Integration of Artificial Neural Network and Genetic Algorithm for Production Scheduling with Transportation Time

Abstract

Production scheduling by the integration of genetic algorithm (GA) and artificial neural network (ANN) in computer integrated manufacturing system is studied in this thesis where the transportation time of overhead hoist transporter (OHT) is considered for optimal dispatching The OHT transportation time varies from complicated traffic constraints that can only be obtained by simulation Instead of the time-consuming simulation by common software the transportation time of different machine dispatching is first estimated by an ANN model GA is then integrated to validate the scheduling of minimal makespan and maximal production output Numerical verifications show that the estimated transportation time paves the way for production scheduling in engineering applications The proposed model integrating GA with ANN makes the optimal scheduling of OHT system become possible

Date of Award	2014 Jun 26
Original language	English
Supervisor	Shih-Ming Yang (Supervisor)