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Solving the Team Orienteering Problem with Particle Swarm Optimization

  • Ai, The Jin (Department of Industrial Engineering, Faculty of Industrial Technology, Universitas Atma Jaya Yogyakarta) ;
  • Pribadi, Jeffry Setyawan (Department of Industrial Engineering, Faculty of Industrial Technology, Universitas Atma Jaya Yogyakarta) ;
  • Ariyono, Vincensius (Department of Industrial Engineering, Faculty of Industrial Technology, Universitas Atma Jaya Yogyakarta)
  • Received : 2013.01.17
  • Accepted : 2013.09.10
  • Published : 2013.09.30

Abstract

The team orienteering problem (TOP) or the multiple tour maximum collection problem can be considered as a generic model that can be applied to a number of challenging applications in logistics, tourism, and other fields. This problem is generally defined as the problem of determining P paths, in which the traveling time of each path is limited by $T_{max}$ that maximizes the total collected score. In the TOP, a set of N vertices i is given, each with a score $S_i$. The starting point (vertex 1) and the end point (vertex N) of all paths are fixed. The time $t_{ij}$ needed to travel from vertex i to j is known for all vertices. Some exact and heuristics approaches had been proposed in the past for solving the TOP. This paper proposes a new solution methodology for solving the TOP using the particle swarm optimization, especially by proposing a solution representation and its decoding method. The performance of the proposed algorithm is then evaluated using several benchmark datasets for the TOP. The computational results show that the proposed algorithm using specific settings is capable of finding good solution for the corresponding TOP instance.

Keywords

References

  1. Ai, T. J. and Kachitvichyanukul, V. (2009), A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery, Computers & Operations Research, 36(5), 1693-1702. https://doi.org/10.1016/j.cor.2008.04.003
  2. Archetti, C., Hertz, A., and Speranza, M. (2007), Metaheuristic for the team orienteering problem, Journal of Heuristics, 13(1), 49-76. https://doi.org/10.1007/s10732-006-9004-0
  3. Butt, S. E. and Cavalier, T. M. (1994), A heuristic for the multiple tour maximum collection problem, Computers & Operations Research, 21(1), 101-111. https://doi.org/10.1016/0305-0548(94)90065-5
  4. Chang, B. C. H., Ratnaweera, A., Halgamuge, S. K., and Watson, H. C. (2004), Particle swarm optimisation for protein motif discovery, Genetic Programming and Evolvable Machines, 5(2), 203-214. https://doi.org/10.1023/B:GENP.0000023688.42515.92
  5. Chao, I., Golden, B. L., and Wasil, E. A. (1996a), The team orienteering problem, European Journal of Operational Research, 88(3), 464-474. https://doi.org/10.1016/0377-2217(94)00289-4
  6. Chao, I., Golden, B. L., and Wasil, E. A. (1996b), A fast and effective heuristic for the orienteering problem, European Journal of Operational Research, 88(3), 475-489. https://doi.org/10.1016/0377-2217(95)00035-6
  7. Clerc, M. (2004), Discrete particle swarm optimization, illustrated by the traveling salesman problem. In: New Optimization Techniques in Engineering, Springer, Berlin, Germany, 219-239.
  8. Hu, X. (2006), Particle swarm optimization, cited 2013 Sep 1, Available from: http://www.swarmintelligence.org.
  9. Ke, L., Archetti, C., and Feng, Z. (2008), Ants can solve the team orienteering problem, Computers & Industrial Engineering, 54(3), 648-665. https://doi.org/10.1016/j.cie.2007.10.001
  10. Muthuswamy, S. and Lam, S. S. (2011), Discrete particle swarm optimization for the team orienteering problem, Memetic Computing, 3(4), 287-303. https://doi.org/10.1007/s12293-011-0071-x
  11. Nguyen, S., Ai, T. J., and Kachitvichyanukul, V. (2010), Object Library for Evolutionary Techniques ET-Lib: User's Guide, High Performance Computing Group, Asian Institute of Technology, Thailand.
  12. Souffriau, W., Vansteenwegen, P., Berghe, G. V., and Van Oudheusden, D. (2010), A path relinking approach for the team orienteering problem, Computers & Operations Research, 37(11), 1853-1859. https://doi.org/10.1016/j.cor.2009.05.002
  13. Souffriau, W., Vansteenwegen, P., Vertommen, J., Berghe, G. V., and Van Oudheusden, D. (2008), A personalized tourist trip design algorithm for mobile tourist guides, Applied Artificial Intelligence, 22(10), 964-985. https://doi.org/10.1080/08839510802379626
  14. Tang, H. and Miller-Hooks, E. (2005), A tabu search heuristic for the team orienteering problem, Computers &Operations Research, 32(6), 1379-1407. https://doi.org/10.1016/j.cor.2003.11.008
  15. Tsiligirides, T. (1984), Heuristic methods applied to orienteering, Journal of the Operational Research Society, 35(9), 797-809. https://doi.org/10.1057/jors.1984.162
  16. Vansteenwegen, P., Souffriau, W., Berghe, G. V., and Van Oudheusden, D. (2009a), A guided local search metaheuristic for the team orienteering problem, European Journal of Operational Research, 196(1), 118-127. https://doi.org/10.1016/j.ejor.2008.02.037
  17. Vansteenwegen, P., Souffriau, W., Berghe, G. V., and Van Oudheusden, D. (2009b), Metaheuristics for tourist trip planning. In: Metaheuristics in the Service Industry, Springer, Berlin, Germany, 15-31.
  18. Vansteenwegen, P., Souffriau, W., and Van Oudheusden, D. (2011), The orienteering problem: a survey, European Journal of Operational Research, 209(1), 1-10. https://doi.org/10.1016/j.ejor.2010.03.045