Skip to main content
Log in

Efficient approximation of the metric CVRP in spaces of fixed doubling dimension

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

The capacitated vehicle routing problem (CVRP) is the well-known combinatorial optimization problem having numerous practically important applications. CVRP is strongly NP-hard (even on the Euclidean plane), hard to approximate in general case and APX-complete for an arbitrary metric. Meanwhile, for the geometric settings of the problem, there are known a number of quasi-polynomial and even polynomial time approximation schemes. Among these results, the well-known QPTAS proposed by Das and Mathieu appears to be the most general. In this paper, we propose the first extension of this scheme to a more wide class of metric spaces. Actually, we show that the metric CVRP has a QPTAS any time when the problem is set up in the metric space of any fixed doubling dimension \(d>1\) and the capacity does not exceed \(\mathrm {polylog}{(n)}\).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. And the notation \(\mathrm {CVRP\text{- }SD}({Z,D,w,q})\) and \(\mathrm {CVRP\text{- }SD}^*(Z,D,w,q)\) for the case of CVRP-SD as well

  2. In Sect. 4.4, we provide a dynamic programming algorithm, which finds such a solution for any given random clustering

  3. e.g., \(\beta =3/2\) for the well-known Christofides–Serdyukov algorithm

References

  1. Abraham, I., Bartal, Y., Neiman, O.: Advances in metric embedding theory. Adv. Math. 228(6), 3026–3126 (2011). https://doi.org/10.1016/j.aim.2011.08.003

    Article  MathSciNet  MATH  Google Scholar 

  2. Adamaszek, A., Czumaj, A., Lingas, A.: PTAS for k-tour cover problem on the plane rof moderately large values of \(k\). Int. J. Found. Comput. Sci. 21(6), 893–904 (2010). https://doi.org/10.1142/S0129054110007623

    Article  MATH  Google Scholar 

  3. Arnold, F., Sörensen, K.: Knowledge-guided local search for the vehicle routing problem. Comput. Oper. Res. 105, 32–46 (2019). https://doi.org/10.1016/j.cor.2019.01.002

    Article  MathSciNet  MATH  Google Scholar 

  4. Arora, S.: Polynomial time approximation schemes for Euclidean traveling Salesman and other geometric problems. J. ACM 45, 753–782 (1998)

    Article  MathSciNet  Google Scholar 

  5. Arora, S., Safra, S.: Probabilistic checking of proofs: a new characterization of NP. J. ACM 45, 70–122 (1998). https://doi.org/10.1145/273865.273901

    Article  MathSciNet  MATH  Google Scholar 

  6. Asano, T., Katoh, N., Tamaki, H., Tokuyama, T.: Covering points in the plane by k-tours: towards a polynomial time approximation scheme for general k. In: Proceedings of the Twenty-Ninth Annual ACM Symposium on Theory of Computing, STOC ’97, pp. 275–283. ACM, New York(1997). https://doi.org/10.1145/258533.258602

  7. Avdoshin, S., Beresneva, E.: Local search metaheuristics for capacitated vehicle routing problem: a comparative study. Proc. Inst. Syst. Program. RAS 31, 121–138 (2019). https://doi.org/10.15514/ISPRAS-2019-31(4)-8

    Article  Google Scholar 

  8. Bartal, Y., Gottlieb, L.A., Krauthgamer, R.: The traveling salesman problem: low-dimensionality implies a polynomial time approximation scheme. SIAM J. Comput. 45(4), 1563–1581 (2016). https://doi.org/10.1137/130913328

    Article  MathSciNet  MATH  Google Scholar 

  9. Becker, A., Klein, P.N., Schild, A.: A PTAS for bounded-capacity vehicle routing in planar graphs. In: Friggstad, Z., Sack, J.R., Salavatipour, M.R. (eds.) Algorithms and Data Structures, pp. 99–111. Springer, Cham, Berlin (2019). https://doi.org/10.1007/978-3-030-24766-9_8

    Chapter  Google Scholar 

  10. Chen, G., Ding, Z.: Optimization of transportation routing problem for fresh food by improved ant colony algorithm based on tabu search. Sustainability (2019). https://doi.org/10.3390/su11236584

    Article  Google Scholar 

  11. Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manag. Sci. 6(1), 80–91 (1959)

    Article  MathSciNet  Google Scholar 

  12. Das, A., Mathieu, C.: A quasipolynomial time approximation scheme for Euclidean capacitated vehicle routing. Algorithmica 73, 115–142 (2015). https://doi.org/10.1007/s00453-014-9906-4

    Article  MathSciNet  MATH  Google Scholar 

  13. Demir, E., Huckle, K., Syntetos, A., Lahy, A., Wilson, M.: Vehicle Routing Problem: Past and Future, pp. 97–117. Springer, Berlin (2019). https://doi.org/10.1007/978-3-030-14493-7_7

    Book  Google Scholar 

  14. Frifita, S., Masmoudi, M.: VNS methods for home care routing and scheduling problem with temporal dependencies, and multiple structures and specialties. Int Trans Oper Res 27(1), 291–313 (2020). https://doi.org/10.1111/itor.12604

    Article  MathSciNet  Google Scholar 

  15. Gupta, A., Krauthgamer, R., Lee, J.R.: Bounded geometries, fractals, and low-distortion embeddings. In: 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings, pp. 534–543 (2003). https://doi.org/10.1109/SFCS.2003.1238226

  16. Haimovich, M.: Bounds and heuristics for capacitated routing problems. Math. Oper. Res. 10(4), 527–542 (1985). https://doi.org/10.1287/moor.10.4.527

    Article  MathSciNet  MATH  Google Scholar 

  17. Hokama, P., Miyazawa, F.K., Xavier, E.C.: A branch-and-cut approach for the vehicle routing problem with loading constraints. Expert Syst. Appl. 47, 1–13 (2016). https://doi.org/10.1016/j.eswa.2015.10.013

    Article  Google Scholar 

  18. Khachai, M., Ogorodnikov, Y.: Haimovich–Rinnooy kan polynomial-time approximation scheme for the CVRP in metric spaces of a fixed doubling dimension. Trudy instituta matematiki i mekhaniki UrO RAN 25(4), 235–248 (2019). https://doi.org/10.21538/0134-4889-2019-25-4-235-248

    Article  Google Scholar 

  19. Khachai, M., Ogorodnikov, Y.: Polynomial-time approximation scheme for the capacitated vehicle routing problem with time windows. Proc. Steklov Inst. Math. 307(suppl. 1), S51–S63 (2019). https://doi.org/10.1134/S0081543819070058

    Article  MATH  Google Scholar 

  20. Khachai, M.Y., Dubinin, R.D.: Approximability of the vehicle routing problem in finite-dimensional euclidean spaces. Proc. Steklov Inst. Math. 297(1), 117–128 (2017). https://doi.org/10.1134/S0081543817050133

    Article  MathSciNet  MATH  Google Scholar 

  21. Khachay, M., Dubinin, R.: PTAS for the Euclidean capacitated vehicle routing problem in \(R^d\), LNCS, vol. 9869, pp. 193–205. Springer, Cham, Berlin (2016). https://doi.org/10.1007/978-3-319-44914-2_16

  22. Khachay, M., Ogorodnikov, Y.: Efficient PTAS for the Euclidean CVRP with time windows. In: Analysis of Images, Social Networks and Texts—7th International Conference, AIST 2018, LNCS, vol. 11179, pp. 318–328. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-11027-7_30

  23. Khachay, M., Ogorodnikov, Y.: Approximation scheme for the capacitated vehicle routing problem with time windows and non-uniform demand. In: Mathematical Optimization Theory and Operations Research—18th International Conference (MOTOR 2019). Proceedings, LNCS, vol. 11548, pp. 309–327. Springer, Berlin (2019). https://doi.org/10.1007/978-3-030-22629-9_22

  24. Khachay, M., Ogorodnikov, Y.: Efficient approximation of the capacitated vehicle routing problem in a metric space of an arbitrary fixed doubling dimension. Dokl. Math. 102, 3234–329 (2020). https://doi.org/10.1134/S1064562420040080

    Article  Google Scholar 

  25. Khachay, M., Ogorodnikov, Y., Khachay, D.: An extension of the Das and Mathieu QPTAS to the case of polylog capacity constrained CVRP in metric spaces of a fixed doubling dimension (accepted). In: Mathematical Optimization Theory and Operations Research—19th International Conference (MOTOR 2020). Proceedings, LNCS, vol. 12095. Springer, Berlin (2020)

  26. Khachay, M., Zaytseva, H.: Polynomial time approximation scheme for single-depot Euclidean capacitated vehicle routing problem, LNCS, vol. 9486, pp. 178–190. Springer, Cham, Berlin (2015). https://doi.org/10.1007/978-3-319-26626-8_14

  27. Laporte, G.: Fifty years of vehicle routing. Transp. Sci. 43, 408–416 (2009). https://doi.org/10.1287/trsc.1090.0301

    Article  Google Scholar 

  28. Nalepa, J., Blocho, M.: Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows. Soft. Comput. 20(6), 2309–2327 (2016). https://doi.org/10.1007/s00500-015-1642-4

    Article  Google Scholar 

  29. Nazari, M., Oroojlooy, A., Takáč, M., Snyder, L.V.: Reinforcement learning for solving the vehicle routing problem. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, pp. 9861–9871. Curran Associates Inc., Red Hook (2018)

  30. Necula, R., Breaban, M., Raschip, M.: Tackling dynamic vehicle routing problem with time windows by means of ant colony system. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 2480–2487 (2017). https://doi.org/10.1109/CEC.2017.7969606

  31. Papadimitriou, C.: Euclidean TSP is NP-complete. Theoret. Comput. Sci. 4, 237–244 (1977)

    Article  MathSciNet  Google Scholar 

  32. Pessoa, A.A., Sadykov, R., Uchoa, E.: Enhanced branch-cut-and-price algorithm for heterogeneous fleet vehicle routing problems. Eur. J. Oper. Res. 270(2), 530–543 (2018). https://doi.org/10.1016/j.ejor.2018.04.009

    Article  MathSciNet  MATH  Google Scholar 

  33. Polat, O.: A parallel variable neighborhood search for the vehicle routing problem with divisible deliveries and pickups. Comput. Oper. Res. 85, 71–86 (2017). https://doi.org/10.1016/j.cor.2017.03.009

    Article  MathSciNet  MATH  Google Scholar 

  34. Qiu, M., Fu, Z., Eglese, R., Tang, Q.: A tabu search algorithm for the vehicle routing problem with discrete split deliveries and pickups. Comput. Oper. Res. 100, 102–116 (2018). https://doi.org/10.1016/j.cor.2018.07.021

    Article  MathSciNet  MATH  Google Scholar 

  35. Smid, M.: On some combinatorial problems in metricspaces of bounded doubling dimension (2010). https://people.scs.carleton.ca/ michiel/mst-ann-doubling.pdf. Accessed Dec 6, 2020

  36. Su-Ping, Y., Wei-Wei, M.: An improved ant colony optimization for vrp with time windows. Int. J. Signal Process. Image Process. Pattern Recognit. 9, 327–334 (2016). https://doi.org/10.14257/ijsip.2016.9.10.31

    Article  Google Scholar 

  37. Talwar, K.: Bypassing the embedding: Algorithms for low dimensional metrics. In: Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing, STOC ’04, pp. 281–290. Association for Computing Machinery, New York (2004). https://doi.org/10.1145/1007352.1007399

  38. Toth, P., Vigo, D.: Vehicle Routing: Problems, Methods, and Applications. MOS-Siam Series on Optimization, 2nd edn. SIAM, New Delhi (2014)

    Book  Google Scholar 

  39. Vidal, T., Crainic, T.G., Gendreau, M., Prins, C.: A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time windows. Comput. Oper. Res. 40(1), 475–489 (2013). https://doi.org/10.1016/j.cor.2012.07.018

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

Michael Khachay and Yuri Ogorodnikov were supported by the Ural Mathematical Center and funded by the Russian Foundation for Basic Research, Grant No. 19-07-01243

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Khachay.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khachay, M., Ogorodnikov, Y. & Khachay, D. Efficient approximation of the metric CVRP in spaces of fixed doubling dimension. J Glob Optim 80, 679–710 (2021). https://doi.org/10.1007/s10898-020-00990-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-020-00990-0

Keywords

Navigation