Exploiting sets of independent moves in VRP

  • Tommaso Bianconcini
  • David Di Lorenzo
  • Alessandro Lori
  • Fabio Schoen
  • Leonardo Taccari
Research Paper
  • 68 Downloads

Abstract

Most heuristic methods for VRP and its variants are based on the partial exploration of large neighborhoods, typically by means of single, simple moves applied to the current solution. In this paper we define an extended concept of independent moves and show how even a very standard heuristic method can significantly improve when considering the simultaneous application of carefully chosen sets of moves. We show in particular that, when choosing a set such that the total cost variation is equal to the sum of the variations induced by each single move, the quality of solutions obtained is in general very high. When compared with numerical results obtained by some of the best available heuristics on challenging, large scale, problems, our simple algorithm equipped with the application of optimally chosen independent moves displayed very good quality.

Keywords

VRP Tabu search Matheuristic Independent moves 

References

  1. Boschetti M, Maniezzo V (2015) A set covering based matheuristic for a real-world city logistics problem. Int Trans Oper Res 22:169–196CrossRefGoogle Scholar
  2. Bosco A, Laganà D, Musmanno R, Vocaturo F (2014) A matheuristic algorithm for the mixed capacitated general routing problem. Networks 64(4):262–281CrossRefGoogle Scholar
  3. Bräysy O, Gendreau M (2005) Vehicle routing problem with time windows, part i: route construction and local search algorithms. Trans Sci 39(1):104–118CrossRefGoogle Scholar
  4. Congram RK, Potts CN, van de Velde SL (2002) An iterated dynasearch algorithm for the single-machine total weighted tardiness scheduling problem. INFORMS J Comput 14(1):52–67CrossRefGoogle Scholar
  5. Corman F, Voß S, Negenborn RR (eds) (2015) An ant colony-based matheuristic approach for solving a class of vehicle routing problems. Springer International Publishing, ChamGoogle Scholar
  6. Dayarian I, Crainic TG, Gendreau M, Rei W (2016) An adaptive large neighborhood search heuristic for a multi-period vehicle routing problem. Transp Res Part E: Logist Transp Rev 95:95–123CrossRefGoogle Scholar
  7. De Franceschi R, Fischetti M, Toth P (2006) A new ILP-based refinement heuristic for vehicle routing problems. Math Program 105(2–3):471–499CrossRefGoogle Scholar
  8. Ergun Ö, Orlin JB, Steele-Feldman A (2006) Creating very large scale neighborhoods out of smaller ones by compounding moves. J Heuristics 12(1):115–140CrossRefGoogle Scholar
  9. Foster BA, Ryan DM (1976) An integer programming approach to the vehicle scheduling problem. J Oper Res Soc 27(2):367–384CrossRefGoogle Scholar
  10. Gurobi Optimization Inc (2016) Gurobi optimizer reference manual. http://www.gurobi.com. Accessed 24 Mar 2017
  11. Kelly JP, Xu J (1999) A set-partitioning-based heuristic for the vehicle routing problem. INFORMS J Comput 11(2):161–172CrossRefGoogle Scholar
  12. Koç Ç, Bektaş T, Jabali O, Laporte G (2015) A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows. Comput Oper Res 64:11–27CrossRefGoogle Scholar
  13. Mancini S (2016) A real-life multi depot multi period vehicle routing problem with a heterogeneous fleet: Formulation and adaptive large neighborhood search based matheuristic. Transportation Research Part C: Emerging Technologies, pp. 100–112Google Scholar
  14. Nemhauser GL, Wolsey LA (1988) Integer programming and combinatorial optimization. Wiley, New YorkGoogle Scholar
  15. Pillac V, Guéret C, Medaglia AL (2013) A parallel matheuristic for the technician routing and scheduling problem. Optim Lett 7(7):1525–1535CrossRefGoogle Scholar
  16. Potts CN, van de Velde SL (1995) Dynasearch-Iterative local improvement by dynamic programming. Part I. The traveling salesman problem. Tech. rep., University of TwenteGoogle Scholar
  17. Riise A, Burke EK (2014) On parallel local search for permutations. J Oper Res Soc 66(5):822–831CrossRefGoogle Scholar
  18. Rochat Y, Taillard ÉD (1995) Probabilistic diversification and intensification in local search for vehicle routing. J Heuristics 1(1):147–167CrossRefGoogle Scholar
  19. Rousseau LM, Gendreau M, Pesant G (2002) Using constraint-based operators to solve the vehicle routing problem with time windows. J Heuristics 8(1):43–58CrossRefGoogle Scholar
  20. Schmid V, Doerner KF, Hartl RF, Savelsbergh MW, Stoecher W (2009) A hybrid solution approach for ready-mixed concrete delivery. Transp Sci 43(1):70–85CrossRefGoogle Scholar
  21. Subramanian A, Uchoa E, Ochi LS (2013) A hybrid algorithm for a class of vehicle routing problems. Comput Oper Res 40(10):2519–2531CrossRefGoogle Scholar
  22. Toth P, Vigo D (2014) Vehicle routing: problems, methods, and applications, second, edition edn. SIAM/MOS, PhiladelphiaCrossRefGoogle Scholar
  23. Uchoa E, Pecin D, Pessoa A, Poggi M, Vidal T, Subramanian A (2017) New benchmark instances for the capacitated vehicle routing problem. Eur J Oper Res 257(3):845–858CrossRefGoogle Scholar
  24. Vidal T, Crainic TG, Gendreau M, Prins C (2014) A unified solution framework for multi-attribute vehicle routing problems. Eur J Oper Res 234(3):658–673CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg and EURO - The Association of European Operational Research Societies 2017

Authors and Affiliations

  1. 1.Fleetmatics ResearchFirenzeItaly
  2. 2.DINFOUniversità degli Studi di FirenzeFirenzeItaly

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