基于改进遗传退火算法的电网智慧供应链最优仓库自动定位方法

Automatic Positioning Method of Optimal Warehouse in Smart Power Grid Supply Chain Based on Improved Genetic Annealing Algorithm

为了解决供应链下仓库物资分散、信息错乱、运行效率低下等问题,以提高仓库管理水平、保障仓库运输的供求时间为目标,提出一种基于改进电网智慧供应链最优仓库自动定位方法。建立运输时间最短的目标函数,通过改进遗传退火算法,选择染色体编码方式,设置自动定位约束条件,构建电网物资仓库之间的供需量模型,自动定位存在最优供求关系的最优仓库。实验结果证明,应用所提方法后,随惩罚成本而产生变动的各成本变化趋势整体较平缓,未及时满足需求比例与总成本呈正相关;所定位仓库符合目标影响函数;使用62次迭代即获得了最优仓库定位的目标函数。可以缓解电力物资调度矛盾,提高了电网物资供给点、周转仓、需求点的运输效率。

In order to solve the problems of material dispersion,information confusion and low operation efficiency in the warehouse under the supply chain,and with the goal of improving the warehouse management level and ensuring the supply and demand time of warehouse transportation,an optimal warehouse automatic positioning method is put forward to improve the smart power grid supply chain.This paper establishes the objective function with the shortest transportation time.This paper improves the genetic annealing algorithm,select the chromosomal coding mode,set the automatic positioning constraints,construct the supply and demand model between the power grid material warehouses,the optimal warehouse can be automatically positioned with the optimal supply and demand relationship.The experimental results prove that after applying the proposed method,the change trend of each cost changing with the penalty cost is relatively flat,and the proportion of demand not met in time is positively correlated with the total cost.The positioned warehouse meets the target impact function,and the target function of the optimal warehouse positioning is obtained with 62 iterations.It can alleviate the contradiction in power material scheduling,and improve the transportation efficiency of power grid material supply point,turnover warehouse and demand point.