A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory | Zendy

Toly Chen | Zendy

Open Access

A Nonlinear Programming and Artificial Neural Network Approach for Optimizing the Performance of a Job Dispatching Rule in a Wafer Fabrication Factory

Author(s) -

Toly Chen

Publication year - 2012

Publication title -

applied computational intelligence and soft computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.371

H-Index - 10

eISSN - 1687-9732

pISSN - 1687-9724

DOI - 10.1155/2012/471973

Subject(s) - wafer fabrication , computer science , artificial neural network , factory (object oriented programming) , nonlinear system , scheduling (production processes) , artificial intelligence , rule based system , nonlinear programming , job scheduler , wafer , machine learning , mathematical optimization , engineering , operating system , mathematics , cloud computing , physics , quantum mechanics , electrical engineering , programming language

A nonlinear programming and artificial neural network approach is presented in this study to optimize the performance of a job dispatching rule in a wafer fabrication factory. The proposed methodology fuses two existing rules and constructs a nonlinear programming model to choose the best values of parameters in the two rules by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several studies. In addition, a more effective approach is also applied to estimate the remaining cycle time of a job, which is empirically shown to be conducive to the scheduling performance. The efficacy of the proposed methodology was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future

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