基于同时送取货电动车选址路径问题优化研究

针对电动汽车同时送取货问题，在考虑车辆容量与电量约束情况下，建立以总成本最低为目标的数学模型并通过模拟退火-蚁群算法对模型进行求解。首先，根据实际配送过程中出现的同时送取货约束和时间窗约束建立其选址路径数学模型；其次，通过加入回火操作和高斯变异设计了改进的混合模拟退火-蚁群优化算法对模型求解，并将提出的算法与蚁群算法、禁忌搜索算法以及自适应大领域搜索算法进行对比，证明算法优越性；最后，与送取分离的配送策略进行对比。基于不同规模算例检验算法性能，实验结果表明，提出的算法以及配送策略能得到较低的成本费用。

Optimization of location and routing problem for electric vehicles based on simultaneous delivery and pickup

To solve the electric vehicle problem with simultaneous delivery and pickup, a mathematical model was set up with the lowest total cost as target to solve the model by simulated annealing-ant colony algorithm considering the limitation of vehicle capacity and electric power. Firstly, a mathematical model was built for the location and routing according to the limitation of simultaneous delivery and pickup and the constraints of time window during the actual distribution. Secondly, drawing-back and Gaussian mutation design were added to improve the hybrid simulated annealing-ant colony optimization. The model was solved by this mixed algorithm, and the proposed algorithm was compared with ant colony optimization (ACO), Tabu search (TS) and adaptive large neighborhood search (ALNS) to confirm its superiority. Last, comparison was made with the distribution strategy of separate delivery and pickup. Based on the performance of example checking in different scales, the experimental results show that the algorithm and distribution strategy proposed achieve lower cost.