Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 3 October 2017 / Approved: 3 October 2017 / Online: 3 October 2017 (08:29:45 CEST)

How to cite: Ma, Y.; Qiao, F.; Zhao, F.; Sutherland, J.W. Dynamic Scheduling of a Semiconductor Production Line Based on Composite Rule Set. Preprints 2017, 2017100013. https://doi.org/10.20944/preprints201710.0013.v1 Ma, Y.; Qiao, F.; Zhao, F.; Sutherland, J.W. Dynamic Scheduling of a Semiconductor Production Line Based on Composite Rule Set. Preprints 2017, 2017100013. https://doi.org/10.20944/preprints201710.0013.v1

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. Further, 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.

Keywords

Dynamic Scheduling; Semiconductor Manufacturing; Composite Rule Set; Support Vector Regression (SVR)

Subject

Engineering, Industrial and Manufacturing Engineering

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