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Optimal asset management strategies for mixed transit fleet

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  • Ngo, Huan Hoang
  • Shah, Rohan
  • Mishra, Sabyasachee

Abstract

Transit agencies require equitable and optimal allocation of funds among transit agencies for not just regular operations and maintenance, but also for asset management including purchase of new buses and rehabilitation of aging fleet. The paper proposes a hierarchical structure of resource allocation where federal funding is routed through the state, and ultimately to local transit agencies. The framework encompasses multiple dimensions such as selection of different improvement program options (rehabilitation, remanufacturing, and replacement) of a mixed transit fleet spread over a temporally continuous planning period. It leverages optimization models for capital allocation among transit agencies in the state. Four sub-models are developed—two maximizing passenger miles traveled, and the other two maximizing the total fleetwide remaining life, all under agency-specific budget, capacity and policy constraints, and planning objectives. They are applied on real-world data from set of transit agencies spread across the state of Tennessee, containing a heterogenous fleet of 254 total buses at various levels of aging. Results indicate that by application of the framework, an average 40 percent additional mileage is generated through the planning period with the same levels of fleet size, with nearly 30 percent of the fleet receiving some form of improvement treatment per year.

Suggested Citation

  • Ngo, Huan Hoang & Shah, Rohan & Mishra, Sabyasachee, 2018. "Optimal asset management strategies for mixed transit fleet," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 103-116.
  • Handle: RePEc:eee:transa:v:117:y:2018:i:c:p:103-116
    DOI: 10.1016/j.tra.2018.08.013
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    1. Haggag, A. A., 1981. "A variant of the generalized reduced gradient algorithm for non-linear programming and its applications," European Journal of Operational Research, Elsevier, vol. 7(2), pages 161-168, June.
    2. Sheu, Jiuh-Biing, 2006. "A novel dynamic resource allocation model for demand-responsive city logistics distribution operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 42(6), pages 445-472, November.
    3. Simms, B. W. & Lamarre, B. G. & Jardine, A. K. S. & Boudreau, A., 1984. "Optimal buy, operate and sell policies for fleets of vehicles," European Journal of Operational Research, Elsevier, vol. 15(2), pages 183-195, February.
    4. Mishra, Sabyasachee & Sharma, Sushant & Khasnabis, Snehamay & Mathew, Tom V., 2013. "Preserving an aging transit fleet: An optimal resource allocation perspective based on service life and constrained budget," Transportation Research Part A: Policy and Practice, Elsevier, vol. 47(C), pages 111-123.
    5. Pillai, Rekha S. & Rathi*, Ajay K. & L. Cohen, Stephen, 1998. "A restricted branch-and-bound approach for generating maximum bandwidth signal timing plans for traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 32(8), pages 517-529, November.
    6. Mathew, Tom V. & Khasnabis, Snehamay & Mishra, Sabyasachee, 2010. "Optimal resource allocation among transit agencies for fleet management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(6), pages 418-432, July.
    7. Ross, Anthony D., 2000. "Performance-based strategic resource allocation in supply networks," International Journal of Production Economics, Elsevier, vol. 63(3), pages 255-266, January.
    8. Diana, Marco & Dessouky, Maged M. & Xia, Nan, 2006. "A model for the fleet sizing of demand responsive transportation services with time windows," Transportation Research Part B: Methodological, Elsevier, vol. 40(8), pages 651-666, September.
    9. Uyeno, Dean H. & Willoughby, Keith A., 1995. "Transit centre location-allocation decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 29(4), pages 263-272, July.
    10. Melachrinoudis, Emanuel & Kozanidis, George, 2002. "A mixed integer knapsack model for allocating funds to highway safety improvements," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(9), pages 789-803, November.
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