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Appl. Sci. 2017, 7(10), 1052; https://doi.org/10.3390/app7101052

Dynamic Scheduling of a Semiconductor Production Line Based on a Composite Rule Set

1
School of Electronical & Information Engineering, Tongji University, Shanghai 201804, China
2
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
3
Environmental and Ecological Engineering, Purdue University, West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
Received: 6 September 2017 / Accepted: 10 October 2017 / Published: 13 October 2017
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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Abstract

Various factors and constraints should be considered when developing a manufacturing production schedule, and such a schedule is often based on rules. This paper develops a composite dispatching rule based on heuristic rules that comprehensively consider various factors in a semiconductor production line. The composite rule is obtained by exploring various states of a semiconductor production line (machine status, queue size, etc.), where such indicators as makespan and equipment efficiency are used to judge performance. A model of the response surface, as a function of key variables, is then developed to find the optimized parameters of a composite rule for various production states. Furthermore, dynamic scheduling of semiconductor manufacturing is studied based on support vector regression (SVR). This approach dynamically obtains a composite dispatching rule (i.e., parameters of the composite dispatching rule) that can be used to optimize production performance according to real-time production line state. Following optimization, the proposed dynamic scheduling approach is tested in a real semiconductor production line to validate the effectiveness of the proposed composite rule set. View Full-Text
Keywords: dynamic scheduling; semiconductor manufacturing; composite rule set; support vector regression (SVR) dynamic scheduling; semiconductor manufacturing; composite rule set; support vector regression (SVR)
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Ma, Y.; Qiao, F.; Zhao, F.; Sutherland, J.W. Dynamic Scheduling of a Semiconductor Production Line Based on a Composite Rule Set. Appl. Sci. 2017, 7, 1052.

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