Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Acquisition and Refinement of State Dependent FMS Scheduling Knowledge Using Neural Network and Inductive Learning

인공신경망과 귀납학습을 이용한 상태 의존적 유연생산시스템 스케쥴링 지식의 획득과 정제

  • 김창욱 (고려대학교 정보통신기술공동연구소) ;
  • 민형식 (LG-EDS CIM사업부) ;
  • 이영해 (한양대학교 산업공학과)
  • Published : 1996.12.01
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Abstract

The objective of this research is to develop a knowledge acquisition and refinement method for a multi-objective and multi-decision FMS scheduling problem. A competitive neural network and an inductive learning algorithm are integrated to extract and refine necessary scheduling knowledge from simulation outputs. The obtained scheduling knowledge can assist the FMS operator in real-time to decide multiple decisions simultaneously, while maximally meeting multiple objective desired by the FMS operator. The acquired scheduling knowledge for an FMS scheduling problem is tested by comparing the desired and the simulated values of the multiple objectives. The result show that the knowledge acquisition and refinement method is effective for the multi-objective and multi-decision FMS scheduling problems.

Keywords

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