卡车
运输工程
启发式
列生成
工作量
运筹学
过程(计算)
布线(电子设计自动化)
大都市区
计算机科学
流量网络
工程类
汽车工程
计算机网络
地理
考古
人工智能
数学优化
操作系统
数学
作者
Xiaofang Sun,Manish Garg,Zahir Balaporia,Kendall Bailey,Ted Gifford
出处
期刊:Interfaces
[Institute for Operations Research and the Management Sciences]
日期:2014-12-01
卷期号:44 (6): 579-590
被引量:6
标识
DOI:10.1287/inte.2014.0746
摘要
In rail-based intermodal freight operations, full containers are moved by truck from shipper locations to rail ramps, transported via train to destination ramps, and then moved again by truck to consignee locations. We commonly refer to the roadway portions of this activity as dray operations. In a large metropolitan hub area, such as Chicago or Los Angeles, this drayage activity may involve several hundred drivers and up to 500 daily container moves to or from several distinct rail ramps. Both cost and environmental considerations drive the need to maximize driver productivity and minimize the time and miles not directly associated with moving loaded containers to or from rail ramps. In this paper, we describe a solution to this problem using a set-partitioning formulation and column-generation heuristic and report on a large-scale implementation. We focus on real-world implementation details that include (1) fast solve times to support near-real-time re-solving in the face of constantly changing data, (2) adjustments to account for traffic congestion and other operational considerations, and (3) integration with a commercial transportation management system to provide real-time data to the optimizer and to send solution recommendations to a driver-assignment process.
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