A fuzzy neural network for the flow time estimation in a semiconductor manufacturing factory

Flow time assignment problem creates a great challenge to semiconductor manufacturing managers especially when a company is facing the competitive pressure from customer's requirements of quick response, on-time delivery and low cost. This paper presents a Neural-Fuzzy model for the flow time estimation using simulated data generated from a Foundry Service company located in Hsin-Chu science-based park of Taiwan. This Neural-Fuzzy model applies influential factors identified from the shop floor, i.e. order processing time, total work-in-process, and total jobs in system and utilization of bottleneck machines, to estimate the flow time of a new order. The fuzzy neural network is trained using a back-propagation algorithm to adjust the weight coefficients of the network and the parameters of the fuzzy membership functions. The trained network is then adopted to predict the flow time of each order generated from the simulated data. Model evaluation results for a simulated factory indicate that the Neural-Fuzzy model performs better than the Case-Based Reasoning method and a Multi-Layer Perceptrons Neural Network.