Tightening discretization-based MILP models for the pooling problem using upper bounds on bilinear terms

Yifu Chen, Christos T. Maravelias, Xiaomin Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Discretization-based methods have been proposed for solving nonconvex optimization problems with bilinear terms such as the pooling problem. These methods convert the original nonconvex optimization problems into mixed-integer linear programs (MILPs). In this paper we study tightening methods for these MILP models for the pooling problem, and derive valid constraints using upper bounds on bilinear terms. Computational results demonstrate the effectiveness of our methods in terms of reducing solution time.

Original languageEnglish (US)
Pages (from-to)215-234
Number of pages20
JournalOptimization Letters
Volume18
Issue number1
DOIs
StatePublished - Jan 2024

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting (miscellaneous)
  • Control and Optimization

Keywords

  • Binary expansion
  • Nonconvex optimization
  • Valid constraints

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