Abstract: Aiming at the carbon emissions produced by distribution vehicles in supply chains,and based on the speed of vehicle changes as the important variable under time-varying network,a model was established by considering the combined objectives of carbon emissions with coordinating optimization of production time,inventory time and distribution routes.In the model,the constraints such as product category,time windows of customer demand and full load rate of vehicle and time for loading and unloading were taken into consideration.A hybrid particle swarm optimization algorithm combined with ant colony algorithm was proposed to optimize the model,and the encoding and decoding method of two real numbers were designed.Pheromone intensity method in the ant colony algorithm was used to update the directions of ants to keep the hints on their directions and memories during the update process.The validity of the model and algorithm was verified by the simulation optimization and comparative analysis by numerical examples.

Key words: carbon emission, production-inventory-distribution coordination optimization, hybrid particle swarm algorithm

摘要： 针对供应链配送环节车辆产生的碳排放量问题,以时变网络下车辆变化的速度为关键变量,建立了考虑碳排放量目标的,将生产时间、库存时间和配送路径协同优化的模型,同时考虑了产品种类、客户需求时间窗、车辆满载率及装卸时间等约束。提出了粒子群算法与蚁群算法相结合的混合粒子群算法对模型进行优化计算,并设计了两段实数的编码、解码方式。使用蚁群算法的信息素强度方式更新粒子群算法的粒子方向,使粒子在更新过程中保留方向性和记忆性。通过对数值算例的仿真优化与结果对比分析,验证了模型的合理性和算法的有效性。

关键词: 碳排放, 生产&mdash, 库存&mdash, 配送协同优化, 混合粒子群算法, 供应链