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IJAT Vol.4 No.2 pp. 169-177
doi: 10.20965/ijat.2010.p0169
(2010)

Paper:

Dynamic Controls of Genetic Algorithm Scheduling in Supply Chain

Jia Yee Chai*, Tatsuhiko Sakaguchi**, and Keiichi Shirase*

*Graduate School of Engineering, Kobe University, 1-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan

**Toyohashi University of Technology, 1-1 Hibarigaoka, Tempaku-cho, Toyohashi-shi, Aichi-ken 441-8580, Japan

Received:
October 23, 2009
Accepted:
December 22, 2009
Published:
March 5, 2010
Keywords:
autonomous negotiation, supply chain, multiagents, scheduling, genetic algorithm
Abstract

In a supply chain environment, reactive scheduling process has to deal with both operational objective (minimizing total tardiness) and business objective (accommodating a new order into the current schedule). Genetic algorithm (GA) optimization has been applied in such reactive scheduling problem for job shop style manufacturer in our previous researches. An algorithm is introduced to dynamically adjust the objective function of GA optimization to minimize cost of delay penalties and to maximize the number of contracts captured by the manufacturer. The effectiveness of the proposed model is demonstrated by computational experiments.

Cite this article as:
J. Chai, T. Sakaguchi, and K. Shirase, “Dynamic Controls of Genetic Algorithm Scheduling in Supply Chain,” Int. J. Automation Technol., Vol.4, No.2, pp. 169-177, 2010.
Data files:
References
  1. [1] Y. Tanimizu, Y. Maeda, C. Ozawa, and N. Sugimura, “A study on dynamic supply chain considering production schedules – parallel scheduling for orders,” Proc. of 51st ISCIE, pp. 83-84, 2007 (in Japanese).
  2. [2] T. Kaihara, “Multi-agent based supply chain modeling with dynamic environment,” International Journal of Production Economics 85, pp. 263-269, 2003.
  3. [3] H. L. Young, S. J. Chan, and M. Chiung, “Advanced planning and scheduling with outsourcing in manufacturing supply chain,” Computers & Industrial Engineering 43, pp. 351-374, 2003.
  4. [4] S. H. Zegordi et at., “A novel genetic algorithm for solving production and transportation scheduling in a twostage supply chain,” Computers & Industrial Engineering, doi:10.1016/j.cie.2009.06.012, 2009.
  5. [5] J. Y. Chai, T. Sakaguchi, and K. Shirase, “Penalty Distribution Method for Scheduling Based Supply Chain Management,” The 41st CIRP Conference on Manufacturing System, pp. 261-265, 2009.
  6. [6] J. Y. Chai, T. Sakaguchi, K. Shirase, “Reactive scheduling based multi objectives negotiation for dynamic supply chain model,” Proceeding of Leading Edge Manufacturing in 21st Century, Kyushu, Japan, pp. 655-660, 2007.
  7. [7] T. Sakaguchi, Y. Tanimizu, T. Miyamae, Y. Maeda, K. Shirase, and N. Sugimura, “Improvement of crossover operator in genetic algorithm for reactive scheduling,” Proc. of the 3rd International Conference on Leading Edge Manufacturing in 21st century, pp. 427-432, 2005.

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Last updated on Nov. 18, 2019