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Evolutionary Clustering Search for Flowtime Minimization in Permutation Flow Shop

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 4771)

Abstract

This paper deals with the Permutation Flow Shop scheduling problem with the objective of minimizing total flow time, and therefore reducing in-process inventory. A new hybrid metaheuristic Genetic Algorithm - Cluster Search is proposed for the scheduling problem solution. The performance of the proposed method is evaluated and results are compared with the best reported in the literature. Experimental tests show the new method superiority for the test problems set, regarding the solution quality.

Keywords

  • Schedule Problem
  • Local Search
  • Cluster Center
  • Particle Swarm Optimization Algorithm
  • Flow Shop

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-540-75514-2_6
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Filho, G.R., Nagano, M.S., Lorena, L.A.N. (2007). Evolutionary Clustering Search for Flowtime Minimization in Permutation Flow Shop. In: , et al. Hybrid Metaheuristics. HM 2007. Lecture Notes in Computer Science, vol 4771. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75514-2_6

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  • DOI: https://doi.org/10.1007/978-3-540-75514-2_6

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