Parametric solution algorithms for large-scale mixed-integer fractional programming problems and applications in process systems engineering

Zhixia Zhong, Fengqi You*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this work, we proposed novel parametric algorithms for solving large-scale mixed- integer linear and nonlinear fractional programming problems. By developing an equivalent parametric formulation of the general mixed-integer fractional program (MIFP), we propose exact parametric algorithms based on the root-finding methods for the global optimization of MIFPs. We also propose an inexact parametric algorithm that can potentially outperform the exact parametric algorithms for some types of MIFPs. Extensive computational studies are performed to demonstrate the efficiency of these parametric algorithms and to compare them with the general-purpose mixed-integer nonlinear programming methods. The applications of the proposed algorithms are illustrated through a case study on process scheduling and demonstrate the economic benefits of applying the proposed algorithms to practical application problems.

Original languageEnglish (US)
Pages (from-to)259-264
Number of pages6
JournalComputer Aided Chemical Engineering
Volume33
DOIs
StatePublished - Jan 1 2014

Keywords

  • Global optimization
  • MILFP
  • MIQFP
  • Production scheduling
  • Root-finding

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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