International Journal of Industrial Engineering and Management

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Vol. 3 No. 3 (2012)
Original Research Article

A Genetic Algorithm Approach for Minimizing Total Tardiness in Single Machine Scheduling

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  • Gürsel A. Süer,
  • Xiaozhe Yang,
  • Omar I. Alhawari,
  • Joel Santos,
  • Ramon Vazquez
Gürsel A. Süer
Industrial and Systems Engineering, Ohio University
Bio
Xiaozhe Yang
Industrial and Systems Engineering, Ohio University
Bio
Omar I. Alhawari
Industrial and Systems Engineering, Ohio University
Bio
Joel Santos
Electrical and Computer Engineering, University of Puerto Rico
Bio
Ramon Vazquez
Electrical and Computer Engineering, University of Puerto Rico
Bio
DOI: https://doi.org/10.24867/IJIEM-2012-3-120

Published 2012-09-30

abstract views: 33 // FULL TEXT ARTICLE (PDF): 0


Keywords

  • Single Machine Scheduling,
  • Tardiness,
  • Genetic Algorithms,
  • Mathematical Modeling

How to Cite

Süer, G. A., Yang, X., Alhawari, O. I., Santos, J., & Vazquez, R. (2012). A Genetic Algorithm Approach for Minimizing Total Tardiness in Single Machine Scheduling. International Journal of Industrial Engineering and Management, 3(3), 163–171. https://doi.org/10.24867/IJIEM-2012-3-120

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Creative Commons License

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Abstract

Minimizing total tardiness in single machine scheduling is known as NP-hard. In this paper, the problem is extended to include non-zero ready times and the preemption of jobs is not allowed. First, a mathematical model is developed. Due to computational complexities with the mathematical model, a Genetic Algorithm approach is also proposed and later its performance is compared with optimal solutions. The results show that GA can find optimal solution for small problems and near optimal solutions for large problems. The results also show that among Delay-only, Non-delay-only, and Random strategies, Non-delay strategy produced more robust solutions whereas random strategy found the optimal solution in smaller problem categories.

 

Article history: Received (9 Jul 2012); Revised (22 September 2012); Accepted (30 September 2012)  

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FULL TEXT ARTICLE (PDF)