Advertisement

A Matheuristic for the Drilling Rig Routing Problem

  • Igor Kulachenko
  • Polina KononovaEmail author
Conference paper
  • 209 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12095)

Abstract

In this paper, we discuss the real-world Split Delivery Vehicle Routing Problem with Time Windows (SDVRPTW) for drilling rig routing in Siberia and the Far East. There is a set of objects (exploration sites) requiring well-drilling work. Each object includes a known number of planned wells and needs to be served within a given time interval. Several drilling rigs can operate at the same object simultaneously, but their number must not exceed the number of wells planned for this object. A rig that has started the work on a well completes it to the end. The objective is to determine such a set of rig routes (including the number of assigned wells for each object) to perform all well-drilling requests, respecting the time windows, that minimizes the total traveling distance. The main difference with traditional SDVRP is that it is the service time that is split, not the demand.

We propose a mixed-integer linear programming (MILP) model for this problem. To find high-quality solutions, we design the Variable Neighborhood Search based matheuristic. Exact methods are incorporated into a local search to optimize the distribution of well work among the rigs. Time-window constraints are relaxed, allowing infeasible solutions during the search, and evaluation techniques are applied to treat them. Results of computational experiments for the algorithm and a state-of-the-art MILP solver are discussed.

Keywords

Logistics Uncapacitated vehicles Split delivery service Time windows Metaheuristics Mathematical models Optimization problems 

References

  1. 1.
    Aarts, E., Lenstra, J.: Local Search in Combinatorial Optimization. John Wiley & Sons, New York (1997)zbMATHGoogle Scholar
  2. 2.
    Archetti, C., Speranza, M.G.: Vehicle routing problems with split deliveries. Int. Trans. Oper. Res. 19(1–2), 3–22 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Archetti, C., Speranza, M.G.: A survey on matheuristics for routing problems. EURO J. Comput. Optim. 2(4), 223–246 (2014).  https://doi.org/10.1007/s13675-014-0030-7CrossRefzbMATHGoogle Scholar
  4. 4.
    Bramel, J., Simchi-Levi, D.: Probabilistic analyses and practical algorithms for the vehicle routing problem with time windows. Oper. Res. 44(3), 501–509 (1996)CrossRefGoogle Scholar
  5. 5.
    Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, Part I: route construction and local search algorithms. Transp. Sci. 39(1), 104–118 (2005)CrossRefGoogle Scholar
  6. 6.
    Gendreau, M., Tarantilis, C.D.: Solving large-scale vehicle routing problems with time windows: the state-of-the-art. In: CIRRELT-2010-04, Montreal (2010)Google Scholar
  7. 7.
    Golden, B.L., Raghavan, S., Wasil, E.A.: The Vehicle Routing Problem: Latest Advances and New Challenges. Springer, Boston (2008).  https://doi.org/10.1007/978-0-387-77778-8CrossRefzbMATHGoogle Scholar
  8. 8.
    Gutin, G., Punnen, A.: The Traveling Salesman Problem and Its Variations. Springer, Boston (2002).  https://doi.org/10.1007/b101971CrossRefzbMATHGoogle Scholar
  9. 9.
    Hansen, P., Mladenović, N., Todosijević, R., Hanafi, S.: Variable neighborhood search: basics and variants. EURO J. Comput. Optim. 5(3), 423–454 (2016).  https://doi.org/10.1007/s13675-016-0075-xMathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Hemmelmayr, V.C., Doerner, K.F., Hartl, R.F., Vigo, D.: Models and algorithms for the integrated planning of bin allocation and vehicle routing in solid waste management. Transp. Sci. 48, 103–120 (2014)CrossRefGoogle Scholar
  11. 11.
    Ho, S., Haugland, D.: A tabu search heuristic for the vehicle routing problem with time windows and split deliveries. Comput. Oper. Res. 31(12), 1947–1964 (2004)CrossRefGoogle Scholar
  12. 12.
    Irnich, S.: A unified modeling and solution framework for vehicle routing and local search-based metaheuristics. INFORMS J. Comput. 20(2), 270–287 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Kernighan, B., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)CrossRefGoogle Scholar
  14. 14.
    Kulachenko, I., Kononova, P.: The VNS approach for a consistent capacitated vehicle routing problem under the shift length constraints. In: Bykadorov, I., Strusevich, V., Tchemisova, T. (eds.) MOTOR 2019. CCIS, vol. 1090, pp. 51–67. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-33394-2_5CrossRefGoogle Scholar
  15. 15.
    Li, F., Golden, B., Wasil, E.: The open vehicle routing problem: algorithms, large-scale test problems, and computational results. Comput. Oper. Res. 34(10), 2918–2930 (2007)CrossRefGoogle Scholar
  16. 16.
    Maniezzo, V., Stützle, T., Voß, S.: Matheuristics: Hybridizing Metaheuristics and Mathematical Programming. Springer, Boston (2009).  https://doi.org/10.1007/978-1-4419-1306-7CrossRefzbMATHGoogle Scholar
  17. 17.
    Mladenovic, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24, 1097–1100 (1997)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Nagata, Y., Bräysy, O., Dullaert, W.: A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows. Comput. Oper. Res. 37(4), 724–737 (2010)CrossRefGoogle Scholar
  19. 19.
    Savelsbergh, M.W.P.: The vehicle routing problem with time windows: minimizing route duration. INFORMS J. Comput. 4, 146–154 (1992)CrossRefGoogle Scholar
  20. 20.
    Solomon, M.M.: Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35, 254–265 (1985)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Talbi, E.G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken (2009)CrossRefGoogle Scholar
  22. 22.
    Talbi, E.G.: Hybrid Metaheuristics. Springer, Berlin (2013).  https://doi.org/10.1007/978-3-642-30671-6
  23. 23.
    Toth, P., Vigo, D. (eds.): Vehicle Routing: Problems, Methods, and Applications, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2014)zbMATHGoogle Scholar
  24. 24.
    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)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Yakici, E., Karasakal, O.: A min-max vehicle routing problem with split delivery and heterogeneous demand. Optim. Lett. 7, 1611–1625 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Novosibirsk State UniversityNovosibirskRussia
  2. 2.Sobolev Institute of Mathematics SB RASNovosibirskRussia

Personalised recommendations