A reformulation-linearization method for the global optimization of large-scale mixed-integer linear fractional programming problems and cyclic scheduling application

Dajun Yue, Fengqi You

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Global optimization of large-scale mixed-integer linear fractional programs (MILFPs) could be computationally intractable due to the presence of discrete variables and the pseudoconvex/pseudoconcave objective function. In this paper, we propose a novel and efficient reformulation-linearization method, which integrates the Charnes-Cooper transformation and the Glover's linearization scheme, to transform general MILFPs into their equivalent mixed-integer linear programs (MILP), allowing MILFPs to be globally optimized effectively with MILP methods. A case study on the cyclic scheduling of multipurpose batch plant is demonstrated to illustrate the efficiency of this method. Computational results show that the proposed approach requires significantly shorter CPU times than various general-purpose MINLP methods and is comparable with the tailored Dinkelbach's algorithm for solving large-scale MILFP problems.

Original languageEnglish (US)
Title of host publication2013 American Control Conference, ACC 2013
Pages3985-3990
Number of pages6
StatePublished - Sep 11 2013
Event2013 1st American Control Conference, ACC 2013 - Washington, DC, United States
Duration: Jun 17 2013Jun 19 2013

Other

Other2013 1st American Control Conference, ACC 2013
CountryUnited States
CityWashington, DC
Period6/17/136/19/13

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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