With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used ...With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used to improve efficiency.This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand.New customers appear in the delivery process at any time and are periodically optimized according to time slices.Then,we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem.The classical ant colony algorithm uses spatial distance to select nodes,while TS-ACO considers the impact of both temporal and spatial distance on node selection.Meanwhile,we adopt Cordeau’s Multi-Depot Vehicle Routing Problem with Time Windows(MDVRPTW)dataset to evaluate the performance of our system.According to the experimental results,TS-ACO,which considers spatial and temporal distance,is more effective than the classical ACO,which only considers spatial distance.展开更多
Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience.However,they have not been well investigated because their charging and order-serving are affected or managed by the po...Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience.However,they have not been well investigated because their charging and order-serving are affected or managed by the power grid dispatching center and the ride-hailing platform.Effective pre-strategies can improve the prevention ability for high-impact and low-probability(HILP)events and provide the foundation for measures in the response and restoration stages.First,this paper proposes a resilience reserve to expand the existing research on power system resilience.Secondly,this paper puts forward an interactive method of deep reinforcement learning,which considers the interests of both the power grid dispatching center and the ride-hailing platform.It improves the resilience reserve by achieving the order dispatch,orderly charging management of ride-hailing electric vehicles,and the pricing strategy of charging stations.Finally,this paper uses a practical example covering about 107.32 km2 in the center of Chengdu to verify that the proposed method improves the resilience reserve of the power system without obviously damaging the interests of the ride-hailing platform.展开更多
针对需求可拆分的多品种库存路径问题(multi-product inventory routing problem with split deliveries,MIRPSD),提出一种基于最小化库存持有成本、运输成本和车辆使用总成本的车辆路径优化模型。同时考虑每个客户的交货计划及每种货...针对需求可拆分的多品种库存路径问题(multi-product inventory routing problem with split deliveries,MIRPSD),提出一种基于最小化库存持有成本、运输成本和车辆使用总成本的车辆路径优化模型。同时考虑每个客户的交货计划及每种货物的运输数量。设计混合遗传算法进行求解,引入扰动策略以提高搜索效率,并通过实验选取合适的参数。探讨了平均日需求量与车辆载重量的比值、单位库存持有成本对需求拆分策略及总配送成本的影响。多组算例试验表明,本文提出的模型和算法可有效解决该问题。当需求量服从正态分布且平均日需求量为车辆载重量的55%时,采用需求拆分策略的效果最佳。本研究拓展了库存路径问题的相关理论,既可为解决MIRPSD问题提供一种新思路,也可为物流企业的相关决策提供理论依据。展开更多
为系统分析和总结急救车资源布局与运营调度的研究现状与发展趋势,基于Web of Science数据库收录的1 502篇文献,从战略层、战术层、操作层3个层次梳理急救车资源布局与运营调度的研究脉络。研究结果表明,在战略层,急救车定位问题的研究...为系统分析和总结急救车资源布局与运营调度的研究现状与发展趋势,基于Web of Science数据库收录的1 502篇文献,从战略层、战术层、操作层3个层次梳理急救车资源布局与运营调度的研究脉络。研究结果表明,在战略层,急救车定位问题的研究重点在于持续改善覆盖定义、准确刻画系统内在的不确定性,主要研究方法包括随机规划、鲁棒优化等不确定性建模和求解方法。在战术层,急救车重定位问题按照触发重定位决策的方式分为多阶段重定位和动态重定位,由于重定位较定位更为复杂,研究重点在于应用启发式、强化学习等算法求解现实中的大规模问题。操作层关键的决策问题包括急救车指派、目的地选择和路径规划:急救车指派相关研究呈现从基于规则到基于模型,从独立优化到和重定位联合优化的发展历程;目的地选择涉及与医院工作负载的协同优化,路径规划则主要针对灾难响应等特殊场景。在未来的研究中,需要紧扣动态性和随机性2条研究主线,在准确刻画院前医疗急救系统不确定性来源的同时,充分利用更细粒度的数据辅助实时决策。在建模和求解的具体技术上,应考虑打通不同层次的多个决策问题开展联合优化,实现急救车定位与调度方案从局部最优到系统最优的迭代,并持续开发能处理现实大规模场景的高效求解算法以支撑联合优化的求解。展开更多
基金supported by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology.
文摘With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used to improve efficiency.This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand.New customers appear in the delivery process at any time and are periodically optimized according to time slices.Then,we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem.The classical ant colony algorithm uses spatial distance to select nodes,while TS-ACO considers the impact of both temporal and spatial distance on node selection.Meanwhile,we adopt Cordeau’s Multi-Depot Vehicle Routing Problem with Time Windows(MDVRPTW)dataset to evaluate the performance of our system.According to the experimental results,TS-ACO,which considers spatial and temporal distance,is more effective than the classical ACO,which only considers spatial distance.
文摘Ride-hailing electric vehicles are mobile resources with dispatch potential to improve resilience.However,they have not been well investigated because their charging and order-serving are affected or managed by the power grid dispatching center and the ride-hailing platform.Effective pre-strategies can improve the prevention ability for high-impact and low-probability(HILP)events and provide the foundation for measures in the response and restoration stages.First,this paper proposes a resilience reserve to expand the existing research on power system resilience.Secondly,this paper puts forward an interactive method of deep reinforcement learning,which considers the interests of both the power grid dispatching center and the ride-hailing platform.It improves the resilience reserve by achieving the order dispatch,orderly charging management of ride-hailing electric vehicles,and the pricing strategy of charging stations.Finally,this paper uses a practical example covering about 107.32 km2 in the center of Chengdu to verify that the proposed method improves the resilience reserve of the power system without obviously damaging the interests of the ride-hailing platform.
文摘针对需求可拆分的多品种库存路径问题(multi-product inventory routing problem with split deliveries,MIRPSD),提出一种基于最小化库存持有成本、运输成本和车辆使用总成本的车辆路径优化模型。同时考虑每个客户的交货计划及每种货物的运输数量。设计混合遗传算法进行求解,引入扰动策略以提高搜索效率,并通过实验选取合适的参数。探讨了平均日需求量与车辆载重量的比值、单位库存持有成本对需求拆分策略及总配送成本的影响。多组算例试验表明,本文提出的模型和算法可有效解决该问题。当需求量服从正态分布且平均日需求量为车辆载重量的55%时,采用需求拆分策略的效果最佳。本研究拓展了库存路径问题的相关理论,既可为解决MIRPSD问题提供一种新思路,也可为物流企业的相关决策提供理论依据。
文摘为系统分析和总结急救车资源布局与运营调度的研究现状与发展趋势,基于Web of Science数据库收录的1 502篇文献,从战略层、战术层、操作层3个层次梳理急救车资源布局与运营调度的研究脉络。研究结果表明,在战略层,急救车定位问题的研究重点在于持续改善覆盖定义、准确刻画系统内在的不确定性,主要研究方法包括随机规划、鲁棒优化等不确定性建模和求解方法。在战术层,急救车重定位问题按照触发重定位决策的方式分为多阶段重定位和动态重定位,由于重定位较定位更为复杂,研究重点在于应用启发式、强化学习等算法求解现实中的大规模问题。操作层关键的决策问题包括急救车指派、目的地选择和路径规划:急救车指派相关研究呈现从基于规则到基于模型,从独立优化到和重定位联合优化的发展历程;目的地选择涉及与医院工作负载的协同优化,路径规划则主要针对灾难响应等特殊场景。在未来的研究中,需要紧扣动态性和随机性2条研究主线,在准确刻画院前医疗急救系统不确定性来源的同时,充分利用更细粒度的数据辅助实时决策。在建模和求解的具体技术上,应考虑打通不同层次的多个决策问题开展联合优化,实现急救车定位与调度方案从局部最优到系统最优的迭代,并持续开发能处理现实大规模场景的高效求解算法以支撑联合优化的求解。
