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Limited,c2021 9789811648588 327 $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 E?ciency Improvement with Multi-?delity 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. 330 $aThis book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP?s performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering. 410 0$aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 606 $aMachine learning 606 $aExpert systems (Computer science) 606 $aIndustrial engineering 606 $aProduction engineering 606 $aOperations research 606 $aMachine Learning 606 $aKnowledge Based Systems 606 $aIndustrial and Production Engineering 606 $aOperations Research and Decision Theory 615 0$aMachine learning. 615 0$aExpert systems (Computer science) 615 0$aIndustrial engineering. 615 0$aProduction engineering. 615 0$aOperations research. 615 14$aMachine Learning. 615 24$aKnowledge Based Systems. 615 24$aIndustrial and Production Engineering. 615 24$aOperations Research and Decision Theory. 676 $a658.53 700 $aZhang$b Fangfang$f1966-$01253205 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910508466203321 996 $aGenetic programming for production scheduling$92905344 997 $aUNINA