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A genetic local search algorithm for minimizing total flowtime in the permutation flowshop scheduling problem

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  • Tseng, Lin-Yu
  • Lin, Ya-Tai

Abstract

Recently, the flowshop scheduling problem to minimize total flowtime has attracted more attention from researchers. In this paper, a genetic local search algorithm is proposed to solve this problem. The proposed algorithm hybridizes the genetic algorithm and the tabu search. It employs the genetic algorithm to do the global search and the tabu search to do the local search. The orthogonal-array-based crossover is utilized to enhance the capability of intensification. Also, a novel orthogonal-array-based mutation is proposed, in order to add capability of intensification to the traditional mutation operator. The performance of the proposed genetic local search algorithm is very competitive. It improved 54 out of 90 current best solutions reported in the literature for short-term search, and it also improved 18 out of 20 current best solutions reported in the literature for long-term search.

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  • Tseng, Lin-Yu & Lin, Ya-Tai, 2010. "A genetic local search algorithm for minimizing total flowtime in the permutation flowshop scheduling problem," International Journal of Production Economics, Elsevier, vol. 127(1), pages 121-128, September.
    • Handle: RePEc:eee:proeco:v:127:y:2010:i:1:p:121-128
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    References listed on IDEAS

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    7. Tasgetiren, M. Fatih & Liang, Yun-Chia & Sevkli, Mehmet & Gencyilmaz, Gunes, 2007. "A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1930-1947, March.
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    Citations

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    Cited by:

    1. Che, Ada & Chabrol, Michelle & Gourgand, Michel & Wang, Yuan, 2012. "Scheduling multiple robots in a no-wait re-entrant robotic flowshop," International Journal of Production Economics, Elsevier, vol. 135(1), pages 199-208.
    2. Lee, Wen-Chiung & Chung, Yu-Hsiang, 2013. "Permutation flowshop scheduling to minimize the total tardiness with learning effects," International Journal of Production Economics, Elsevier, vol. 141(1), pages 327-334.
    3. Pan, Quan-Ke & Ruiz, Rubén, 2012. "Local search methods for the flowshop scheduling problem with flowtime minimization," European Journal of Operational Research, Elsevier, vol. 222(1), pages 31-43.
    4. Ławrynowicz Anna, 2011. "Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems," Foundations of Management, Sciendo, vol. 3(2), pages 7-26, January.
    5. Cheng, T.C.E. & Wu, Chin-Chia & Chen, Juei-Chao & Wu, Wen-Hsiang & Cheng, Shuenn-Ren, 2013. "Two-machine flowshop scheduling with a truncated learning function to minimize the makespan," International Journal of Production Economics, Elsevier, vol. 141(1), pages 79-86.
    6. Paz Perez-Gonzalez & Jose M. Framinan, 2018. "Single machine interfering jobs problem with flowtime objective," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 953-972, June.
    7. Wang, Sheng-yao & Wang, Ling & Liu, Min & Xu, Ye, 2013. "An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 145(1), pages 387-396.

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