Abstract
In this study, the fuzzy-neural ensemble and geometric rule fusion approach is presented to optimize the performance of job dispatching in a wafer fabrication factory with an intelligent rule. The proposed methodology is a modification of a previous study by fusing two dispatching rules and diversifying the job slacks in novel ways. To this end, the geometric mean of the neighboring distances of slacks is maximized. In addition, the fuzzy c-means (FCM) and backpropagation network (BPN) ensemble approach was also proposed to estimate the remaining cycle time of a job, which is an important input to the new rule. A new aggregation mechanism was also designed to enhance the robustness of the FCM-BPN ensemble approach. To validate the effectiveness of the proposed methodology, some experiments have been conducted. The experimental results did support the effectiveness of the proposed methodology.
Highlights
The scheduling of complex manufacturing systems is usually a NP-hard problem, which means it is very difficult for the production controller to find the best schedule within a reasonable period of time
In order to solve some of these problems and to further improve the performance of job scheduling in a wafer fabrication factory, Chen’s approach has been modified, and a fuzzyneural-ensemble and geometric rule fusion approach was proposed in this study
Chen [30] applied the backpropagation network (BPN) of all clusters to estimate the cycle time of a job and used a BPN to aggregate these estimates
Summary
The scheduling of complex manufacturing systems is usually a NP-hard problem (see Table 1), which means it is very difficult for the production controller to find the best schedule within a reasonable period of time. Hu et al [16] divided the process flow into several stages and protected the bottleneck step at each reentrant stage from the system fluctuations These dispatching rules are relatively easier to use, they cannot produce optimal or nearoptimal scheduling results. Harrath et al [7] proposed a hybrid genetic algorithm (GA) and data mining approach to determine the optimal scheduling plan of a job shop, in which GA was used to generate a learning population of good solutions. These good solutions were mined to find out some decision rules that could be transformed into a metaheuristic. (1) A more effective approach to optimize the parameter values is needed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
