Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem

• Published in: International journal of advanced manufacturing technology Vol. 64; no. 5-8; pp. 915 - 932
• Main Authors: Rahmati, Seyed Habib A., Zandieh, M., Yazdani, M.
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.02.2013; Springer Nature B.V
• Subjects: CAE) and Design; Classification; Computer-Aided Engineering (CAD; Engineering; Evolutionary algorithms; Genetic algorithms; Heuristic methods; HyperText Markup Language; Industrial and Production Engineering; Job shop scheduling; Job shops; Mechanical Engineering; Media Management; Multiple criterion; Multiple objective analysis; Original Article; Production scheduling; Servers; Sorting algorithms; Statistical tests; Workloads; Scheduling; Multiobjective evolutionary algorithm; Non-dominated sorting genetic algorithm; Flexible job shop; Non-dominated ranking genetic algorithm
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-012-4051-1

The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm.