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Solving TSP by considering processing time: Meta-heuristics and fuzzy approaches

Author

Listed:
  • S. H. Nasseri

    (University of Mazandaran)

  • M. H. Khaviari

    (Science and Technology University of Mazandaran)

Abstract

In this paper, general properties of traveling salesman problem has been illustrated, then a model has been introduced to minimize Make-span (time interval which all of jobs will be done) with considering sequence-dependence setup times and processing time. Furthermore, fuzzy sets and its characteristics are applied to a Hard-solvable traveling salesman problem with considering processing times. As it can be seen, traveling salesman problems are NP-hard, so solving problem in huge dimensions is uncontrollably manageable and in other side these kinds of problems in real-world are unavoidable, so a local search can prove their importance. (However this Meta-heuristic methods may not reveal exact optimal solution, but they yield a near-exact optimal solution in undeniable reduced computational time). Here, some of most famous local searches have been explained in their common and normal form, which are Genetic Algorithm (GA), Tabu Search (TS), Simulated Annealing (SA), Ant Colony System (ACO). In rest, a comprehensive comparison through these methods has been shown. This normality in methods structure could help the article to hold no-side-taken and acceptable judgments. In final, point methods analysis and parameter tuning are held.

Suggested Citation

  • S. H. Nasseri & M. H. Khaviari, 2011. "Solving TSP by considering processing time: Meta-heuristics and fuzzy approaches," Fuzzy Information and Engineering, Springer, vol. 3(4), pages 359-378, December.
    • Handle: RePEc:spr:fuzinf:v:3:y:2011:i:4:d:10.1007_s12543-011-0091-8
    DOI: 10.1007/s12543-011-0091-8
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    References listed on IDEAS

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    1. Choobineh, F. Fred & Mohebbi, Esmail & Khoo, Hansen, 2006. "A multi-objective tabu search for a single-machine scheduling problem with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 175(1), pages 318-337, November.
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