社区食材联合配送问题的车辆路径优化

The Optimization of Vehicle Routing for the Community Joint Distribution Problem of Food Materials

在新型冠状病毒肺炎疫情防控时期,以社区为尺度的食材联合采购及配送成为基本民生需求.为提高城市物流系统效率,考虑食材保鲜及配送时间窗要求高的特点,量化分析联合配送的车辆路径优化方案,综合固定成本、运输成本、货损成本、时间惩罚成本以及联合配送补贴成本,建立以总成本最小为目标函数的混合整数优化模型.在车辆载重、车行里程、社区联合配送意愿等约束条件下,设计改进的遗传算法对模型进行求解.选取北京市27个社区的食材联合配送作为案例分析,计算结果表明此模型可节省12%的配送成本,随机仿真实验证明算法具有良好的可行性及鲁棒性.该方法可应用推广至医药配送、应急救援等物流系统优化问题.

In the prevention period of COVID-19,the joint procurement and distribution of food materials in communities have become the basic people livelihood needs.To improve the efficiency of the urban logistics,the optimal solution of the vehicle route problem for joint distribution is quantitatively analyzed considering the high requirements of the fresh food preservation and the delivery time window.The total cost function is composed with the fixed cost of vehicle,the transportation cost,the cargo loss cost,the time penalty cost and the joint distribution allowance cost.Then the optimization model is established to minimize the total cost based on the mixed integer programming.The vehicle capacity,travel distance,and the willingness of the joint distribution of the community are combined as the constraints.An improved genetic algorithm is designed to solve the model.The food ingredient delivery problem of 27 communities in Beijing is taken as a case study.The results show that the formulation can reduce 12%of the delivery cost.The simulation results of the random tests prove the feasibility and robust of the algorithm.The proposed method can be applied to the other optimization problems in the logistics systems such as the medical distribution and emergency rescue.