Integration of metaheuristic algorithms for job shop scheduling

dc.contributor.guideBalasubramanie Pen_US
dc.coverage.spatialMetaheuristic algorithms for job shop schedulingen_US
dc.creator.researcherThamilselvan Ren_US
dc.date.accessioned2014-09-03T09:06:22Z
dc.date.available2014-09-03T09:06:22Z
dc.date.awarded30/07/2012en_US
dc.date.completed01/07/2012en_US
dc.date.issued2014-09-03
dc.date.registeredn.d.en_US
dc.description.abstractScheduling is generally considered to be one of the most significant issues in the planning and operation of a manufacturing system Better scheduling systems have significant impact on cost reduction increased productivity, customer satisfaction and overall competitive advantage Proficient scheduling leads to increase in capacity utilization efficiency thereby reducing the time required to complete jobs and a consequent increase newlinein the profitability of an organization in the present competitive environment For each job in a job shop scheduling problem there is a sequence of operations which needs to be processed without interruption on a given machine for a given period of time Each machine can process only one job at a time The main objective of Job Shop Scheduling Problem is to find the feasible schedule that could minimize the maximum completion time number of iterations and processing time A job shop scheduling is among the toughest scheduling problems because of the huge number of possible solutions that could be generated for every problem There is no efficient conventional optimization algorithm that can guarantee an optimal solution in polynomial time Metaheuristics are used to solve the computationally hard optimization problems Metaheuristics are newlineused as a stand alone approach for solving hard combinatorial optimization problems newline newlineen_US
dc.description.noteReferences p.169-178,en_US
dc.format.accompanyingmaterialNoneen_US
dc.format.dimensions23cm.en_US
dc.format.extentxxiii, 180p.en_US
dc.identifier.urihttp://hdl.handle.net/10603/24563
dc.languageEnglishen_US
dc.publisher.institutionFaculty of Information and Communication Engineeringen_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.relationp.169-178,en_US
dc.rightsuniversityen_US
dc.source.universityUniversityen_US
dc.subject.keywordGenetic Algorithmen_US
dc.subject.keywordMetaheuristic algorithmsen_US
dc.subject.keywordParallel Simulated Annealingen_US
dc.subject.keywordSimulated Annealingen_US
dc.titleIntegration of metaheuristic algorithms for job shop schedulingen_US
dc.title.alternativeen_US
dc.type.degreePh.D.en_US

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