Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A column-generation technique for the long-haul crew-assignment problem
Optimization in industry 2
Algorithms for railway crew management
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Crew scheduling of light rail transit in Hong Kong: from modeling to implementation
Computers and Operations Research
Computer Scheduling of Public Transportation: Urban Passenger Vehicle and Crew Scheduling
Computer Scheduling of Public Transportation: Urban Passenger Vehicle and Crew Scheduling
A Survey of Optimization Models for Train Routing and Scheduling
Transportation Science
Simultaneous disruption recovery of a train timetable and crew roster in real time
Computers and Operations Research
Incremental Network Optimization: Theory and Algorithms
Operations Research
Strategic crew planning tool in railroad: a discrete event simulation
Proceedings of the Winter Simulation Conference
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We present our solution to the crew-scheduling problem Jor North American railroads. (Crew scheduling in North America is very different from scheduling in Europe, where it has been well studied.) The crew-scheduling problem is to assign operators to scheduled trains over a time horizon at minimal cost while honoring operational and contractual requirements. Currently, decisions related to crew are made manually. We present our work developing a network-flow-based crew-optimization model that can be applied at the tactical, planning, and strategic levels of crew scheduling. Our network flow model maps the assignment of crews to trains as the flow of crews on an underlying network, where different crew types are modeled as different commodities in this network. We formulate the problem as an integer programming problem on this network, which allows it to be solved to optimality. We also develop several highly efficient algorithms using problem decomposition and relaxation techniques, in which we use the special structure of the underlying network model to obtain significant increases in speed. We present very promising computational results of our algorithms on the data provided by a major North American railroad. Our network flow model is likely to form a backbone for a decision-support system for crew scheduling.