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Conference Paper: An enhanced ant colony optimization approach for integrated process planning and scheduling

TitleAn enhanced ant colony optimization approach for integrated process planning and scheduling
Authors
KeywordsIntegrated process planning and scheduling
Job shop scheduling
Ant colony optimization
Issue Date2013
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1803377
Citation
The 2013 International Conference on Industrial Engineering and Systems Management (IESM), Rabat, Morocco, 28-30 October 2013. In Conference Proceedings, 2013, p. 599-604 How to Cite?
AbstractAn enhanced ant colony optimization (eACO) meta-heuristics is proposed in this paper to accomplish the integrated process planning and scheduling (IPPS) in the jobshop environments. The IPPS problem is graphically formulated to implement the ACO algorithm. In accordance with the characteristics of the IPPS problem, the mechanism of eACO has been enhanced with several modifications, including quantification of convergence level, introduction of pheromone on nodes, new strategy of determining heuristic desirability and directive pheromone deposit strategy. Experiments are conducted to evaluate the approach, while makespan and CPU time are used as measurements. Encouraging results can be seen when comparing to other IPPS approaches based on evolutionary algorithms. © 2013 International Institute for Innovation, Industrial Engineering and Entrepreneurship - I4e2.
Persistent Identifierhttp://hdl.handle.net/10722/209625

 

DC FieldValueLanguage
dc.contributor.authorZhang, S-
dc.contributor.authorWong, TN-
dc.date.accessioned2015-05-11T08:49:35Z-
dc.date.available2015-05-11T08:49:35Z-
dc.date.issued2013-
dc.identifier.citationThe 2013 International Conference on Industrial Engineering and Systems Management (IESM), Rabat, Morocco, 28-30 October 2013. In Conference Proceedings, 2013, p. 599-604-
dc.identifier.urihttp://hdl.handle.net/10722/209625-
dc.description.abstractAn enhanced ant colony optimization (eACO) meta-heuristics is proposed in this paper to accomplish the integrated process planning and scheduling (IPPS) in the jobshop environments. The IPPS problem is graphically formulated to implement the ACO algorithm. In accordance with the characteristics of the IPPS problem, the mechanism of eACO has been enhanced with several modifications, including quantification of convergence level, introduction of pheromone on nodes, new strategy of determining heuristic desirability and directive pheromone deposit strategy. Experiments are conducted to evaluate the approach, while makespan and CPU time are used as measurements. Encouraging results can be seen when comparing to other IPPS approaches based on evolutionary algorithms. © 2013 International Institute for Innovation, Industrial Engineering and Entrepreneurship - I4e2.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1803377-
dc.relation.ispartofInternational Conference on Industrial Engineering and Systems Management (IESM)-
dc.rightsInternational Conference on Industrial Engineering and Systems Management (IESM). Copyright © IEEE.-
dc.rights©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectIntegrated process planning and scheduling-
dc.subjectJob shop scheduling-
dc.subjectAnt colony optimization-
dc.titleAn enhanced ant colony optimization approach for integrated process planning and scheduling-
dc.typeConference_Paper-
dc.identifier.emailWong, TN: tnwong@hku.hk-
dc.identifier.authorityWong, TN=rp00192-
dc.description.naturepublished_or_final_version-
dc.identifier.scopuseid_2-s2.0-84899129024-
dc.identifier.hkuros242529-
dc.identifier.spage599-
dc.identifier.epage604-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 150511-

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