In this paper, we present a new algorithm of the time-dependent shortest path problem with time windows. Give a directed graph , where V is a set of nodes, E is a set of edges with a non-negative transit-time function...In this paper, we present a new algorithm of the time-dependent shortest path problem with time windows. Give a directed graph , where V is a set of nodes, E is a set of edges with a non-negative transit-time function . For each node , a time window ?within which the node may be visited and ?, is non-negative of the service and leaving time of the node. A source node s, a destination node d and a departure time?t0, the time-dependent shortest path problem with time windows asks to find an s, d-path that leaves a source node s at a departure time t0;and minimizes the total arrival time at a destination node d. This formulation generalizes the classical shortest path problem in which ce are constants. Our algorithm of the time windows gave the generalization of the ALT algorithm and A* algorithm for the classical problem according to Goldberg and Harrelson [1], Dreyfus [2] and Hart et al. [3].展开更多
为了降低插电式混合动力汽车(Plug-in Hybrid Electric Vehicle,PHEV)在驾驶过程中的能耗,本文对插电式混合动力汽车绿色路径规划问题(Plug-in Hybrid Electric Vehicle Green Routing Problem,PHEVGRP)进行了研究。基于脉冲耦合神经网...为了降低插电式混合动力汽车(Plug-in Hybrid Electric Vehicle,PHEV)在驾驶过程中的能耗,本文对插电式混合动力汽车绿色路径规划问题(Plug-in Hybrid Electric Vehicle Green Routing Problem,PHEVGRP)进行了研究。基于脉冲耦合神经网络提出了用时间依赖中继神经网络求解时间依赖车辆路径规划问题。基于可实时获取的道路交通状态量建立PHEV能耗计算模型。采用硬参数共享多任务学习建立道路交通状态量的预测模型。结合两个模型,将时间依赖中继神经网络应用于PHEVGRP的求解。采用真实数据进行试验,结果表明所提出的方法能够求得PHEVGRP的基于预测模型的最优解且求解速度优于启发式算法。展开更多
文摘In this paper, we present a new algorithm of the time-dependent shortest path problem with time windows. Give a directed graph , where V is a set of nodes, E is a set of edges with a non-negative transit-time function . For each node , a time window ?within which the node may be visited and ?, is non-negative of the service and leaving time of the node. A source node s, a destination node d and a departure time?t0, the time-dependent shortest path problem with time windows asks to find an s, d-path that leaves a source node s at a departure time t0;and minimizes the total arrival time at a destination node d. This formulation generalizes the classical shortest path problem in which ce are constants. Our algorithm of the time windows gave the generalization of the ALT algorithm and A* algorithm for the classical problem according to Goldberg and Harrelson [1], Dreyfus [2] and Hart et al. [3].
文摘为了降低插电式混合动力汽车(Plug-in Hybrid Electric Vehicle,PHEV)在驾驶过程中的能耗,本文对插电式混合动力汽车绿色路径规划问题(Plug-in Hybrid Electric Vehicle Green Routing Problem,PHEVGRP)进行了研究。基于脉冲耦合神经网络提出了用时间依赖中继神经网络求解时间依赖车辆路径规划问题。基于可实时获取的道路交通状态量建立PHEV能耗计算模型。采用硬参数共享多任务学习建立道路交通状态量的预测模型。结合两个模型,将时间依赖中继神经网络应用于PHEVGRP的求解。采用真实数据进行试验,结果表明所提出的方法能够求得PHEVGRP的基于预测模型的最优解且求解速度优于启发式算法。
