This paper uses the bibliometric analysis software CiteSpace to examine the current status and evolution of cold-chain logistics vehicle routing problems(CCVRP).7381 relevant articles published in the Web of Science c...This paper uses the bibliometric analysis software CiteSpace to examine the current status and evolution of cold-chain logistics vehicle routing problems(CCVRP).7381 relevant articles published in the Web of Science core collection from 2008 to 2024 were analyzed,an in-depth understanding of the publication trends and category distribution were gained.Subsequently,CiteSpace was used to create a scientific knowledge graph and perform visualization analysis.The analysis includes collaboration among authors,countries,and institutions;co-citation analysis of authors,journals,and references;citation burst detection of keywords;and co-citation cluster analysis of references.Based on a deep understanding of current research hotspots,an in-depth discussion of existing research was conducted from three perspectives:optimization objectives,distribution scenarios,and solution algorithms.The results show that CCVRP involves complex factors such as temperature requirements,time window constraints,and multi-objective optimization.These intricate constraints are causing research to become increasingly interdisciplinary and comprehensive.The evolution of hot topics shows that the research directions span multiple fields,from algorithm design to logistics management.This review helps researchers better understand the history,current status,and future development directions of CCVRP research,and provides valuable references and inspiration for academia and practice.展开更多
The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering...The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.展开更多
针对带时间窗的时间依赖型同时取送货车辆路径问题(Time Dependent Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows,TDVRPSPDTW),本文建立以车辆固定成本、驾驶员成本、燃油消耗及碳排放成本之和为优化...针对带时间窗的时间依赖型同时取送货车辆路径问题(Time Dependent Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows,TDVRPSPDTW),本文建立以车辆固定成本、驾驶员成本、燃油消耗及碳排放成本之和为优化目标的数学模型;并在传统蚁群算法的基础上,利用节约启发式构造初始解初始化信息素,改进状态转移规则,引入局部搜索策略,提出一种带自适应大邻域搜索的混合蚁群算法(Ant Colony Optimization with Adaptive Large Neighborhood Search,ACO-ALNS)进行求解;最后,分别选取基准问题算例和改编生成TDVRPSPDTW算例进行实验。实验结果表明:本文提出的ACO-ALNS算法可有效解决TDVRPSPDTW的基准问题;相较于模拟退火算法和带局部搜索的蚁群算法,本文算法求解得到的总配送成本最优值平均分别改善7.56%和2.90%;另外,相比于仅考虑碳排放或配送时间的模型,本文所构建的模型综合多种因素,总配送成本平均分别降低4.38%和3.18%,可有效提高物流企业的经济效益。展开更多
Just-in-time(JIT)part feeding is adopted by more and more automobile producers.Based on this part feeding policy,vehicles perform their assigned routes cyclically and provide stations with the exact quantity of parts ...Just-in-time(JIT)part feeding is adopted by more and more automobile producers.Based on this part feeding policy,vehicles perform their assigned routes cyclically and provide stations with the exact quantity of parts required until the next arrival of the vehicle.However,if there are uncertain travel times,a shortage of materials in stations will be caused.In this paper,the JIT part feeding optimization problem under travel time uncertainty is studied.The uncertain travel time is represented by the interval number according to the actual situation.To minimize the largest possible vehicle trip time,the optimization model is developed based on robust optimization.In the model,a route-dependent uncertain parameter is introduced.Through this model,the route of each vehicle and the parts load needed to be delivered by the vehicle can be calculated.A hybrid simulated annealing algorithm is designed to solve this model.The parts feeding planning for an engine assembly line is taken as an example.By the Monte Carlo simulation,the relationship between the line stoppage probability and the uncertain parameter is studied to obtain the final solution.The effectiveness of the method is demonstrated by this case study.展开更多
基金supported by the Natural Science Foundation of China(No.52062027)the'Double-First Class'Major Research Programs,the Educational Department of Gansu Province(GSSYLXM-04)+5 种基金Soft Science Special Project of Gansu Basic Research PIan under Grant No.22JR4ZA035Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(No.22ZD6GA010)Natural Science Foundation of Gansu Province(22JR5RA343)Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University,China,and Open Fund of National Engineering Research Center of Highway Maintenance Technology,Changsha University of Science&Technology(No.kfj220108)Key Research and Development Project of Gansu Province(No.22YF7GA142)Industry Support Plan Project from the Department of Education of Gansu Province(No.2024CYZC-28).
文摘This paper uses the bibliometric analysis software CiteSpace to examine the current status and evolution of cold-chain logistics vehicle routing problems(CCVRP).7381 relevant articles published in the Web of Science core collection from 2008 to 2024 were analyzed,an in-depth understanding of the publication trends and category distribution were gained.Subsequently,CiteSpace was used to create a scientific knowledge graph and perform visualization analysis.The analysis includes collaboration among authors,countries,and institutions;co-citation analysis of authors,journals,and references;citation burst detection of keywords;and co-citation cluster analysis of references.Based on a deep understanding of current research hotspots,an in-depth discussion of existing research was conducted from three perspectives:optimization objectives,distribution scenarios,and solution algorithms.The results show that CCVRP involves complex factors such as temperature requirements,time window constraints,and multi-objective optimization.These intricate constraints are causing research to become increasingly interdisciplinary and comprehensive.The evolution of hot topics shows that the research directions span multiple fields,from algorithm design to logistics management.This review helps researchers better understand the history,current status,and future development directions of CCVRP research,and provides valuable references and inspiration for academia and practice.
基金National natural science foundation (No:70371040)
文摘The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.
文摘针对带时间窗的时间依赖型同时取送货车辆路径问题(Time Dependent Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows,TDVRPSPDTW),本文建立以车辆固定成本、驾驶员成本、燃油消耗及碳排放成本之和为优化目标的数学模型;并在传统蚁群算法的基础上,利用节约启发式构造初始解初始化信息素,改进状态转移规则,引入局部搜索策略,提出一种带自适应大邻域搜索的混合蚁群算法(Ant Colony Optimization with Adaptive Large Neighborhood Search,ACO-ALNS)进行求解;最后,分别选取基准问题算例和改编生成TDVRPSPDTW算例进行实验。实验结果表明:本文提出的ACO-ALNS算法可有效解决TDVRPSPDTW的基准问题;相较于模拟退火算法和带局部搜索的蚁群算法,本文算法求解得到的总配送成本最优值平均分别改善7.56%和2.90%;另外,相比于仅考虑碳排放或配送时间的模型,本文所构建的模型综合多种因素,总配送成本平均分别降低4.38%和3.18%,可有效提高物流企业的经济效益。
基金This research received no external funding.This research is supported by the National Key Research and Development Program of China(Grant No.2017YFB1301600)。
文摘Just-in-time(JIT)part feeding is adopted by more and more automobile producers.Based on this part feeding policy,vehicles perform their assigned routes cyclically and provide stations with the exact quantity of parts required until the next arrival of the vehicle.However,if there are uncertain travel times,a shortage of materials in stations will be caused.In this paper,the JIT part feeding optimization problem under travel time uncertainty is studied.The uncertain travel time is represented by the interval number according to the actual situation.To minimize the largest possible vehicle trip time,the optimization model is developed based on robust optimization.In the model,a route-dependent uncertain parameter is introduced.Through this model,the route of each vehicle and the parts load needed to be delivered by the vehicle can be calculated.A hybrid simulated annealing algorithm is designed to solve this model.The parts feeding planning for an engine assembly line is taken as an example.By the Monte Carlo simulation,the relationship between the line stoppage probability and the uncertain parameter is studied to obtain the final solution.The effectiveness of the method is demonstrated by this case study.
