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Developing green fleet management strategies: Repair/retrofit/replacement decisions under environmental regulation


  • Stasko, Timon H.
  • Oliver Gao, H.


The considerable cost of maintaining large fleets has generated interest in cost minimization strategies. With many related decisions, numerous constraints, and significant sources of uncertainty (e.g. vehicle breakdowns), fleet managers face complex dynamic optimization problems. Existing methodologies frequently make simplifying assumptions or fail to converge quickly for large problems. This paper presents an approximate dynamic programming approach for making vehicle purchase, resale, and retrofit decisions in a fleet setting with stochastic vehicle breakdowns. Value iteration is informed by dual variables from linear programs, as well as other bounds on vehicle shadow prices. Sample problems are based on a government fleet seeking to comply with emissions regulation. The model predicts the expected cost of compliance, the rules the fleet manager will use in deciding how to comply, and the regulation’s impact on the value of vehicles in the fleet. Stricter regulation lowers the value of some vehicle categories while raising the value of others. Such insights can help guide regulators, as well as the fleet managers they oversee. The methodologies developed could be applied more broadly to general multi-asset replacement problems, many of which have similar structures.

Suggested Citation

  • Stasko, Timon H. & Oliver Gao, H., 2012. "Developing green fleet management strategies: Repair/retrofit/replacement decisions under environmental regulation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1216-1226.
  • Handle: RePEc:eee:transa:v:46:y:2012:i:8:p:1216-1226 DOI: 10.1016/j.tra.2012.05.012

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    References listed on IDEAS

    1. Redmer, Adam, 2009. "Optimisation of the exploitation period of individual vehicles in freight transportation companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(6), pages 978-987, November.
    2. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    3. Nejat Karabakal & Jack R. Lohmann & James C. Bean, 1994. "Parallel Replacement under Capital Rationing Constraints," Management Science, INFORMS, vol. 40(3), pages 305-319, March.
    4. 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.
    5. Suzuki, Yoshinori & Pautsch, Gregory R., 2005. "A vehicle replacement policy for motor carriers in an unsteady economy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(5), pages 463-480, June.
    6. Jin, Di & Kite-Powell, Hauke L., 2000. "Optimal fleet utilization and replacement," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 36(1), pages 3-20, March.
    7. Oliver Gao, H. & Stasko, Timon H., 2009. "Diversification in the driveway: mean-variance optimization for greenhouse gas emissions reduction from the next generation of vehicles," Energy Policy, Elsevier, vol. 37(12), pages 5019-5027, December.
    8. Guy T. de Ghellinck & Gary D. Eppen, 1967. "Linear Programming Solutions for Separable Markovian Decision Problems," Management Science, INFORMS, vol. 13(5), pages 371-394, January.
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    Cited by:

    1. Guerrero, Sebastian E. & Madanat, Samer M. & Leachman, Robert C., 2013. "The Trucking Sector Optimization Model: A tool for predicting carrier and shipper responses to policies aiming to reduce GHG emissions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 59(C), pages 85-107.
    2. repec:eee:transb:v:103:y:2017:i:c:p:158-187 is not listed on IDEAS


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