞科研基金(4501192150)。
