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Multicommodity network flow approach to the railroad crew-scheduling problem

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3 Author(s)
B. Vaidyanathan ; Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, Gainesville, 32611, USA ; K. C. Jha ; R. K. Ahuja

We present our solution to the crew-scheduling problem for 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.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:51 ,  Issue: 3.4 )