A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy adaptive scheduling and control systems
Fuzzy Sets and Systems
Using data mining to find patterns in genetic algorithm solutions to a job shop schedule
Computers and Industrial Engineering
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
A multi-criteria approach for scheduling semiconductor wafer fabrication facilities
Journal of Scheduling
Predicting Wafer-Lot Output Time With a Hybrid FCM–FBPN Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A simulation-based two-stage scheduling methodology for controlling semiconductor wafer fabs
Expert Systems with Applications: An International Journal
An Integrated Project Management System for Facilitating Knowledge Learning
International Journal of Enterprise Information Systems
| Hi-index | 12.05 |
A self-adaptive agent-based fuzzy-neural system is constructed in this study to enhance the performance of scheduling jobs in a wafer fabrication factory. The system integrates dispatching, performance evaluation and reporting, and scheduling policy optimization. Unlike in the past studies a single pre-determined scheduling algorithm is used for all agents, in this study every agent develops and modifies its own scheduling algorithm to adapt it to the local conditions. To stabilize the performance of the self-adaptive agent-based fuzzy-neural scheduling system, some treatments have also been taken. To evaluate the effectiveness of the proposed methodology and to make comparison with some existing approaches, production simulation is also applied in this study to generate some test data. According to experimental results, the self-adaptive agent-based fuzzy-neural system did improve the performance of scheduling jobs in the simulated wafer fabrication factory, especially with respect to the average cycle time and cycle time standard deviation.