基于强化学习的电动车路径优化研究  

Research on electric vehicle routing problem based on reinforcement learning

作　　者：胡尚民 沈惠璋[1] Hu Shangmin;Shen Huizhang(Antai College of Economics&Management,Shanghai Jiaotong University,Shanghai 200030,China)

机构地区：[1]上海交通大学安泰经济与管理学院,上海200030

出　　处：《计算机应用研究》2020年第11期3232-3235,共4页Application Research of Computers

摘　　要：针对有路径总时长约束、载重量约束和电池容量约束的电动车路径优化问题(EVRP),考虑其途中可前往充电站充电的情境,构建以最小化路径总长度为目标的数学模型,提出一种基于强化学习的求解算法RL-EVRP。该算法用给定的分布生成训练数据,再通过策略梯度法训练模型,并保证在训练过程中路径合法即可。训练得到的模型可用于解决其他数据同分布的问题,无须重新训练。通过仿真实验及与其他算法的对比,表明RL-EVRP算法求解的路径总长度更短、车辆数更少,也表明强化学习可成功运用于较复杂的组合优化问题中。This paper took the electric vehicle routing problem(EVRP)with constraints of time,load and battery capacity as the research object,it considered its recharging need in transit,constructed a mathematic model aiming at minimizing the total route length,and proposed an algorithm RL-EVRP based on reinforcement learning.The algorithm generated instances sampled from a given distribution,and trained a model by applying a policy gradient method while keeping the route feasible.The trained model could solve other instances from similar distribution without the need to re-train.Simulation results show that the RL-EVRP can get shorter total route length and less number of vehicles and that the reinforcement learning can be applied to complicated combinatorial optimization problem successfully.

关 键 词：车辆路径问题 电动车 多约束 强化学习 策略梯度法 组合优化 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]