摘要
为了有效减少物流网络中的碳排放量,对低碳选址-路径问题的优化车辆路径和选址方案进行研究。在区域化选址路径问题中,客户和仓库位于以不同速度限制为特征的嵌套区域,所构建的模型为最小化碳排放的物流成本。针对该问题,提出一种基于共享机制的自适应超启发式求解算法,通过共享底层算子的近期性能信息,自适应地选择优质合适的底层算子,并提出一种自适应解的接收机制来提高算法的收敛速度与精度。通过CPLEX求解简单算例验证了所提模型的正确性,通过仿真实验验证了所提算法的有效性和鲁棒性。分析了仓库的分布与成本、多车型车队和客户分布对碳排放和物流成本的影响,并为企业提供了统筹规划配送决策的管理指导与建议。
To reduce the carbon emission in logistics network effectively,the vehicle routing and location of Low-carbon location-routing problem(LCLRP)were researched.In Regional LCLRP(RLCLRP),the constructed model was the cost of minimum carbon emission owing to the nested zones of clients and depots characterized by different speed limits.Aiming at this problem,a Shared Mechanism based Self-Adaptive Hyper-Heuristic(SMSAHH)was developed,which could share the recent performance information of Low-Level Heuristics(LLH)for adaptively choosing the promising and appropriate LLH.A self-adaptive acceptance criterion was designed to accelerate the convergence and improve accuracy.The correctness of formulated model was verified though tackling the small-size instances with CPLEX method,and the effectiveness and robustness of presented SMSAHH was also verified by simulation results and comparisons.Moreover,extensive analyses were performed to empirically assess the effect of various problem parameters such as depot cost and location,customer distribution and heterogeneous vehicles.Several managerial insights were presented for logistics enterprise to plan and design the distribution network.
作者
冷龙龙
赵燕伟
张春苗
LENG Longlong;ZHAO Yanwei;ZHANG Chunmiao(Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology,Ministry of Education,Zhejiang University of Technology,Hangzhou 310023,China;Mechanical and Automotive Branches,Jiaxing Vocational and Technical College,Jiaxing 314036,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2020年第5期1407-1424,共18页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(61572438,61873240)
浙江省科技计划资助项目(LQ14F030005)。
关键词
区域化低碳选址-路径问题
多车型
共享机制
超启发式算法
regional low-carbon location-routing problem
heterogeneous vehicles
shared mechanism
hyper-heuristic algorithm
