A neuro-fuzzy model for a new hybrid integrated Process Planning and Scheduling system

• Generating efficient production plans as the production constraints change.
• A new hybrid heuristic model combining both GA and Fuzzy Neural Network.
• Improving GA performance by using a clustered chromosome structure.
• Fuzzy neural networks in re-planning module is able to produce accurate predictions.
• Process plans and schedules are generated separately(no need to reorganize factory).

In customized mass production, isolation of Process Planning (PP) and Scheduling stages has a critical effect on the efficiency of production. In this study, to overcome this isolation problem, we propose an integrated system that does PP and Scheduling in parallel and responds to fluctuations in job floor on time. One common problem observed in integration models is the increase in computational time in conjunction with the increase of problem size. Therefore in this study, we use a hybrid heuristic model combining both Genetic Algorithm (GA) and Fuzzy Neural Network (FNN). To improve GA performance and increase the efficiency of searching, we use a clustered chromosome structure and test the performance of GA with respect to different scenarios. Data provided by GA is used in constructing an FNN model that instantly provides new schedules as new constraints emerge in the production environment. Introduction of fuzzy membership functions in Artificial Neural Network (ANN) model allows us to generate fuzzy rules for production environment.