The metaheuristics to solve the Flow-shop Scheduling Problem: A Comparative Study

Authors

  • Abdelhamid BOUZIDI
  • Moahmmed Essaid RIFF
  • Mohammed BARKATOU

Keywords:

computational intelligent, Flow shop scheduling problem, genetic, cat, particle, swarm optimization, metaheuristic.

Abstract

In our life, there are multiple real problems based on the Flow shop-scheduling problem, which is a NP-hard combinatorial optimization problem. Many researchers had tried to solve it by using the computational intelligence, such as the metaheuristics and the exact methods. Hence, the problem consists on determining the efficient method among them to solve this theoretical problem. This paper aims describes an experimental comparison study of four metaheuristics that are the hybrid genetic algorithm, particle swarm optimization (by and without using local search), and the cat swarm optimization algorithm. In order to analyze their performance in term of solution, the four algorithms has been applied to some benchmark Flow shop scheduling problem. The results show that the Cat swarm optimization algorithm is more efficient than the other selected methods to solve the flow shop-scheduling problem; and then the best one to solve the real application based on this theoretical optimization problem.

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Published

2016-01-01

How to Cite

Abdelhamid BOUZIDI, Moahmmed Essaid RIFF, & Mohammed BARKATOU. (2016). The metaheuristics to solve the Flow-shop Scheduling Problem: A Comparative Study. Journal of Network and Innovative Computing, 4, 9. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/102

Issue

Vol. 4 (2016)

Section

Original Article

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