Title: A new hybrid genetic algorithm for job shop scheduling problem

Authors: Marjan Kuchaki Rafsanjani; Milad Riyahi

Addresses: Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran ' Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract: Job shop scheduling problem is an NP-hard problem. This paper proposes a new hybrid genetic algorithm to solve the problem in an appropriate way. In this paper, a new selection criterion to tackle premature convergence problem is introduced. To make full use of the problem itself, a new crossover based on the machines is designed. Furthermore, a new local search is designed which can improve the local search ability of proposed GA. This new approach is run on the some problems and computer simulation shows the effectiveness of the proposed approach.

Keywords: job shop scheduling problem; JSSP; genetic algorithm; selection operator; crossover operator; local search.

DOI: 10.1504/IJAIP.2020.107012

International Journal of Advanced Intelligence Paradigms, 2020 Vol.16 No.2, pp.157 - 171

Received: 28 Jun 2016
Accepted: 24 Oct 2016
Published online: 01 May 2020 *

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