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Routing Strategies for Efficient Deployment of Alternative Fuel Vehicles for Freight Delivery

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  • Dessouky, Maged M
  • Shao, Yihuan E

Abstract

With increasing concerns on environmental issues, recent research on Vehicle Routing Problems (VRP) has added new factors such as greenhouse gas emissions and alternative fuel vehicles into the models. In this report, the authors consider one such promising alternative fuel vehicle, Compressed Natural Gas (CNG). However, due to the limited number of available fueling stations and small fuel tank capacity, CNG trucks face several challenges on their way to replacing traditional diesel trucks. Even though CNG trucks have advantages on less greenhouse gas emissions and cheaper fuel cost, the detours to the fueling station may increase the total travel distance. The authors introduce the CNG Truck Routing Problem with Fueling Stations (CTRPFS) to model decisions to be made with regards to the vehicle routes including the choice of fueling stations. Moreover the authors consider load capacity, fuel tank capacity and the driver’s daily traveling distance limitation. The authors develop a Mixed Integer Programming (MIP) model with preprocessing and valid inequalities to solve the problem optimally. A hybrid heuristic method is also proposed to solve this problem, which combines an Adaptive Large Neighborhood Search (ALNS) with a local search and a MIP model. View the NCST Project Webpage

Suggested Citation

  • Dessouky, Maged M & Shao, Yihuan E, 2017. "Routing Strategies for Efficient Deployment of Alternative Fuel Vehicles for Freight Delivery," Institute of Transportation Studies, Working Paper Series qt0nj024qn, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt0nj024qn
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    References listed on IDEAS

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    1. J-F Chen & T-H Wu, 2006. "Vehicle routing problem with simultaneous deliveries and pickups," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(5), pages 579-587, May.
    2. Michael Schneider & Andreas Stenger & Dominik Goeke, 2014. "The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations," Transportation Science, INFORMS, vol. 48(4), pages 500-520, November.
    3. Laporte, Gilbert, 1992. "The vehicle routing problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(3), pages 345-358, June.
    4. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    5. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    6. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "A review of recent research on green road freight transportation," European Journal of Operational Research, Elsevier, vol. 237(3), pages 775-793.
    7. Schneider, M. & Stenger, A. & Goeke, D., 2014. "The Electric Vehicle Routing Problem with Time Windows and Recharging Stations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 62382, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    8. Khan, Muhammad Imran & Yasmin, Tabassum & Shakoor, Abdul, 2015. "Technical overview of compressed natural gas (CNG) as a transportation fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 785-797.
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