Meta-heuristic algorithms for a clustering-based fuzzy bi-criteria hybrid flow shop scheduling problem

Abstract

This paper deals with hybrid flow shop scheduling problem with unrelated and eligible machines along with fuzzy processing times and fuzzy due dates. The objective is to minimize a linear combination of total completion time and maximum lateness of jobs. A mixed integer mathematical model is presented for the problem. The most challenging parts of hybrid evolutionary algorithms are determination of efficient strategies by which the whole search space is explored to perform local search around the promising search areas. In this study, a clustering-based approach as a data mining tool is introduced to identify the promising search areas. A repetitive clustering with an evolutionary algorithm is simultaneously employed to determine more promising parts of the solution space. Then, the searches in those parts are intensified with a local search. Here, two clustering-based meta-heuristic algorithms are applied to solve the problem, namely particle swarm optimization and genetic algorithm. The parameters are tuned by Taguchi experimental design, and various randomly generated test problems are used to evaluate the efficiency of the proposed algorithms.

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Correspondence to Hamed Fazlollahtabar.

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Golneshini, F.P., Fazlollahtabar, H. Meta-heuristic algorithms for a clustering-based fuzzy bi-criteria hybrid flow shop scheduling problem. Soft Comput 23, 12103–12122 (2019). https://doi.org/10.1007/s00500-019-03767-0

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Keywords

  • Hybrid flow shop (HFS)
  • Genetic algorithm (GA)
  • Particle swarm optimization (PSO)
  • Bi-criteria programming
  • Fuzzy scheduling
  • Clustering