Feature selection approach for evolving reactive scheduling policies for dynamic job shop scheduling problem using gene expression programming

Abstract

Dispatching rules are one of the most widely applied methods for solving Dynamic Job Shop Scheduling problems (DJSSP) in real-world manufacturing systems. Hence, the automated design of effective rules has been an important subject in the scheduling literature for the past several years. High computational requirements and difficulty in interpreting generated rules are limitations of literature methods. Also, feature selection approaches in the field of automated design of scheduling policies have been developed for the tree-based GP approach only. Therefore, the aim of this study is to propose a feature selection approach for the Gene Expression Programming (GEP) algorithm to evolve high-quality rules in simple structures with an affordable computational budget. This integration speeds up the search process by restricting the GP search space using the linear representation of the GEP algorithm and creates concise rules with only meaningful features using the feature selection approach. The proposed algorithm is compared with five algorithms and 30 rules from the literature under different processing conditions. Three performance measures are considered including total weighted tardiness, mean tardiness, and mean flow time. The results show that the proposed algorithm can generate smaller rules with high interpretability in a much shorter training time.

KEYWORDS:

• Feature selection
• gene expression programming
• dispatching rules
• genetic programming
• dynamic job shop scheduling
• discrete event simulation

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Salama Shady

Salama Shady received a master’s degree in industrial engineering and systems management from the Egypt Japan University of Science and Technology in 2018. He is currently a PhD student at the graduate school of system informatics, Kobe University, Japan. He is also a research associate at the Research Institute for Economics and Business Administration at Kobe University. His research interests include production planning and optimization, evolutionary computation, and simulation.

Toshiya Kaihara

Toshiya Kaihara is a Professor of Graduate School of System Informatics and a Director of Value Creation Smart Production Centre at Kobe University, Japan. He received his B.E. and M.E. degrees from Kyoto University, Japan, and his Ph.D. degree from Imperial College London, UK. He is an author of more than 250 publications of journal papers and books. He is a fellow member of JSME, IEEJ, and CIRP (The International Academy of Production Engineering), and a member of IFAC, IFIP, IEEE, ASME, EAJ, SICE, ISCIE, and many others. His research interests include systems optimisation theory, and its application into production, logistics, and social systems.

Nobutada Fujii

Nobutada Fujii is an Associate Professor of Graduate School of System Informatics, Kobe University, Japan. He received his bachelor and master degrees in engineering from Kobe University, Japan in 1996 and 1998, and Ph.D. in engineering from the University of Tokyo, Japan in 2004, respectively. His major research topics are about Autonomous and Decentralized Manufacturing Systems, Agent-based Modeling and Simulation of Social Systems, and Service Engineering.

Daisuke Kokuryo

Daisuke Kokuryo received a Ph. D. degree in engineering from Kobe University in 2007. He is currently an assistant professor at Graduate School of System Informatics, Kobe University, Japan. His research interests include mathematical optimization for medical systems, biomedical engineering and manufacturing systems.