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A Lagrangian Relaxation-Based Solution Method for a Green Vehicle Routing Problem to Minimize Greenhouse Gas Emissions

Author

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  • Yanjie Zhou

    (Department of Industrial Engineering, Pusan National University, Busan 46241, Korea)

  • Gyu M. Lee

    (Department of Industrial Engineering, Pusan National University, Busan 46241, Korea)

Abstract

The effects of greenhouse gas (GHG) on the weather has caused ever-increasing disasters around the world. Many countries are making significant efforts to reduce GHG emissions in all industrial sectors. In this study, a green vehicle routing problem (GVRP) has been formulated as a nonlinear integer programming problem to minimize GHG emissions, considering various realistic factors that include three-dimensional customer locations, gravity, vehicle speed, vehicle operating time, vehicle capacity, rolling resistance, air density, road grade and inertia. Lagrangian relaxation has been introduced to propose a simple solution method. In contrast to traditional vehicle routing problems, the vehicle speed, vehicle weight, and road grade between two customer locations are also determined along with vehicle routes. The computational results demonstrate the effectiveness and efficiency of the proposed solution method.

Suggested Citation

  • Yanjie Zhou & Gyu M. Lee, 2017. "A Lagrangian Relaxation-Based Solution Method for a Green Vehicle Routing Problem to Minimize Greenhouse Gas Emissions," Sustainability, MDPI, vol. 9(5), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:5:p:776-:d:97945
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    References listed on IDEAS

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    Cited by:

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    9. Emna Marrekchi & Walid Besbes & Diala Dhouib & Emrah Demir, 2021. "A review of recent advances in the operations research literature on the green routing problem and its variants," Annals of Operations Research, Springer, vol. 304(1), pages 529-574, September.
    10. Behnke, Martin & Kirschstein, Thomas & Bierwirth, Christian, 2021. "A column generation approach for an emission-oriented vehicle routing problem on a multigraph," European Journal of Operational Research, Elsevier, vol. 288(3), pages 794-809.
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