A Greedy Randomized Adaptive Search for the Surveillance Patrol Vehicle Routing Problem

  • Simona ManciniEmail author
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)


In this chapter a new rich vehicle routing problem is introduced, the Surveillance Patrol Vehicle Routing Problem (SPVRP) . This problem came out from a real need of a surveillance company to create fairer routing plans for its security patrols. The problem consist into routing a set of patrols in order to visit a set of checkpoints. Each checkpoint requires one or more visits, each one of which, to be performed within a fixed time window. A minimum time spacing between two consecutive visits should be observed. The goal is to minimize cost while minimizing, at the same time, time windows and minimum spacing constraints violations. In order to avoid repetitiveness in the routes and to provide more unpredictable routing plans, the company looks for a pool of sensibly different high quality solutions form which, each night they can choose the routing plan to be followed. To address this problem a Greedy Randomized Adaptive Search algorithm (GRASP) , is used to provide good solutions and a further GRASP algorithm is used to generate pools of good solutions. The quality of a pool is measured both in terms of averaged quality of the solutions in the pools and in terms of diversity among each others. Experimental tests on real instances are reported.


Rich Vehicle Routing Multiple Time Windows Heterogenous fleet Greedy Randomized Adaptive Search 


  1. 1.
    R. Baldacci, M. Battarra, D. Vigo, Routing a heterogeneous fleet of vehicles, in The Vehicle Routing Problem: Latest Advances and New Challenges, ed. by B.L. Golden, S. Raghavan, E.A. Wasil (Springer, Heidelberg, 2008), pp. 3–27CrossRefGoogle Scholar
  2. 2.
    R. Bowerman, B. Hall, P. Calamai, A multi-objective optimization approach to urban school bus routing: Formulation and solution method. Transp. Res. A 29, 123–197 (1995)Google Scholar
  3. 3.
    A. Corberan, E. Fernandez, M. Laguna, R. Marti, Heuristic solutions to the problem of routing school buses with multiple objectives. J. Oper. Res. Soc. 53 427–435 (2002)CrossRefGoogle Scholar
  4. 4.
    T.G. Crainic, S. Mancini, G. Perboli, R. Tadei, A GRASP with path-relinking for the two-echelon vehicle routing problem, in Advances in Metaheuristics, ed. by L. Di Gaspero, A. Schaerf, T. Stutzle (Springer, Berlin, 2013), pp. 113–125CrossRefGoogle Scholar
  5. 5.
    T. Feo, M. Resende, Greedy randomized adaptive search procedures. J. Glob. Optim. 6, 109–133 (1995)CrossRefGoogle Scholar
  6. 6.
    P. Festa, M. Resende, An annotated bibliography of GRASP - part I: algorithms. Int. Trans. Oper. Res. 16, 124 (2009)Google Scholar
  7. 7.
    P. Festa, M. Resende, An annotated bibliography of GRASP - part II: applications. Int. Trans. Oper. Res. 16, 131–172 (2009)CrossRefGoogle Scholar
  8. 8.
    M. Gendreau, J. Potvin, Handbook of Metaheuristics, 2nd edn. (Springer, New York, 2010)CrossRefGoogle Scholar
  9. 9.
    I. Giannikos, A multiobjective goal programming model for locating treatment sites and routing hazardous wastes. Eur. J. Oper. Res. 104, 333–342 (1998)CrossRefGoogle Scholar
  10. 10.
    C. Groer, B.L. Golden, E. Wasil, The consistent vehicle routing problem. Manuf. Serv. Oper. Manage. 11(4), 630–643 (2009)CrossRefGoogle Scholar
  11. 11.
    N. Jozefowiez, F. Semet, T. El-Ghazali, Multi-objective vehicle routing problems. Eur. J. Oper. Res. 189, 293–309 (2008)CrossRefGoogle Scholar
  12. 12.
    P. Lacomme, C. Prins, M. Sevaux, A genetic algorithm for a bi-objective capacitated arc routing problem. Comput. Oper. Res. 33, 3473–3493 (2006)CrossRefGoogle Scholar
  13. 13.
    T.-R. Lee, J.H. Ueng, A study of vehicle routing problem with load balancing. Int. J. Phys. Distrib. Logist. Manag. 29, 646–648 (1999)CrossRefGoogle Scholar
  14. 14.
    S. Mancini, Optimizing real-life freight-distribution problems. Supply Chain Forum Int. J. 15(4), 42–50 (2014)Google Scholar
  15. 15.
    S. Mancini, Time dependent travel speed vehicle routing and scheduling on a real road network: the case of Torino. Transp. Res. Proc. 3, 433–441 (2014)CrossRefGoogle Scholar
  16. 16.
    W. Sessomboon, K. Watanabe, T. Irohara, K. Yoshimoto, A study on multi-objective vehicle routing problem considering customer satisfaction with due-time (the creation of Pareto optimal solutions by hybrid genetic algorithm). Trans. Jpn. Soc. Mech. Eng. (1998)Google Scholar
  17. 17.
    P. Toth, D. Vigo, The Vehicle Routing Problem (Society for Industrial and Applied Mathematics, Philadelphia, 2002)CrossRefGoogle Scholar
  18. 18.
    P. Toth, D. Vigo, The granular tabu search and its application to the vehicle-routing problem. INFORMS J. Comput. 15(4), 333–346 (2003)CrossRefGoogle Scholar
  19. 19.
    K.G. Zografos, K.N. Androustsopoulos, A heuristic algorithm for solving hazardous material distribution problems. Eur. J. Oper. Res. 152, 507–519 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Politecnico di TorinoTorinoItaly

Personalised recommendations