Solving the Reentrant Permutation Flow-Shop Scheduling Problem with a Hybrid Genetic Algorithm

Authors

  • Jen-Shiang Chen Far East University
  • Jason Chao-Hsien Pan Takming University of Science and Technology
  • Chien-Min Lin National Taiwan University of Science and Technology

DOI:

https://doi.org/10.23055/ijietap.2009.16.1.201

Keywords:

Scheduling, genetic algorithm, hybrid genetic algorithm, reentrant permutation flow-shop.

Abstract

Most production scheduling-related research assumes that a job visits certain machines once at most, but this is often untrue in practical situations. A reentrant permutation flow-shop (RPFS) describes situations in which a job must be processed on machines in M1, M2, …, Mm, M1, M2, …, Mm, …, and M1, M2, …, Mm order and no job is allowed to pass a previous job. This study minimizes makespan by using the genetic algorithm to move from local optimal solutions to near-optimal solutions for RPFS scheduling problems. In addition, the hybrid genetic algorithm (HGA) improves the genetic algorithm’s performance in solving RPFS.

Author Biographies

Jen-Shiang Chen, Far East University

J

Jason Chao-Hsien Pan, Takming University of Science and Technology

Jason Chao-Hsien Pan is a Professor at the Department of Business Administration, Takming University of Science and Technology. He completed his Ph.D. degree in Industrial Engineering from the University of Houston. His current research interests include production scheduling and inventory management. His recent publications have appeared in European Journal of Operational Research, International Journal of Production Research, Journal of the Operational Research Society, Computers & Operations Research, International Journal of Systems Science, Production Planning & Control, and others.

Chien-Min Lin, National Taiwan University of Science and Technology

Chien-Min Lin completed his master degree in Industrial Management from the National Taiwan University of Science and Technology. His research interest is production scheduling.

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Published

2022-03-14

How to Cite

Chen, J.-S., Pan, J. C.-H., & Lin, C.-M. (2022). Solving the Reentrant Permutation Flow-Shop Scheduling Problem with a Hybrid Genetic Algorithm. International Journal of Industrial Engineering: Theory, Applications and Practice, 16(1), 23–31. https://doi.org/10.23055/ijietap.2009.16.1.201

Issue

Vol. 16 No. 1 (2009)

Section

Production Planning and Control

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