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A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows

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  • Qi, Mingyao
  • Lin, Wei-Hua
  • Li, Nan
  • Miao, Lixin

Abstract

For VRP with time windows (VRPTW) solved by conventional cluster-first and route-second approach, temporal information is usually considered with vehicle routing but ignored in the process of clustering. We propose an alternative approach based on spatiotemporal partitioning to solving a large-scale VRPTW, considering jointly the temporal and spatial information for vehicle routing. A spatiotemporal representation for the VRPTW is presented that measures the spatiotemporal distance between two customers. The resulting formulation is then solved by a genetic algorithm developed for k-medoid clustering of large-scale customers based on the spatiotemporal distance. The proposed approach showed promise in handling large scale networks.

Suggested Citation

  • Qi, Mingyao & Lin, Wei-Hua & Li, Nan & Miao, Lixin, 2012. "A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 248-257.
    • Handle: RePEc:eee:transe:v:48:y:2012:i:1:p:248-257
    DOI: 10.1016/j.tre.2011.07.001
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    2. Tu, Wei & Fang, Zhixiang & Li, Qingquan & Shaw, Shih-Lung & Chen, BiYu, 2014. "A bi-level Voronoi diagram-based metaheuristic for a large-scale multi-depot vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 84-97.
    3. Wang, Yuan & Lei, Linfei & Zhang, Dongxiang & Lee, Loo Hay, 2020. "Towards delivery-as-a-service: Effective neighborhood search strategies for integrated delivery optimization of E-commerce and static O2O parcels," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 38-63.

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