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Solving Real-Life Locomotive-Scheduling Problems

Author

Listed:
  • Ravindra K. Ahuja

    (Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida 32611)

  • Jian Liu

    (Innovative Scheduling, GTEC, 2153 SE Hawthorne Road, Gainesville, Florida 32641)

  • James B. Orlin

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Dushyant Sharma

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

  • Larry A. Shughart

    (Innovative Scheduling, GTEC, 2153 SE Hawthorne Road, Gainesville, Florida 32641)

Abstract

In the locomotive-scheduling problem (or the locomotive-assignment problem), we must assign a consist (a set of locomotives) to each train in a preplanned train schedule so as to provide each train with sufficient locomotive power to pull the train from its origin to its destination. Locomotive-scheduling problems are among the most important problems in railroad scheduling. In this paper, we report the results of a study on the locomotive-scheduling problem as it is faced by CSX Transportation, a major U.S. railroad company. We consider the planning version of the locomotive-scheduling model (LSM) in which multiple types of locomotives exist, and we need to decide which set of locomotives should be assigned to each train. We present an integrated model that determines: the set of active and deadheaded locomotives for each train; the light-traveling locomotives from power sources to power sinks; and train-to-train connections (for which we specify which inbound trains and outbound trains can directly connect). An important feature of our model is that we explicitly consider consist bustings and consistency . A consist is said to be busted when a set of locomotives coming on an inbound train is broken into subsets to be reassigned to two or more outbound trains. A solution is consistent over a week if a train receives the same locomotive assignment each day that it runs. We will provide a mixed-integer programming (MIP) formulation of the locomotive-assignment problem. However, an MIP of this size cannot be solved to optimality or near optimality in acceptable running times using commercially available software. Using problem decomposition, integer programming, and very large-scale neighborhood search, we have developed a solution technique to solve this problem within 30 minutes of computation time on a Pentium III computer. Our solution obtained a potential savings of over 400 locomotives over the solution obtained by the in-house software developed by CSX.

Suggested Citation

  • Ravindra K. Ahuja & Jian Liu & James B. Orlin & Dushyant Sharma & Larry A. Shughart, 2005. "Solving Real-Life Locomotive-Scheduling Problems," Transportation Science, INFORMS, vol. 39(4), pages 503-517, November.
    • Handle: RePEc:inm:ortrsc:v:39:y:2005:i:4:p:503-517
    DOI: 10.1287/trsc.1050.0115
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    References listed on IDEAS

    as
    1. Jean-François Cordeau & Paolo Toth & Daniele Vigo, 1998. "A Survey of Optimization Models for Train Routing and Scheduling," Transportation Science, INFORMS, vol. 32(4), pages 380-404, November.
    2. Koorush Ziarati & François Soumis & Jacques Desrosiers & Marius M. Solomon, 1999. "A Branch-First, Cut-Second Approach for Locomotive Assignment," Management Science, INFORMS, vol. 45(8), pages 1156-1168, August.
    3. Ziarati, Koorush & Soumis, Francois & Desrosiers, Jacques & Gelinas, Sylvie & Saintonge, Andre, 1997. "Locomotive assignment with heterogeneous consists at CN North America," European Journal of Operational Research, Elsevier, vol. 97(2), pages 281-292, March.
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