Multilayer optimization and scheduling using model predictive control: Application to reentrant semiconductor manufacturing lines

Felipe D. Vargas-Villamil, Daniel Rivera

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

A two-layer production control method applied to discrete event reentrant semiconductor manufacturing lines is investigated. A modified l1-norm predictive controller/optimizer is proposed as a coordinator in the highest layer and a distributed control policy is used as a follow-up controller in the lowest layer. The use of a model predictive control (MPC) formulation allows the scheduling algorithm to simultaneously solve the production optimization and in-process inventory control problems at each sampling time. As an optimizer, the scheduler maximizes production rate and, as a controller, it addresses variability. Using this control-oriented framework, an optimal trade-off between production rate and cycle time is obtained. An l1-norm cost function allows the implementation of the optimization layer as a mixed integer linear program (MILP) which is solved at each time shift. The approach is applied to a one-product six-step, five-machine reentrant discrete event semiconductor manufacturing line whose specifications were provided by Intel Corporation. (C) 2000 Elsevier Science Ltd.

Original languageEnglish (US)
Pages (from-to)2009-2021
Number of pages13
JournalComputers and Chemical Engineering
Volume24
Issue number8
DOIs
Publication statusPublished - Sep 1 2000

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Keywords

  • Discrete event system
  • Hierarchical structure
  • Model predictive control
  • Reentrant manufacturing line
  • Scheduling
  • Semiconductor fabrication facility

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Control and Systems Engineering

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