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Energy Saving-Oriented Multi-Depot Vehicle Routing Problem with Time Windows in Disaster Relief

Author

Listed:
  • Peng Xu

    (Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116081, China)

  • Qixing Liu

    (Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116081, China)

  • Yuhu Wu

    (Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian 116081, China)

Abstract

This paper studies the distribution of emergency relief for electric vehicles (EVs), which considers energy saving, multi-depot, and vehicle routing problems with time windows, and the named energy saving-oriented multi-depot vehicle routing problem with time windows (ESMDVRPTW). Our aim is to find routes for EVs such that all the shelter demands are fulfilled during their time windows and the total cost traveled by the fleet is minimized. To this end, we formulate the ESMDVRPTW as a mixed-integer linear programming model. Since the post-disaster transportation network contains a large number of vertices and arcs composed of vertices, we propose a two-stage approach to solve the ESMDVRPTW. The first stage is to obtain the minimal travel cost between any two vertices in real-time on a post-disaster transportation network using the proposed Floyd algorithm combined with the neighboring list (Floyd-NL algorithm). In the second stage, we develop the genetic algorithm (GA) incorporating large neighborhood search (GA-LNS), which determines the delivery scheme of shelters. Simulation results of the MDVRPTW benchmark illustrate that the performance of the GA-LNS is better than GA, simulated annealing (SA) and tabu search (TS). Finally, case studies are constructed on two real cases acquired from the OpenStreetMap (OSM) generated by the Quantum Geographic Information System (QGIS) in Ichihara city, Japan, and the test results of case studies show the effectiveness of the proposed two-stage approach.

Suggested Citation

  • Peng Xu & Qixing Liu & Yuhu Wu, 2023. "Energy Saving-Oriented Multi-Depot Vehicle Routing Problem with Time Windows in Disaster Relief," Energies, MDPI, vol. 16(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1992-:d:1071658
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    References listed on IDEAS

    as
    1. Wei Xu & Chenghao Zhang & Ming Cheng & Yucheng Huang, 2022. "Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery: Mathematical Modeling and Adaptive Large Neighborhood Search Heuristic Method," Energies, MDPI, vol. 15(23), pages 1-25, December.
    2. Tomislav Erdelić & Tonči Carić, 2022. "Goods Delivery with Electric Vehicles: Electric Vehicle Routing Optimization with Time Windows and Partial or Full Recharge," Energies, MDPI, vol. 15(1), pages 1-27, January.
    3. David Pisinger & Stefan Ropke, 2010. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 399-419, Springer.
    4. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    5. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).
    6. Wei, Lijun & Zhang, Zhenzhen & Zhang, Defu & Leung, Stephen C.H., 2018. "A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints," European Journal of Operational Research, Elsevier, vol. 265(3), pages 843-859.
    7. Danny García Sánchez & Alejandra Tabares & Lucas Teles Faria & Juan Carlos Rivera & John Fredy Franco, 2022. "A Clustering Approach for the Optimal Siting of Recharging Stations in the Electric Vehicle Routing Problem with Time Windows," Energies, MDPI, vol. 15(7), pages 1-19, March.
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