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A mathematical modeling approach to improving locomotive utilization at a freight railroad


  • Kuo, Ching-Chung
  • Nicholls, Gillian M.


Moving freight by rail remains one of the major transportation modes in today's business world. Although railcars compare unfavorably with trucks and airplanes with respect to mobility, flexibility, and speed, the shipping costs are lower and the energy-efficiency is higher. In order to become more competitive in the logistics industry, railroads have taken a number of new initiatives to improve their operations in recent years. One of such efforts made by Consolidated Rail Corporation (Conrail) is described in this paper. The main focus of the present study is on helping Conrail increase the utilization of its locomotive fleet by developing a mixed integer linear program (MILP) to determine the least-cost plan of allocating locomotives to yards and moving light engines between yards. The MILP is tested on a set of real data gathered at Conrail and it is proven to be superior to the existing method. A simple sensitivity analysis is also performed to gain insight into the trade-off between investment in additional locomotives and cost of light engine moves.

Suggested Citation

  • Kuo, Ching-Chung & Nicholls, Gillian M., 2007. "A mathematical modeling approach to improving locomotive utilization at a freight railroad," Omega, Elsevier, vol. 35(5), pages 472-485, October.
  • Handle: RePEc:eee:jomega:v:35:y:2007:i:5:p:472-485

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    References listed on IDEAS

    1. Brucker, Peter & Drexl, Andreas & Mohring, Rolf & Neumann, Klaus & Pesch, Erwin, 1999. "Resource-constrained project scheduling: Notation, classification, models, and methods," European Journal of Operational Research, Elsevier, vol. 112(1), pages 3-41, January.
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    Cited by:

    1. Arsham, Hossein & Adlakha, Veena & Lev, Benjamin, 2009. "A simplified algebraic method for system of linear inequalities with LP applications," Omega, Elsevier, vol. 37(4), pages 876-882, August.
    2. Jaehn, Florian & Rieder, Johannes & Wiehl, Andreas, 2015. "Single-stage shunting minimizing weighted departure times," Omega, Elsevier, vol. 52(C), pages 133-141.
    3. Yang, Lixing & Li, Keping & Gao, Ziyou & Li, Xiang, 2012. "Optimizing trains movement on a railway network," Omega, Elsevier, vol. 40(5), pages 619-633.
    4. Hong, Sung-Pil & Kim, Kyung Min & Lee, Kyungsik & Hwan Park, Bum, 2009. "A pragmatic algorithm for the train-set routing: The case of Korea high-speed railway," Omega, Elsevier, vol. 37(3), pages 637-645, June.
    5. Kowalski, Krzysztof & Lev, Benjamin, 2008. "On step fixed-charge transportation problem," Omega, Elsevier, vol. 36(5), pages 913-917, October.
    6. Kallrath, J. & Klosterhalfen, S.T. & Walter, M. & Fischer, G. & Blackburn, R., 2017. "Payload-based fleet optimization for rail cars in the chemical industry," European Journal of Operational Research, Elsevier, vol. 259(1), pages 113-129.
    7. Dalalah, Doraid & Lev, Benjamin, 2009. "Duality of the improved algebraic method (DIAM)," Omega, Elsevier, vol. 37(5), pages 1027-1035, October.
    8. Chung, Ji-Won & Oh, Seog-Moon & Choi, In-Chan, 2009. "A hybrid genetic algorithm for train sequencing in the Korean railway," Omega, Elsevier, vol. 37(3), pages 555-565, June.


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