Heuristics for job shop scheduling with volatile machine constraints

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

An alteration of the job shop scheduling problem, concerning advertisement scheduling on digital advertisement spaces, is presented. Dispatching Rules (DR), Iterated Local Search (ILS) and Genetic Algorithms (GA) are discussed and applied to the problem space. The results show that ILS is the best performing heuristic, and surpasses the other heuristics especially in large problem spaces (≥ 100 machines, ≥ 100 jobs). The results match previously made findings, which indicates that effects on large-scale problems should be further researched in conjunction with amalgam algorithms between DR, GA and ILS.

Original languageEnglish
Title of host publicationProceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
EditorsBing Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages389-393
Number of pages5
ISBN (Electronic)9781467396127
DOIs
Publication statusPublished - 28 Feb 2017
Event2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016 - Xi'an, China
Duration: 3 Oct 20165 Oct 2016

Publication series

NameProceedings of 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016

Conference

Conference2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016
CountryChina
CityXi'an
Period03.10.201605.10.2016

Keywords

  • Distribution rules
  • Genetic algorithms
  • Job shop
  • Local search
  • Scheduling

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