Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Genetic algorithms for hybrid job-shop problems with minimizing the makespan and mean flow time
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. (Transportation)
United Institute Of Informatics Problems, BLR.
Otto-von-Guericke-Universität, GER.
(English)In: Article in journal (Refereed) Accepted
Abstract [en]

We address a generalization of the classical job-shop problem which is called a hybrid job-shop problem. The criteria under consideration are the minimization of the makespan and mean ow time. In the hybrid job-shop, machines of type k are available for processing the specific subset O^k of the given operations. Each set O^k maybe partitioned into subsets for their processing on the machines of type k. Solving the hybrid job-shopproblem implies the solution of two subproblems: an assignment of all operations fromthe set O^k to the machines of type k and nding optimal sequences of the operationsfor their processing on each machine. In this paper, a genetic algorithm is developedto solve these two subproblems simultaneously. For solving the subproblems, a specialchromosome is used in the genetic algorithm based on a mixed graph model. We com-pare our genetic algorithms with a branch and bound algorithms and three other recentheuristic algorithms from the literature. Computational results for benchmark instanceswith 10 jobs and up to 50 machines show that the proposed genetic algorithm is ratherecient for both criteria. Compared with the other heuristics, the new algorithm givesmost often an optimal solution and the average percentage deviation from the optimalfunction value is about 4 %.

Keywords [en]
Hybrid Job-shop; Makespan; Mean Flow Time; Genetic Algorithm.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-15456OAI: oai:DiVA.org:bth-15456DiVA, id: diva2:1155488
Available from: 2017-11-08 Created: 2017-11-08 Last updated: 2017-11-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Search in DiVA

By author/editor
Gholami, Omid
By organisation
Department of Computer Science and Engineering
Other Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 59 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf