single-jc.php

JACIII Vol.18 No.2 pp. 190-196
doi: 10.20965/jaciii.2014.p0190
(2014)

Paper:

Flexible Route Planning for Sightseeing with Fuzzy Random and Fatigue-Dependent Satisfactions

Takashi Hasuike*1, Hideki Katagiri*2, Hiroe Tsubaki*3,
and Hiroshi Tsuda*4

*1Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan

*2Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

*3Department of Data Science, The Institute of Statistical Mathematics, 10-3 Midorimachi, Tachikawa, Tokyo 190-8562, Japan

*4Faculty of Science and Engineering, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0321, Japan

Received:
October 11, 2013
Accepted:
January 13, 2014
Published:
March 20, 2014
Keywords:
route planning for sightseeing, flexibility, fuzzy random and fatigue-dependent satisfactions, mathematical modelling
Abstract
This paper proposes a flexible route planning problem for sightseeing with fuzzy random variables for travel times and satisfaction with activities under general sightseeing constraints. Travel time between sightseeing sites and satisfactions with activities depend on weather and climate conditions, and on traveler fatigue, so both fuzzy random variables for travel times and satisfactions and traveler fatigue-dependence are introduced. Tourists are likely to plan favored without drastically changing from the optimal route under usual conditions such as fine weather that suddenly changes for the worse. A route planning problem is proposed to obtain a favorite route similar to the optimal route under usual conditions. Trapezoidal fuzzy numbers and order relations are introduced as a basic case of fuzzy numbers. From order relations, the proposed model is transformed into an extended model of network optimization problems. A numerical example is used to compare the proposed model to standard route planning problems in sightseeing.
Cite this article as:
T. Hasuike, H. Katagiri, H. Tsubaki, and H. Tsuda, “Flexible Route Planning for Sightseeing with Fuzzy Random and Fatigue-Dependent Satisfactions,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.2, pp. 190-196, 2014.
Data files:
References
  1. [1] W. Souffriau, P. Vansteenwegen, J. Vertommen, G. V. Berghe, and D. V. Oudheusden, “A personalized tourist trip design algorithm for mobile tourist guides,” Applied Artificial Intelligence, Vol.22, No.10, pp. 964-985, 2008.
  2. [2] K. G. Zografos and K. N. Androutsopoulos, “Algorithms for itinerary planning in multimodal transportation networks,” IEEE Trans. on Intelligent Transportation System, Vol.9, No.1, pp. 175-184, 2008.
  3. [3] R. A. Abbaspour and F. Samadzadegan, “Time-dependent personal tour planning and scheduling in metropolises,” Expert Systems with Applications, Vol.38, pp. 12439-12452, 2011.
  4. [4] L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol.8, pp. 338-353, 1965.
  5. [5] C. Carlsson and R. Fuller, “Fuzzy Reasoning in Decision Making and Optimization,” Physica Verlag, 2002.
  6. [6] H. Kwakernaak, “Fuzzy random variable-I,” Information Sciences, Vol.15, pp. 1-29, 1978.
  7. [7] M. L. Puri and D. A. Ralescu, “Fuzzy random variables,” J. ofMathematical Analysis and Applications, Vol.114, pp. 409-422, 1986.
  8. [8] R. R. Yager, “A procedure for ordering fuzzy subsets of the unit interval,” Information Sciences, Vol.24, pp. 143-161, 1981.
  9. [9] M. Fischetti, J. S. Gonzalez, and P. Toth, “Solving the orienteering problem through branch-and-cut,” INFORMS J. on Computing, Vol.10, No.2, pp. 133-148, 1998.
  10. [10] I. M. Chao, B. L. Golden, and E. A. Wasil, “A fast and effective heuristic for the orienteering problem,” European J. of Operational Research, Vol.88, No.3, pp. 475-489, 1996.
  11. [11] J. L. Kennington and C. D. Nicholson, “The uncapacitated timespace fixed-charge network flow problem; an empirical investigation of procedures for arc capacity assignment,” INFORMS J. of Computing, Vol.22, pp. 326-337, 2009.
  12. [12] Q. Wang, X. Sun, B.L. Golden, and J. Jia, “Using artificial neural networks to solve the orienteering problem,” Annals of Operations Research, Vol.61, pp. 111-120, 1995.
  13. [13] M. Gendreau, G. Laporte, and F. Semet, “A tabu search heuristic for the undirected selective traveling salesman problem,” European J. of Operational Research, Vol.106, No.2-3, pp. 539-545, 1998.
  14. [14] H. Tang and E. Miller-Hooks, “A tabu search heuristic for the team orienteering problem,” Computers & Operations Research, Vol.32, pp. 1379-1407, 2005.
  15. [15] L. Ke, C. Archetti, and Z. Feng, “Ants can solve the team orienteering problem,” Computers & Industrial Engineering, Vol.54, No.3, pp. 648-665, 2008.
  16. [16] M. Fischetti, J. J. Salazar-Gonzalez, and P. Toth, “The generalized traveling salesman and orienteering problems,” In G. Gutin & A. P. Punnen (Eds.), The traveling salesman problem and its variations, Dordrecht: Kluwer Academic Publisher, pp. 609-662, 2002.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024