alexa The Permutation Flow-Shop Scheduling Using a Genetic Algorithm-based Iterative Method
ISSN: 2169-0316

Industrial Engineering & Management
Open Access

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Research Article

The Permutation Flow-Shop Scheduling Using a Genetic Algorithm-based Iterative Method

Mahdi Eskenasi and Mehran Mehrandezh*

Department of Engineering and Applied Science, University of Regina, Canada

*Corresponding Author:
Mehrandezh M
Department of Engineering and Applied Science
University of Regina, Canada
Tel: 306-585-4658
Fax: 306-585-4855
E-mail: [email protected], [email protected]

Received Date: November 24, 2015; Accepted Date: June 24, 2016; Published Date:June 30, 2016

Citation: Eskenasi M, Mehrandezh M (2016) The Permutation Flow-Shop Scheduling Using a Genetic Algorithm-based Iterative Method. Ind Eng Manage 5:191. doi: 10.4172/2169-0316.1000191

Copyright: © 2016 Eskenasi M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




The objective is to investigate a well-known scheduling problem, namely the Permutation Flow-Shop Scheduling with the makespan as the objective function to be minimized. Various techniques, ranging from the simple constructive algorithms to the state-of-the-art techniques, such as Genetic Algorithms (GA), have been cited in the pertinent literature to solve this type of scheduling problem. A new GA-based solution methodology was developed and implemented. In this context, the performance of a stand-alone genetic algorithm (referred to as the non-hybrid genetic algorithm) and a novel hybridized genetic algorithm amalgamated with an iterative greedy algorithm were studied. The parameters of the hybrid and the non-hybrid genetic algorithms were tuned using a Full Factorial Experimental Design and Analysis of Variance. The performance of the properly tuned hybridized GA-based algorithm was examined on the existing standard benchmark problems of Taillard and it was shown that the proposed hybridized genetic algorithm performs very well on the benchmark problems.


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