带时间窗的粮食物流车辆路径问题是一个典型的NP—难问题。针对粮食物流批量大、多点对多点等特点,建立了带时间窗的粮食物流车辆路径问题(Vehicle Routing Problem with Time Windows,VRPTM)的数学模型,进一步构造粒子群算法(Particle ...带时间窗的粮食物流车辆路径问题是一个典型的NP—难问题。针对粮食物流批量大、多点对多点等特点,建立了带时间窗的粮食物流车辆路径问题(Vehicle Routing Problem with Time Windows,VRPTM)的数学模型,进一步构造粒子群算法(Particle Swarm Optimization,PSO)用于问题求解,并将求解结果与遗传算法进行比较。结果表明,粒子群算法可以快速、有效地求得带时间窗的粮食物流车辆路径问题的优化解,降低配送成本。展开更多
The service capacity of urban public open spaces is an important indicator of the justness and soundness of the allocation of public space resources,such as parks and green spaces,in the process of urban development.I...The service capacity of urban public open spaces is an important indicator of the justness and soundness of the allocation of public space resources,such as parks and green spaces,in the process of urban development.Improving the service capacity of urban public open spaces is conductive to healthy,sustainable urban development.In this study,taking Shengyang City,China as a case study,a Gaussian-based two-step floating catchment area method(2 SFCA)is used to calculate an accessibility index and identify residential areas with a poor accessibility to urban public open spaces.Then,a particle swarm algorithm(PSA)is used to optimize the locations of new open space developments.Finally,the optimization results are verified using the analytic network process(ANP).The results show that the service capacity of public open spaces in the center of Shenyang City(covering six districts)is relatively low and exhibits an uneven spatial distribution.In the service scope of the existing urban public open spaces,the accessibility for 48.6%of the residential estates is moderately poor or poor.The layout is optimized when the number of optimization points is set to 8.These points are mainly located in old town areas such as the Tiexi,Huanggu,and Dadong districts.The optimization increases the green space area accessible by motor vehicles(60 min),bicycles(60 min),and walking(30 min)by 4.67%,5.38%,and 8.03%of the study area,respectively.Finally,green space planning recommendations are offered from two perspectives:spatial layout and transport system optimization.展开更多
文摘带时间窗的粮食物流车辆路径问题是一个典型的NP—难问题。针对粮食物流批量大、多点对多点等特点,建立了带时间窗的粮食物流车辆路径问题(Vehicle Routing Problem with Time Windows,VRPTM)的数学模型,进一步构造粒子群算法(Particle Swarm Optimization,PSO)用于问题求解,并将求解结果与遗传算法进行比较。结果表明,粒子群算法可以快速、有效地求得带时间窗的粮食物流车辆路径问题的优化解,降低配送成本。
基金Under the auspices of the China National R&D Program(No.2017YFC0505704)National Natural Science Foundation of China(No.32101325)+1 种基金Fundamental Research Funds for the Central Universities of China(No.N2011005)Student Innovation Training Program of Northeastern University of China(No.201299)。
文摘The service capacity of urban public open spaces is an important indicator of the justness and soundness of the allocation of public space resources,such as parks and green spaces,in the process of urban development.Improving the service capacity of urban public open spaces is conductive to healthy,sustainable urban development.In this study,taking Shengyang City,China as a case study,a Gaussian-based two-step floating catchment area method(2 SFCA)is used to calculate an accessibility index and identify residential areas with a poor accessibility to urban public open spaces.Then,a particle swarm algorithm(PSA)is used to optimize the locations of new open space developments.Finally,the optimization results are verified using the analytic network process(ANP).The results show that the service capacity of public open spaces in the center of Shenyang City(covering six districts)is relatively low and exhibits an uneven spatial distribution.In the service scope of the existing urban public open spaces,the accessibility for 48.6%of the residential estates is moderately poor or poor.The layout is optimized when the number of optimization points is set to 8.These points are mainly located in old town areas such as the Tiexi,Huanggu,and Dadong districts.The optimization increases the green space area accessible by motor vehicles(60 min),bicycles(60 min),and walking(30 min)by 4.67%,5.38%,and 8.03%of the study area,respectively.Finally,green space planning recommendations are offered from two perspectives:spatial layout and transport system optimization.
