Solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics [An article from: European Journal of Operational Research]
Book Details
Author(s)R. Ruiz, C. Maroto, J. Alcaraz
PublisherElsevier
ISBN / ASINB000RR65PU
ISBN-13978B000RR65P9
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from European Journal of Operational Research, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
This paper deals with the permutation flowshop scheduling problem in which there are sequence dependent setup times on each machine, commonly known as the SDST flowshop. The optimisation criteria considered is the minimisation of the makespan or C"m"a"x. Genetic algorithms have been successfully applied to regular flowshops before, and the objective of this paper is to assess their effectiveness in a more realistic and complex environment. We present two advanced genetic algorithms as well as several adaptations of existing advanced metaheuristics that have shown superior performance when applied to regular flowshops. We show a calibration of the genetic algorithm's parameters and operators by means of a Design of Experiments (DOE) approach. For evaluating the proposed algorithms, we have coded several, if not all, known SDST flowshop specific algorithms. All methods are tested against an augmented benchmark based on the instances of Taillard. The results show a clear superiority of the algorithms proposed, especially for the genetic algorithms, regardless of instance type and size.
Description:
This paper deals with the permutation flowshop scheduling problem in which there are sequence dependent setup times on each machine, commonly known as the SDST flowshop. The optimisation criteria considered is the minimisation of the makespan or C"m"a"x. Genetic algorithms have been successfully applied to regular flowshops before, and the objective of this paper is to assess their effectiveness in a more realistic and complex environment. We present two advanced genetic algorithms as well as several adaptations of existing advanced metaheuristics that have shown superior performance when applied to regular flowshops. We show a calibration of the genetic algorithm's parameters and operators by means of a Design of Experiments (DOE) approach. For evaluating the proposed algorithms, we have coded several, if not all, known SDST flowshop specific algorithms. All methods are tested against an augmented benchmark based on the instances of Taillard. The results show a clear superiority of the algorithms proposed, especially for the genetic algorithms, regardless of instance type and size.
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