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IJIET 2015 Vol.5(7): 549-552 ISSN: 2010-3689
DOI: 10.7763/IJIET.2015.V5.566

Performance Analyzes of Bee Colony Split-Plot Algorithm

Trifenaus Prabu Hidayat and Andre Sugioko

Abstract—The Bee Colony Algorithms is a popular algorithm in 2006, in which this algorithm was an approach to solve problems using bee’s behavior. The Bee Colony Algorithm has a quite simple flow, therefore, many studies have carry out modifications for specific problems. This study will modify the Bee Colony Algorithm to become more resembled with the Bee’s behavior, using the split-plot design principle.
This study is aiming to look at the performance of the Bee Colony split-plot Algorithm based on the bee’s group behavior. In this study the performance of the Bee Colony algorithm is tested using the case study of the flow shop scheduling with the due date of 3 (three) cases, aiming to minimize the amount of late jobs. This test will be compared with the genetic algorithm. The performance of some groups and the computing time of the Bee Colony’s Algorithm split-plot will be analyzed using the flow shop problem with the aim to minimize the makespan. The study finding has shown that the Bee Colony Algorithm split-plot as proposed has resulted in a performance that resembles the genetic algorithm for the second and the third cases, whereas for the first case, the algorithm bee colony has a better performance with an average of 2,4615 late jobs and for the genetic 2,615 jobs.

Index Terms—Bee colony, flow shop, scheduling, split-plot.

The authors are with the Department of Industrial Technology, Atma Jaya Chatolic University of Indonesia, Jakarta 12930, Indonesia (e-mail: hidayat_tp@yahoo.com, andresugioko@yahoo.com).


Cite: Trifenaus Prabu Hidayat and Andre Sugioko, "Performance Analyzes of Bee Colony Split-Plot Algorithm," International Journal of Information and Education Technology vol. 5, no. 7, pp. 549-552, 2015.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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