Modelling and Intelligent Optimisation of Production Scheduling in VCIM Systems

Description: Modelling and Intelligent Optimisation of Production Scheduling in VCIM Systems by Son Duy Dao 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. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Back Cover 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. Table of Contents Chapter 1: Introduction.- Chapter 2: Literature review.- Chapter 3: An innovative production scheduling model for VCIM systems.- Chapter 4: An innovative genetic algorithm for VCIM production scheduling optimisation.- Chapter 5: Case study.- Chapter 6: Conclusions and recommendations for future work. Feature Nominated as an outstanding PhD thesis by the University of South Australia, Adelaide Reports on an innovative production-scheduling model for virtual computer-integrated manufacturing (VCIM) systems Presents a robust genetic algorithm for optimising production scheduling in VCIM systems Description for Sales People Nominated as an outstanding PhD thesis by the University of South Australia, Adelaide Reports on an innovative production scheduling model for virtual computer-integrated manufacturing (VCIM) systems Presents a robust genetic algorithm for optimising production scheduling in VCIM systems Details ISBN3319721127 Author Son Duy Dao Publisher Springer International Publishing AG Series Springer Theses Year 2018 Edition 1st ISBN-10 3319721127 ISBN-13 9783319721125 Format Hardcover Imprint Springer International Publishing AG Place of Publication Cham Country of Publication Switzerland DEWEY 006.3 Pages 147 Illustrations 11 Illustrations, color; 6 Illustrations, black and white; XVII, 147 p. 17 illus., 11 illus. in color. Publication Date 2018-01-30 Language English Edited by Alfredo Cuzzocrea Birth 1974 Affiliation Massachusetts Institute of Technology Position journalist Qualifications S. J. Edition Description 1st ed. 2018 Alternative 9783319891415 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:131029633;

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