基于学习型算法的农资配送优化问题研究

Research on Optimization Problem of Agricultural Supplies Distribution Based on Learning Algorithm

针对农资供应链订单配送路径优化问题,考虑新能源货车综合续航里程、车辆最大载荷能力和时间窗等约束,建立车辆路径规划问题的数学模型(DCVRPTW)综合优化车辆固定成本和运输成本,提出一种基于深度强化学习的群智能优化算法框架(DRL-SIA)。智能体就是决策者,以环境状态为输入选出动作池中最佳动作改变环境并获得环境奖励。DRL-SIA算法结合训练后的智能体与群智能算法以代替原算法进行决策选择,从而提升寻优速度与精度。实验表明,所提算法的最优解相较于其他算法在所有算例中最优,验证了该算法能有效降低农用物资供应链中的物流运输成本。

A mathematical model for vehicle path planning(DCVRPTW)was established to optimize the delivery path of agricultural inputs supply chain orders,taking into account constraints such as the comprehensive range,maximum load capacity,and time window of new energy trucks.The model comprehensively optimizes the fixed and transportation costs of vehicles,and proposes a swarm intelligence optimization algorithm framework based on deep reinforcement learning(DRL-SIA).An intelligent agent is a decision-maker who selects the best action from the action pool based on the environmental state as input to change the environment and obtain environmental rewards.The DRL-SIA algorithm combines trained agents with swarm intelligence algorithms to replace the original algorithm for decision selection,thereby improving optimization speed and accuracy.The experiment shows that the optimal solution of the proposed algorithm is superior to other algorithms in all cases,verifying that the algorithm can effectively reduce logistics transportation costs in the agricultural material supply chain.