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Optimal resource allocation among transit agencies for fleet management

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  • Mathew, Tom V.
  • Khasnabis, Snehamay
  • Mishra, Sabyasachee
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    Abstract

    Most transit agencies require government support for the replacement of their aging fleet. A procedure for equitable resource allocation among competing transit agencies for the purpose of transit fleet management is presented in this study. The proposed procedure is a 3-dimensional model that includes the choice of a fleet improvement program, agencies that may receive them, and the timing of investments. Earlier efforts to solve this problem involved the application of 1- or 2-dimensional models for each year of the planning period. These may have resulted in suboptimal solution as the models are blind to the impact of the fleet management program of the subsequent years. Therefore, a new model to address a long-term planning horizon is proposed. The model is formulated as a non-linear optimization problem of maximizing the total weighted average remaining life of the fleet subjected to improvement program and budgetary constraints. Two variants of the problem, one with an annual budget constraint and the other with a single budget constraint for the entire planning period, are formulated. Two independent approaches, namely, branch and bound algorithm and genetic algorithm are used to obtain the solution. An example problem is solved and results are discussed in details. Finally, the model is applied to a large scale real-world problem and a detailed analysis of the results is presented.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 44 (2010)
    Issue (Month): 6 (July)
    Pages: 418-432

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    Handle: RePEc:eee:transa:v:44:y:2010:i:6:p:418-432

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    Related research

    Keywords: Transit fleet management Resource allocation Genetic algorithm Branch and bound algorithm;

    References

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    1. 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.
    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. 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.
    5. 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.
    6. 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.
    7. 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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