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

摘要

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.