A Local Search Heuristic with Self-tuning Parameter for Permutation Flow-Shop Scheduling Problem

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Dengiz B., Alabas-Uslu C., Sabuncuoglu I.

IEEE Symposium on Computational Intelligence in Scheduling, Tennessee, United States Of America, 30 March - 02 April 2009, pp.62-63 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/scis.2009.4927016
  • City: Tennessee
  • Country: United States Of America
  • Page Numbers: pp.62-63
  • Abdullah Gül University Affiliated: Yes


In this paper, a new local search metaheuristic is proposed for the permutation flow-shop scheduling problem. In general, metaheuristics are widely used to solve this problem due to its NP-completeness. Although these heuristics are quite effective to solve the problem, they suffer from the need to optimize parameters. The proposed heuristic, named STLS, has a single self-tuning parameter which is calculated and updated dynamically based on both the response surface information of the problem field and the performance measure of the method throughout the search process. Especially, application simplicity of the algorithm is attractive for the users. Results of the experimental study show that STLS generates high quality solutions and outperforms the basic tabu search, simulated annealing, and record-to-record travel algorithms which are well-known local search based metaheuristics.