dc.contributor.authorHo Nhu Binhen_US
dc.identifier.citationHo, N. B. (2007). Learnable evolutionary model for flexible job-shop scheduling. Doctoral thesis, Nanyang Technological University, Singapore.
dc.description196 p.en_US
dc.description.abstractIn recent years, resource allocation problems have received much attention in the research community and industry. The most frequently encountered problems of these topics are the Job-Shop Scheduling Problem (JSP). Effective scheduling for the JSP has the potential to decrease cost and increase profits. The JSP can be modeled as the allocation of machines over specific time to process a collection of jobs. An extension of the JSP which makes this problem more difficult is the Flexible Job-Shop Scheduling Problem (FJSP). Unfortunately, the traditional deterministic approaches, such as Branch and Bound, Priority Dispatching Rules, have scaled poorly with large problem size. The current local search techniques have also performed in expensive computational time. Currently, the interaction between evolution and learning has received much attention from the research community. Some recent studies on machine learning have shown that it can significantly improve the efficiency of problem solving when using Evolutionary Algorithms (EAs).en_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
dc.subjectDRNTU::Engineering::Manufacturing::Production management
dc.titleLearnable evolutionary model for flexible job-shop schedulingen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.contributor.supervisorTay Joc Cingen_US
dc.description.degreeDOCTOR OF PHILOSOPHY (SCE)en_US

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