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Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm

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Abstract

The aim of the traveling salesman problem (TSP) is to find the cheapest way of visiting all elements in a given set of cities and returning to the starting point. In solutions presented in the literature costs of travel between nodes (cities) are based on Euclidean distances, the problem is symmetric and the costs are constant and crisp values. Practical application in road transportation and supply chains are often fuzzy. The risk attitude depends on the features of the given operation. The model presented in this paper handles the fuzzy, time dependent nature of the TSP and also gives solution for the asymmetric loss aversion by embedding the risk attitude into the fitness function of the bacterial memetic algorithm. Computational results are presented as well.

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References

  1. Ammar EE, Youness EA (2005) Study on multiobjective transportation problem with fuzzy numbers. Appl Math Comput 166: 241–253

    Article  MATH  MathSciNet  Google Scholar 

  2. Applegate DL, Bixby RE, Chvátal V, Cook WJ (2006) The traveling salesman problem, a computational study. Princeton University Press, Princeton, pp 10–11

    MATH  Google Scholar 

  3. Aumann RJ (1997) Rationality and bounded rationality. Games Econ Behav 21: 2–14

    Article  MATH  MathSciNet  Google Scholar 

  4. Bontoux B, Feillet D (2008) Ant colony optimization for the traveling purchaser problem. Comput Oper Res 35: 628–637

    Article  MATH  MathSciNet  Google Scholar 

  5. Botzheim J, Hámori B, Kóczy LT, Ruano AE (2002) Bacterial algorithm applied for fuzzy rule extraction. In: Proceedings of the international conference on information processing and management of uncertainty in knowledge-based systems. Annecy, France, pp 1021–1026

  6. Botzheim J, Drobics M, Kóczy LT (July 2004) Feature selection using bacterial optimization. In: Proceedings of the international conference on information processing and management of uncertainty in knowledge-based systems. IPMU 2004, Perugia, pp 797–804

  7. Botzheim J, Cabrita C, Kóczy LT, Ruano AE (2005) Fuzzy rule extraction by bacterial memetic algorithms. IFSA 2005, Beijing, pp 1563–1568

    Google Scholar 

  8. Botzheim J, Földesi P, Kóczy LT (2009) Solution for fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm. IFSA 2009, Lisbon, pp 1667–1672

    Google Scholar 

  9. Ding C, Cheng Y, He M (2007) Two-level genetic algorithm for clustered traveling salesman problem with application in large-scale TSPs. Tsinghua Sci Technol 12(4): 459–465

    Article  MATH  MathSciNet  Google Scholar 

  10. Favaretto D, Moretti E, Pellegrini P (2006) An ant colony system approach for variants of traveling salesman problem with time windows. J Inform Optimiz Sci 27: 35–54

    MATH  Google Scholar 

  11. Földesi P, Botzheim J, Kóczy LT (2008) Fuzzy exponents for heuristic based applications. Acta Technica Jaurinensis Series Intelligentia Computatorica 1(3): 423–435

    Google Scholar 

  12. Földesi P, Botzheim J (2008) Solution for modified traveling salesman problem with variable cost matrix using bacterial evolutionary algorithm. Acta Technica Jaurinensis Series Logistica 1(2): 159–171

    Google Scholar 

  13. Gholamian MR, Fatemi Ghomi SMT, Ghazanfari M (2007) A hybrid system for multiobjective problems—a case study in NP-hard problems. Knowl Based Syst 20: 426–436

    Article  Google Scholar 

  14. Guan-Chun L, Shih-Wei L (2006) A bacterial evolutionary algorithm for the job shop scheduling problem. J Chin Inst Ind Eng 23(3): 185–191

    Article  Google Scholar 

  15. Hasan SMK, Sarkar R, Essam D, Cornforth D (2009) Memetic algorithms for solving job-shop scheduling problems. Memetic Comput 1: 69–83

    Article  Google Scholar 

  16. Held MR, Karp RM (1962) A dynamic programming approaches to sequencing problems. J Soc Ind Appl Math 10: 196–210

    Article  MATH  MathSciNet  Google Scholar 

  17. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. The MIT Press, Cambridge

    Google Scholar 

  18. Hsiao-Fan W, Yu-Pin W (2002) Time-constrained Chinese postman problems. Comput Math Appl 44: 375–387

    Article  MATH  MathSciNet  Google Scholar 

  19. Kikuchi S, Chakroborty P (2006) Place of possibility theory in transportation analysis. Trans Res B 40: 595–615

    Article  Google Scholar 

  20. Klir GJ (1997) Fuzzy arithmetic with requisite constrains. Fuzzy Sets Syst 91: 165–175

    Article  MATH  MathSciNet  Google Scholar 

  21. Köbberling V, Wakker PP (2005) An index of loss aversion. J Econ Theory 122: 119–131

    Article  MATH  Google Scholar 

  22. Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling-salesman problem. Oper Res 21: 498–516

    Article  MATH  MathSciNet  Google Scholar 

  23. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program. Technical Report. California

  24. Nawa NE, Furuhashi T (1999) Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Trans Fuzzy Syst 7: 608–616

    Article  Google Scholar 

  25. Oussalah M (2002) On the compability between defuzzification and fuzzy arithmetic operations. Fuzzy Sets Syst 128: 247–260

    Article  MATH  MathSciNet  Google Scholar 

  26. Ozcan E, Erenturk M (2004) A brief review of memetic algorithms for solving euclidean 2D traveling salesrep problem. In: Turkish symposium on artificial intelligence and neural networks

  27. Shi XC, Liang YC, Lee HP, Lu C, Wang QX (2007) Particle swarm optimization-based algorithms for TSP and generalized TSP. Inform Process Lett 103: 169–176

    Article  MATH  MathSciNet  Google Scholar 

  28. Shiang-Tai L (2007) Geometric programming with fuzzy parameters in engineering optimization. Int J Approx Reason 46: 484–498

    Article  MATH  Google Scholar 

  29. Stefanini L, Sorini L, Guerra ML (2006) Parametric representations of fuzzy numbers and application to fuzzy calculus. Fuzzy Sets Syst 157: 2423–2455

    Article  MATH  MathSciNet  Google Scholar 

  30. Teoh EJ, Tan KC, Tang HJ, Xiang C, Goh CK (2008) An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem. Neurocomputing 71: 1359–1372

    Article  Google Scholar 

  31. Ye J, Tanaka M, Tanino T (1996) Eugenics-based genetic algorithm. IEICE Trans Inform Syst E79-D(5): 600–607

    Google Scholar 

  32. Yin J, Branke J (2005) Evolutionary optimization in uncertain environments—a survey. IEEE Trans Evol Comput 9(3): 303–317

    Article  Google Scholar 

  33. Yu-Hsin Liu (2007) A hybrid scatter search for the probabilistic traveling salesman problem. Comput Oper Res 34: 2949–2963

    Article  MATH  Google Scholar 

  34. Yu-Wan C, Yong-Zai L, Penf C (2007) Optimization with extremal dynamics for the traveling salesman problem. Physica A 385: 115–123

    Article  Google Scholar 

  35. Zadeh LA (1975) The concept of linguistic variable and its application to approximate reasoning. Inform Sci Part 1 8:199–249 (Part 2, 301–357, Part 3, 9:43–80)

    Google Scholar 

  36. Zhong Q, Yue Z, Guangyuan W (1998) Fuzzy random variable-valued exponential function, logarithmic function and power function. Fuzzy Sets Syst 99: 311–324

    Article  MATH  Google Scholar 

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Correspondence to Péter Földesi.

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Földesi, P., Botzheim, J. Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm. Memetic Comp. 2, 259–271 (2010). https://doi.org/10.1007/s12293-010-0037-4

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