A resource constraint approach for one global constraint MINLP

Pavlo Muts, Ivo Nowak, Eligius M.T. Hendrix*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

Abstract

Many industrial optimization problems are sparse and can be formulated as block-separable mixed-integer nonlinear programming (MINLP) problems, where low-dimensional sub-problems are linked by a (linear) knapsack-like coupling constraint. This paper investigates exploiting this structure using decomposition and a resource constraint formulation of the problem. The idea is that one outer approximation master problem handles sub-problems that can be solved in parallel. The steps of the algorithm are illustrated with numerical examples which shows that convergence to the optimal solution requires a few steps of solving sub-problems in lower dimension.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2020 - 20th International Conference, Proceedings
EditorsOsvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blecic, David Taniar, Bernady O. Apduhan, Ana Maria A.C. Rocha, Eufemia Tarantino, Carmelo Maria Torre, Yeliz Karaca
Place of PublicationCham
PublisherSpringer
Pages590-605
Number of pages16
ISBN (Print)9783030588076
DOIs
Publication statusPublished - 29 Sept 2020
Externally publishedYes
Event20th International Conference on Computational Science and Its Applications, ICCSA 2020 - Cagliari, Italy
Duration: 1 Jul 20204 Jul 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12251 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference/symposium

Conference/symposium20th International Conference on Computational Science and Its Applications, ICCSA 2020
Country/TerritoryItaly
CityCagliari
Period1/07/204/07/20

Keywords

  • Column generation
  • Decomposition
  • Global optimization
  • Mixed-integer nonlinear programming
  • Parallel computing

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