基于电商承诺送达机制的多目标低碳MDVRP优化研究

针对存在多配送站的电商物流配送问题，首先，考虑实际装载量对物流配送过程中车辆燃料消耗量的影响，建立燃料消耗量模型，并结合电商平台的承诺送达机制，构建配送延迟时间函数。随后，提出了以最小化物流成本和延迟收货时间的多目标多配送站车辆路径规划问题，建立该问题的混合整数规划模型。再次，采用基于分解的多目标遗传求解算法对问题进行求解。该算法采用矩阵编码的方式，设计了基于贪婪搜索策略的启发式初始化方法，考虑到贪婪搜索策略容易陷入局部最优的劣势，在算法迭代过程中，允许部分不可行解存在以扩大解空间的搜索范围，并进一步设计了遗传算法的交叉和变异算子。最后，以具体物流配送案例进行数值实验，实验结果表明所设计的算法对求解本文模型是有效的。

Multi-objective Low Carbon MDVRP Optimization Based on E-commerce Commitment Delivery Mechanism

Under the background of rapid development of e-commerce, the problem of logistics terminal distribution optimization has become the key to its operational efficiency. For the multi-depot e-commerce logistics distribution problem, firstly, considering the impact of actual loading on vehicle fuel consumption in the process of logistics distribution, a fuel consumption model is established, and combining the commitment delivery mechanism of e-commerce platform, delayed delivery time function is built. Then, the multi-objective multi-depot vehicle routing problem with minimizing logistics distribution cost and minimizing delayed delivery time is proposed, and a mixed integer programming model of the problem is established. Thirdly, considering the NP-Hard characteristics of the problem and the difficulty of solving multi-objective, the problem is solved by the multi-objective genetic algorithm based on decomposition. The algorithm uses matrix coding to design a heuristic initialization method based on greedy search strategy. At the same time, considering that the greedy search strategy is easy to fall into the local optimum, in the iterative process of the algorithm, some unfeasible solutions are allowed to exist to expand the search range of the solution space, and the crossover and mutation operators of the genetic algorithm are further designed. Finally, the numerical experiments are carried out with specific logistics distribution cases. The experimental results verify the effectiveness of the designed algorithm to solve the model, and also show the inverse relationship between logistics distribution cost and delayed delivery time. There is a trade-off between consumer satisfaction and logistics distribution costs. In order to achieve higher consumer satisfaction, it often means increasing the cost of logistics distribution. At the same time, the solution algorithm of this paper provides a solution to the problem of e-commerce terminal delivery.