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008			211112s2021 si | s |||| 0|eng d
020			_a9789811648595
024	7		_a10.1007/978-981-16-4859-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTS157.5 _b2021 EB
100	1		_aZhang, Fangfang, _9681849 _d1966-
245	1	0	_aGenetic Programming for Production Scheduling : _bAn Evolutionary Learning Approach _cby Fangfang Zhang, Su Nguyen, Yi Mei, Mengjie Zhang.
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Publising _c2021
300			_a1 recurso en línea (XXXIII, 336 páginas) _b154 ilustraciones, 105 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aMachine Learning: Foundations Methodologies and Applications _x2730-9916
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_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.
520	3		_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.
988			_aSpringer_Computer_2021
650		7	_2embne _9150644 _aGestión de la producción
700	1		_aNguyen, Su _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aMei, Yi _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aZhang, Mengjie _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9789811648588
776	0	8	_iPrinted edition: _z9789811648601
776	0	8	_iPrinted edition: _z9789811648618
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-4859-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eh _zSI