Enhanced linear reformulation for engineering optimization models with discrete and bounded continuous variables

Qi An, Shu Cherng Fang, Han-Lin Li, Tiantian Nie*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

In this paper, we significantly extend the applicability of state-of-the-art ELDP (equations for linearizing discrete product terms) method by providing a new linearization to handle more complicated non-linear terms involving both of discrete and bounded continuous variables. A general class of “representable programming problems” is formally proposed for a much wider range of engineering applications. Moreover, by exploiting the logarithmic feature embedded in the discrete structure, we present an enhanced linear reformulation model which requires half an order fewer equations than the original ELDP. Computational experiments on various engineering design problems support the superior computational efficiency of the proposed linearization reformulation in solving engineering optimization problems with discrete and bounded continuous variables.

Original languageEnglish
Pages (from-to)140-157
Number of pages18
JournalApplied Mathematical Modelling
Volume58
DOIs
StatePublished - 1 Jun 2018

Keywords

  • Linear reformulation
  • Nonlinear discrete optimization
  • Polynomial programming
  • Signomial programming

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