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An artificial neural network optimized by a genetic algorithm for real-time flow-shop scheduling

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3 Author(s)
Abe, M. ; Graduate Sch. of Sci. & Technol., Tokyo Inst. of Technol., Japan ; Matsumoto, M. ; Kuroda, C.

A job-shop scheduling method using a three-layered neural network optimized by a genetic algorithm, which is called a GANN (genetic algorithm neural net) scheduling method, is a flexible and practical quasi-optimal scheduling method. However, further improvements of the present GANN scheduling system are required for rapid flow-shop rescheduling in chemical processes for multi-purpose production. In this study, we investigated the effect of improvements to the GANN scheduling system on the efficiency of rescheduling when new jobs were appended in a chemical process with some buffer tanks. The results showed that the former GANN scheduling method could be developed into a practical real-time scheduling system for process problems

Published in:

Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on  (Volume:1 )

Date of Conference:

2000