摘要
鉴于快时尚企业高频率配送的特性以及国家倡导的低碳环保理念,以服装产品为研究对象,以碳排放成本、车辆运输成本和顾客满意度为优化目标,建立多目标绿色车辆路径问题模型。提出一种改进蚁群算法对模型进行求解,在信息素更新过程中采用最大最小蚂蚁系统和云模型提高自身的全局搜索能力,同时对初始状态转移概率进行优化,并采取局部优化策略提高算法的适应性。仿真结果表明,提出的模型和算法能够充分调用企业资源,帮助快时尚企业科学规划车辆配送方案,降低运输成本和碳排放,实现经济效益与环境效益的最佳平衡。
In view of the characteristics of high frequency distribution of fast fashion enterprises and the low carbon and environmental protection concept advocated by the country,a multi-objective green vehicle routing problem model is established with apparel products as the research object and the carbon emission cost,vehicle transportation cost and customer satisfaction as the optimization objectives.An improved ant colony optimization is proposed to solve the model,where the max-min ant system and the cloud model are used in the pheromone update process to improve its own global search capability,the initial state transfer probability is optimized,and the local optimization strategy is adopted to improve the adaptability.The simulation results show that the proposed model and algorithm can fully call on enterprise resources to help fast fashion enterprises plan vehicle distribution schemes scientifically,reduce transportation cost and carbon emission,and achieve the best balance between economic benefit and environmental benefit.
作者
程亮
干宏程
李尚卿
陈雨蝶
郑荣
CHENG Liang;GAN Hongcheng;LI Shangqing;CHEN Yudie;ZHENG Rong(School of Management,University of Shanghai for Science and Technology,Shanghai 200082,China;Center for Supernetworks Research,University of Shanghai for Science and Technology,Shanghai 200082,China)
出处
《上海海事大学学报》
北大核心
2023年第1期30-37,共8页
Journal of Shanghai Maritime University
基金
国家自然科学基金(71871143)
上海市曙光学者人才计划(15SG41)。
关键词
绿色车辆路径问题
快时尚
碳排放
客户满意度
改进蚁群算法
green vehicle routing problem
fast fashion
carbon emission
customer satisfaction
improved ant colony optimization