Modelling and Intelligent Optimisation of Production Scheduling in VCIM Systems [electronic resource] /
By: Dao, Son Duy [author.].
Contributor(s): SpringerLink (Online service).
Series: Springer Theses, Recognizing Outstanding Ph.D. Research: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XVII, 147 p. 17 illus., 11 illus. in color. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319721132.Subject(s): Mechanical Engineering
| Engineering Economics, Organization, Logistics, Marketing | Manufacturing, Machines, Tools, Processes | LogisticsDDC classification: 658.5 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
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Springer Nature eBookSummary: This thesis reports on an innovative production-scheduling model for virtual computer-integrated manufacturing (VCIM) systems. It also describes a robust genetic algorithm for production scheduling in VCIM systems. The model, which is the most comprehensive of its kind to date, is not only capable of supporting collaborative shipment scheduling and handling multiple product orders simultaneously, but also helps cope with multiple objective functions under uncertainties. In turn, the genetic algorithm, characterised by an innovative algorithm structure, chromosome encoding, crossover and mutation, is capable of searching for optimal/suboptimal solutions to the complex optimisation problem in the VCIM production- scheduling model described. Lastly, the effectiveness of the proposed approach is verified in a comprehensive case study.
This thesis reports on an innovative production-scheduling model for virtual computer-integrated manufacturing (VCIM) systems. It also describes a robust genetic algorithm for production scheduling in VCIM systems. The model, which is the most comprehensive of its kind to date, is not only capable of supporting collaborative shipment scheduling and handling multiple product orders simultaneously, but also helps cope with multiple objective functions under uncertainties. In turn, the genetic algorithm, characterised by an innovative algorithm structure, chromosome encoding, crossover and mutation, is capable of searching for optimal/suboptimal solutions to the complex optimisation problem in the VCIM production- scheduling model described. Lastly, the effectiveness of the proposed approach is verified in a comprehensive case study.
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