Biobjective Sightseeing Route Planning with Uncertainty Dependent on Tourist’s Tiredness Responding Various Conditions

  • Takashi Hasuike
  • Hideki Katagiri
  • Hiroe Tsubaki
  • Hiroshi Tsuda
Chapter

Abstract

This paper proposes a biobjective route planning problem for sightseeing with fuzzy random traveling times and satisfaction of sightseeing activities under general sightseeing constraints and various conditions. In general, traveling times among sightseeing sites and satisfactions of activities depend on weather and climate conditions. Furthermore, the satisfactions also depend on the tourist’s tiredness. Therefore, not only fuzzy random variables for traveling times and satisfactions but also the tiredness-dependency is introduced. In addition, the tourist will like to do a route planning without drastically changing from the optimal route to each condition. A route planning problem is proposed to obtain the favorable common route supplying target satisfactions under various conditions. As a basic case of fuzzy numbers, trapezoidal fuzzy numbers and the order relation are introduced. From the order relation for fuzziness and the transformation for the biobjective, the proposed model is transformed into an extended model of network optimization problems.

Keywords

Biobjective programming Fuzzy random variable Mathematical modeling Network optimization Sightseeing route planning Tiredness-dependency 

References

  1. 1.
    R.A. Abbaspour, F. Samadzadegan, Time-dependent personal tour planning and scheduling in metropolises. Expert Syst. Appl. 38, 12439–12452 (2011)CrossRefGoogle Scholar
  2. 2.
    C. Carlsson, R. Fuller, Fuzzy Reasoning in Decision Making and Optimization (Springer, Berlin, 2002)CrossRefMATHGoogle Scholar
  3. 3.
    I.M. Chao, B.L. Golden, E.A. Wasil, A fast and effective heuristic for the orienteering problem. Eur. J. Oper. Res. 88(3), 475–489 (1996)CrossRefMATHGoogle Scholar
  4. 4.
    M. Fischetti, J.S. Gonzalez, P. Toth, Solving the orienteering problem through branch-and-cut. INFORMS J. Comput. 10(2), 133–148 (1998)CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    M. Fischetti, J.J. Salazar-González, P. Toth, The Generalized Traveling Salesman and Orienteering Problems, in The Traveling Salesman Problem and its Variations, ed. by G. Gutin, A.P. Punnen (Kluwer Academic Publisher, Dordrecht, 2002), pp. 609–662Google Scholar
  6. 6.
    M. Gendreau, G. Laporte, F. Semet, A tabu search heuristic for the undirected selective traveling salesman problem. Eur. J. Oper. Res. 106(2–3), 539–545 (1998)CrossRefMATHGoogle Scholar
  7. 7.
    T. Hasuike, H. Katagiri, H. Tsubaki, H. Tsuda, Flexible route planning for sightseeing with fuzzy random and fatigue-dependent satisfactions. J. Ref: J. Adv. Comput. Intell. Intell. Inf. 18(2), 190–196 (2014)Google Scholar
  8. 8.
    T. Hasuike, H. Katagiri, H. Tsubaki, H. Tsuda, Sightseeing route planning responding various conditions with fuzzy random satisfactions dependent on tourist’s tiredness, in Proceedings of the International MultiConference of Engineers and Computer Scientists 2014, IMECS 2014, Hong Kong, 12–14 Mar 2014. Lecture Notes in Engineering and Computer Science, pp. 1232–1236Google Scholar
  9. 9.
    H. Kwakernaak, Fuzzy random variable-I. Inf. Sci. 15, 1–29 (1978)CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    L. Ke, C. Archetti, Z. Feng, Ants can solve the team orienteering problem. Comput. Ind. Eng. 54(3), 648–665 (2008)CrossRefGoogle Scholar
  11. 11.
    J.L. Kennington, C.D. Nicholson, The uncapacitated time-space fixed-charge network flow problem; an empirical investigation of procedures for arc capacity assignment. INFORMS J. Comput. 22, 326–337 (2009)CrossRefGoogle Scholar
  12. 12.
    M.L. Puri, D.A. Ralescu, Fuzzy random variables. J. Math. Anal. Appl. 114, 409–422 (1986)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    W. Souffriau, P. Vansteenwegen, J. Vertommen, G.V. Berghe, D.V. Oudheusden, A personalized tourist trip design algorithm for mobile tourist guides. Appl. Artif. Intell. 22(10), 964–985 (2008)CrossRefGoogle Scholar
  14. 14.
    H. Tang, E. Miller-Hooks, A tabu search heuristic for the team orienteering problem. Comput. Oper. Res. 32, 1379–1407 (2005)CrossRefGoogle Scholar
  15. 15.
    Q. Wang, X. Sun, B.L. Golden, J. Jia, Using artificial neural networks to solve the orienteering problem. Ann. Oper. Res. 61, 111–120 (1995)CrossRefMATHGoogle Scholar
  16. 16.
    R.R. Yager, A procedure for ordering fuzzy subsets of the unit interval. Inf. Sci. 24, 143–161 (1981)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    L.A. Zadeh, Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefMATHMathSciNetGoogle Scholar
  18. 18.
    K.G. Zografos, K.N. Androutsopoulos, Algorithms for itinerary planning in multimodal transportation networks. IEEE Trans. Intell. Transp. Syst. 9(1), 175–184 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Takashi Hasuike
    • 1
  • Hideki Katagiri
    • 2
  • Hiroe Tsubaki
    • 3
  • Hiroshi Tsuda
    • 4
  1. 1.Graduate School of Information Science and TechnologyOsaka UniversityOsakaJapan
  2. 2.Graduate School of EngineeringHiroshima UniversityHiroshimaJapan
  3. 3.Department of Data ScienceThe Institute of Statistical MathematicsTokyoJapan
  4. 4.Faculty of Science and EngineeringDoshisha UniversityKyotoJapan

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