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A generic model of motor‐carrier fuel optimization

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  • Yoshinori Suzuki

Abstract

Fuel optimizers are decision models (software products) that are increasingly recognized as effective fuel management tools by U.S. truckload carriers. Using the latest price data of every truck stop, these models calculate the optimal fueling schedule for each route that indicates: (i) which truck stop(s) to use, and (ii) how much fuel to buy at the chosen truck stop(s) to minimize the refueling cost. In the current form, however, these models minimize only the fuel cost, and ignore or underestimate other costs that are affected by the models' decision variables. On the basis of the interviews with carrier managers, truck drivers, and fuel‐optimizer vendors, this article proposes a comprehensive model of motor‐carrier fuel optimization that considers all of the costs that are affected by the model's decision variables. Simulation results imply that the proposed model not only attains lower vehicle operating costs than the commercial fuel optimizers, but also gives solutions that are more desirable from the drivers' viewpoint. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008

Suggested Citation

  • Yoshinori Suzuki, 2008. "A generic model of motor‐carrier fuel optimization," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(8), pages 737-746, December.
  • Handle: RePEc:wly:navres:v:55:y:2008:i:8:p:737-746
    DOI: 10.1002/nav.20317
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Timothy M. Sweda & Irina S. Dolinskaya & Diego Klabjan, 2017. "Adaptive Routing and Recharging Policies for Electric Vehicles," Transportation Science, INFORMS, vol. 51(4), pages 1326-1348, November.
    2. Mesut Yavuz & Ismail Çapar, 2017. "Alternative-Fuel Vehicle Adoption in Service Fleets: Impact Evaluation Through Optimization Modeling," Transportation Science, INFORMS, vol. 51(2), pages 480-493, May.
    3. Schulz, Arne & Suzuki, Yoshinori, 2023. "An efficient heuristic for the fixed-route vehicle-refueling problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    4. Suzuki, Yoshinori & Lan, Bo, 2018. "Cutting fuel consumption of truckload carriers by using new enhanced refueling policies," International Journal of Production Economics, Elsevier, vol. 202(C), pages 69-80.
    5. Timothy M. Sweda & Irina S. Dolinskaya & Diego Klabjan, 2017. "Optimal Recharging Policies for Electric Vehicles," Transportation Science, INFORMS, vol. 51(2), pages 457-479, May.

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