火车
计算机科学
模糊逻辑
布线(电子设计自动化)
灵敏度(控制系统)
拖车
车辆路径问题
整数规划
模糊集
运筹学
数学优化
工程类
人工智能
计算机网络
数学
算法
地理
地图学
电子工程
作者
Yue Lu,Maoxiang Lang,Yan Sun,Shiqi Li
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:8: 27532-27548
被引量:33
标识
DOI:10.1109/access.2020.2971027
摘要
This paper investigates a fuzzy intercontinental multimodal routing problem with uncertainties in time and train capacity. The transport network has characteristics of a long distance across continents and a road-rail multimodal routing, with four kinds of nodes and three kinds of arcs. Based on a variant of the vehicle routing problem, the tractor and semi-trailer routing problem is considered for the freight collection part for intercontinental trains. Additionally, the rail routing problem includes domestic direct trains, and intercontinental trains with hard time windows of departure. To make this problem more applicable to real-world circumstances, we describe two types of uncertainty parameters, including time and train uncertainties. Based on the transport conditions of stations, the time uncertainty is considered. Due to the multimodal transport stations' operating capacity and container collection methods, train capacity uncertainty is taken into account. Furthermore, we use solution methods based on the defuzzification approach to solve a fuzzy mixed integer linear programming model and generate a series of instances to verify the fuzzy model. We perform sensitivity analyses of the parameters. The results show that different quantities of intercontinental trains can change the performance by 10% to 20%. The objective function may decrease by more than 20% when the service level increases by 0.1. A sensitivity analysis of the time satisfaction confidence level also shows the trends of fuzzy time and the objective function. These analyses can give reference results about timeliness, transport resource allocation and other suggestions for the intercontinental multimodal transport routing problem.
科研通智能强力驱动
Strongly Powered by AbleSci AI