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001440980 020__ $$a9789811648595$$q(electronic bk.)
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001440980 0247_ $$a10.1007/978-981-16-4859-5$$2doi
001440980 035__ $$aSP(OCoLC)1285678479
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001440980 049__ $$aISEA
001440980 050_4 $$aTS157.5
001440980 08204 $$a004/.35$$223
001440980 1001_ $$aZhang, Fangfang,$$eauthor.
001440980 24510 $$aGenetic programming for production scheduling :$$ban evolutionary learning approach /$$cFangfang Zhang, Su Nguyen, Yi Mei, Mengjie Zhang.
001440980 264_1 $$aSingapore :$$bSpringer,$$c2021.
001440980 300__ $$a1 online resource (xxxiii, 336 pages) :$$billustrations (some color)
001440980 336__ $$atext$$btxt$$2rdacontent
001440980 337__ $$acomputer$$bc$$2rdamedia
001440980 338__ $$aonline resource$$bcr$$2rdacarrier
001440980 347__ $$atext file
001440980 347__ $$bPDF
001440980 4901_ $$aMachine learning,$$x2730-9916
001440980 504__ $$aIncludes bibliographical references and index.
001440980 5050_ $$aPart I Introduction -- 1 Introduction -- 2 Preliminaries -- Part II Genetic Programming for Static Production Scheduling Problems -- 3 Learning Schedule Construction Heuristics -- 4 Learning Schedule Improvement Heuristics -- 5 Learning to Augment Operations Research Algorithms -- Part III Genetic Programming for Dynamic Production Scheduling Problems -- 6 Representations with Multi-tree and Cooperative Coevolution -- 7 Efficiency Improvement with Multi-fidelity Surrogates -- 8 Search Space Reduction with Feature Selection -- 9 Search Mechanism with Specialised Genetic Operators -- Part IV Genetic Programming for Multi-objective Production Scheduling Problems -- 10 Learning Heuristics for Multi-objective Dynamic Production Scheduling Problems -- 11 Cooperative Coevolutionary for Multi-objective Production Scheduling Problems -- 12 Learning Scheduling Heuristics for Multi-objective Dynamic Flexible Job Shop Scheduling -- Part V Multitask Genetic Programming for Production Scheduling Problems -- 13 Multitask Learning in Hyper-heuristic Domain with Dynamic Production Scheduling -- 14 Adaptive Multitask Genetic Programming for Dynamic Job Shop Scheduling -- 15 Surrogate-Assisted Multitask Genetic Programming for Learning Scheduling Heuristics -- Part VI Conclusions and Prospects -- 16 Conclusions and Prospects.
001440980 506__ $$aAccess limited to authorized users.
001440980 520__ $$aThis book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.
001440980 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 19, 2021).
001440980 650_0 $$aComputer scheduling.
001440980 650_0 $$aGenetic programming (Computer science)
001440980 650_0 $$aMachine learning.
001440980 650_6 $$aOrdonnancement (Informatique)
001440980 650_6 $$aProgrammation génétique (Informatique)
001440980 650_6 $$aApprentissage automatique.
001440980 655_0 $$aElectronic books.
001440980 7001_ $$aNguyen, Su,$$eauthor.
001440980 7001_ $$aMei, Yi,$$eauthor.
001440980 7001_ $$aZhang, Mengjie,$$eauthor.
001440980 77608 $$iPrint version:$$aZhang, Fangfang.$$tGenetic programming for production scheduling.$$dSingapore : Springer, 2021$$z9811648581$$z9789811648588$$w(OCoLC)1259048208
001440980 830_0 $$aMachine learning,$$x2730-9916
001440980 852__ $$bebk
001440980 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-4859-5$$zOnline Access$$91397441.1
001440980 909CO $$ooai:library.usi.edu:1440980$$pGLOBAL_SET
001440980 980__ $$aBIB
001440980 980__ $$aEBOOK
001440980 982__ $$aEbook
001440980 983__ $$aOnline
001440980 994__ $$a92$$bISE