考虑时变速度的多车场绿色车辆路径模型及优化算法

针对多车场绿色车辆路径问题,根据顾客的坐标位置,采用K-means聚类方法将顾客分配给不同的车场;考虑时变速度和实时载重对车辆油耗和碳排放的影响,确定车辆油耗和碳排放的度量函数;在此基础上,以车辆油耗成本、碳排放成本、车辆使用成本、驾驶员工资以及时间窗惩罚成本之和最小化作为优化目标,构建多车场绿色车辆路径模型,并根据模型特点设计一种改进的蚁群算法进行求解.算例仿真结果表明,所构建的模型和提出的算法能合理调配不同车场的车辆,科学规划车辆路径,有效规避交通拥堵时间段,降低物流配送总成本,减少车辆油耗和碳排放,促进物流配送企业的节能减排.

Multi-depot green vehicle routing model and its optimization algorithm with time-varying speed

Aiming at the multi-depot green vehicle routing problem, considering the coordinate position of customers, the K-means clustering method is used to assign customers to different depots. In view of the influence of time-varying speed and real-time load on vehicle fuel consumption and carbon emission, the measurement function of vehicle fuel consumption and carbon emission is determined. On this basis, a multi-depot green vehicle routing model is established with the optimization objective of minimizing the sum of fuel consumption cost, carbon emission cost, use cost of vehicles, drivers'' wages and time window penalty cost. Moreover, an improved ant colony algorithm is designed according to the characteristics of the model. The experimental results show that the model and algorithm can reasonably allocate vehicles in different depots, scientifically plan vehicle routes, effectively avoid periods of traffic congestion, lower the total distribution costs, reduce vehicle fuel consumption and carbon emissions, and promote energy conservation and emission reduction of logistics distribution enterprises.