Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems

Authors

  • Rogério M. Branco Instituto Federal do Rio Grande do Sul – IFRS
  • Antônio S. Coelho Universidade Federal de Santa Catarina
  • Sérgio F. Mayerle Universidade Federal de Santa Catarina

DOI:

https://doi.org/10.4995/ijpme.2016.5780

Keywords:

genetic algorithms, dispatching rules, realistic job-shop scheduling

Abstract

This paper discusses the application of heuristic-based evolutionary technique in search for solutions concerning the dynamic job-shop scheduling problems with dependent setup times and alternate routes. With a combinatorial nature, these problems belong to an NP-hard class, with an aggravated condition when in realistic, dynamic and therefore, more complex cases than the traditional static ones. The proposed genetic algorithm executes two important functions: choose the routes using dispatching rules when forming each individual from a defined set of available machines and, also make the scheduling for each of these individuals created. The chromosome codifies a route, or the selected machines, and also an order to process the operations. In essence , each individual needs to be decoded by the scheduler to evaluate its time of completion, so the fitness function of the genetic algorithm, applying the modified Giffler and Thomson’s algorithm, obtains a scheduling of the selected routes in a given planning horizon. The scheduler considers the preparation time between operations on the machines and can manage operations exchange respecting the route and the order given by the chromosome. The best results in the evolutionary process are individuals with routes and processing orders optimized for this type of problema.

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Author Biographies

Antônio S. Coelho, Universidade Federal de Santa Catarina

Departamento de Engenharia de Produção

Sérgio F. Mayerle, Universidade Federal de Santa Catarina

Departamento de Engenharia de Produção

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Published

2016-07-13

How to Cite

Branco, R. M., Coelho, A. S., & Mayerle, S. F. (2016). Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems. International Journal of Production Management and Engineering, 4(2), 75–85. https://doi.org/10.4995/ijpme.2016.5780

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Section

Papers