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

• Main Authors: Shih-YangHuang, 黃士洋
• Other Authors: Shih-Ming Yang
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/64692206836980210281

碩士 === 國立成功大學 === 航空太空工程學系 === 102 === 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.