Cat swarm optimization for solving the open shop scheduling problem

Abdelhamid Bouzidi,Mohammed Barkatou,Mohammed Essaid Riffi

doi:10.1007/s40092-018-0297-z

Abdelhamid Bouzidi, Mohammed Barkatou + Show 1 more

Open Access

https://doi.org/10.1007/s40092-018-0297-z

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Abstract

This paper aims to prove the efficiency of an adapted computationally intelligence-based behavior of cats called the cat swarm optimization algorithm, that solves the open shop scheduling problem, classified as NP-hard which its importance appears in several industrial and manufacturing applications. The cat swarm optimization algorithm was applied to solve some benchmark instances from the literature. The computational results, and the comparison of the relative percentage deviation of the proposed metaheuristic with other’s existing in the literature, show that the cat swarm optimization algorithm yields good results in reasonable execution time.

Highlights

To solve real optimization problems such as in industrial and manufacturing applications, the problem should be formulated as a theoretical problem
The open shop scheduling problem (OSSP) problem is classified as NP-hard (Gonzalez and Sahni 1976), that is why some researchers had tried to solve it by introducing some methods, such as exact methods, polynomial time algorithm proposed by Gonzalez and Sahni (1976), and the branch and bound developed by Brucker et al (1997)
Some metaheuristics are used to solve the OSSP problem, such as simulated annealing (Liaw 1999a) and Tabu search algorithm proposed by Liaw (1999b), genetic algorithm proposed by Prins (2000), extended genetic algorithm proposed by Rahmani Hosseinabadi et al (2018), hybrid ant colony optimization proposed by Blum (2005), bee colony optimization proposed by Huang and Lin (2011), particle swarm optimization proposed by Sha and Hsu (2008)

Summary

Introduction

To solve real optimization problems such as in industrial and manufacturing applications, the problem should be formulated as a theoretical problem. There are several real-world applications of the OSSP, such as system-on-a-chip testing (Iyengar and Chakrabarty 2002), the area of satellite-switched time-division multiple access (Dell’Amico and Martello 1996), routing packets (Suel 1995), the scheduling and wavelength assignment problem in optical networks that are based on the wavelength-division-multiplexing technology (Bampis and Rouskas 2002), routing in optical transpose interconnect system (Lucas 2010), in routing in. LAROSERI, Department of Computer Science, Faculty of Sciences, Chouaib Doukkali University, EL Jadida, Morocco. Innovation in Science, Technology and Modeling (ISTM), Faculty of Sciences, Chouaib Doukkali University, EL Jadida, Morocco heterogeneous networks to model communications schedules (Bhat et al 2000). Some metaheuristics are used to solve the OSSP problem, such as simulated annealing (Liaw 1999a) and Tabu search algorithm proposed by Liaw (1999b), genetic algorithm proposed by Prins (2000), extended genetic algorithm proposed by Rahmani Hosseinabadi et al (2018), hybrid ant colony optimization proposed by Blum (2005), bee colony optimization proposed by Huang and Lin (2011), particle swarm optimization proposed by Sha and Hsu (2008)

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