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Approximation schemes for Euclidean vehicle routing problems with time windows

Author

Listed:
  • Liang Song

    (Harbin Institute of Technology Shenzhen Graduate School
    Shenzhen Key Laboratory of Internet Information Collaboration)

  • Hejiao Huang

    (Harbin Institute of Technology Shenzhen Graduate School
    Shenzhen Key Laboratory of Internet Information Collaboration)

  • Hongwei Du

    (Harbin Institute of Technology Shenzhen Graduate School
    Shenzhen Key Laboratory of Internet Information Collaboration)

Abstract

The vehicle routing problem with time windows (VRPTW) is a variant of the classical vehicle routing problem. The paper considers two dimensional and one dimensional VRPTW, in which each demand must be serviced within the time window which is designated by its customer. In the two dimensional problem, each customer has the same unit demand. The paper gives a quasi-polynomial time approximation scheme and an asymptotic polynomial time approximation scheme for the two dimensional and one dimensional problems under the Euclidean setting, respectively. With reasonable vehicle speed requirements, our algorithms could generate the solutions whose the total route length is $$(1 + O(\varepsilon ))$$ ( 1 + O ( ε ) ) times of that of the optimum solutions.

Suggested Citation

  • Liang Song & Hejiao Huang & Hongwei Du, 2016. "Approximation schemes for Euclidean vehicle routing problems with time windows," Journal of Combinatorial Optimization, Springer, vol. 32(4), pages 1217-1231, November.
  • Handle: RePEc:spr:jcomop:v:32:y:2016:i:4:d:10.1007_s10878-015-9931-5
    DOI: 10.1007/s10878-015-9931-5
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    References listed on IDEAS

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    1. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    2. S. F. Ghannadpour & S. Noori & R. Tavakkoli-Moghaddam, 2014. "A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority," Journal of Combinatorial Optimization, Springer, vol. 28(2), pages 414-446, August.
    3. Roberto Baldacci & Aristide Mingozzi & Roberto Roberti, 2011. "New Route Relaxation and Pricing Strategies for the Vehicle Routing Problem," Operations Research, INFORMS, vol. 59(5), pages 1269-1283, October.
    4. Tao Zhang & W. Art Chaovalitwongse & Yuejie Zhang, 2014. "Integrated Ant Colony and Tabu Search approach for time dependent vehicle routing problems with simultaneous pickup and delivery," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 288-309, July.
    5. Philippe Lacomme & Christian Prins & Wahiba Ramdane-Cherif, 2004. "Competitive Memetic Algorithms for Arc Routing Problems," Annals of Operations Research, Springer, vol. 131(1), pages 159-185, October.
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    Cited by:

    1. Jianming Zhu & Shuyue Liu & Smita Ghosh, 2019. "Model and algorithm of routes planning for emergency relief distribution in disaster management with disaster information update," Journal of Combinatorial Optimization, Springer, vol. 38(1), pages 208-223, July.

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