Genetic Programming for Production Scheduling: An Evolutionary Learning Approach
暫譯: 生物基因程式設計在生產排程中的應用：一種演化學習方法
Zhang, Fangfang, Nguyen, Su, Mei, Yi
- 出版商: Springer
- 出版日期: 2021-11-13
- 售價: $6,740
- 貴賓價: 9.5 折 $6,403
- 語言: 英文
- 頁數: 270
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9811648581
- ISBN-13: 9789811648588
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商品描述
This 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.
商品描述(中文翻譯)
本書介紹了一種進化學習方法，特別是遺傳編程（Genetic Programming, GP），用於生產排程。全書分為六個部分。第一部分提供了生產排程的介紹、現有的解決方法以及GP在生產排程中的應用。還介紹了生產環境的特徵、問題的表述、用於生產排程的抽象GP框架以及評估標準。第二部分展示了GP如何用於解決靜態生產排程問題的各種方法，以及它們與傳統運籌學方法的聯繫。接著，第三部分展示了如何設計GP算法以解決動態生產排程問題，並描述了增強GP性能的先進技術，包括特徵選擇、代理建模和專門的遺傳運算子。第四部分探討了如何使用啟發式方法來處理生產排程問題中的多個潛在衝突目標，並提出了一種先進的多目標方法，結合了合作共演技術或多樹表示法。第五部分展示了如何在超啟發式空間中使用多任務學習技術進行生產排程。它還顯示了代理技術和輔助任務選擇策略如何使GP的多任務學習受益，以便在生產排程的背景下學習啟發式方法。第六部分則對未來進行了展望。
考慮到其範疇，本書對於機器學習、人工智慧、進化計算、運籌學和工業工程領域的科學家、工程師、研究人員、實務工作者、研究生和本科生均有益處。
作者簡介
Fangfang Zhang is a Postdoctoral Research Fellow at the School of Engineering and Computer Science, Victoria University of Wellington, New Zealand. Her current research interests include evolutionary computation, hyper-heuristics learning/optimization, job shop scheduling, and multitask optimization.
Su Nguyen is a Senior Research Fellow and Algorithm Lead at the Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia. His expertise includes evolutionary computation, simulation optimization, automated algorithm design, interfaces of artificial intelligence/operations research, and their applications in logistics, energy, and transportation. Dr. Nguyen chaired the IEEE Task Force on Evolutionary Scheduling and Combinatorial Optimisation from 2014 to 2018. He gave technical tutorials on evolutionary computation and artificial intelligence-based visualization at the Parallel Problem Solving from Nature Conference in 2018 and the IEEE World Congress on Computational Intelligence in 2020.
Yi Mei is a Senior Lecturer at the School of Engineering and Computer Science, Victoria University of Wellington, New Zealand. He has published more than 100 articles in prominent journals for Evolutionary Computation and Operations Research, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Evolutionary Computation, European Journal of Operational Research, and ACM Transactions on Mathematical Software. His research interests include evolutionary scheduling and combinatorial optimization, machine learning, genetic programming, and hyper-heuristics.
Mengjie Zhang is a Professor of Computer Science, Head of the Evolutionary Computation Research Group, and Associate Dean (Research and Innovation) of the Faculty of Engineering, Victoria University of Wellington, New Zealand. His current research interests include artificial intelligence and machine learning, particularly genetic programming, image analysis, feature selection and reduction, job shop scheduling, and transfer learning. He has published over 600 research papers in international journals and conference proceedings. Prof. Zhang is a Fellow of the Royal Society of New Zealand, Fellow of the IEEE, and an IEEE Distinguished Lecturer. He has previously chaired the IEEE CIS Intelligent Systems and Applications Technical Committee, the IEEE CIS Emergent Technologies Technical Committee, and the Evolutionary Computation Technical Committee, and served on the IEEE CIS Award Committee. He is a Vice-Chair of the Task Force on Evolutionary Computer Vision and Image Processing, and the Founding Chair of the IEEE Computational Intelligence Chapter in New Zealand. He is a Fellow of the Royal Society of New Zealand, a Fellow of the IEEE, and an IEEE Distinguished Lecturer.
作者簡介(中文翻譯)
張芳芳是新西蘭威靈頓維多利亞大學工程與計算機科學學院的博士後研究員。她目前的研究興趣包括進化計算、超啟發式學習/優化、作業排程以及多任務優化。
阮蘇是澳大利亞墨爾本拉籌布大學數據分析與認知中心的高級研究員及算法負責人。他的專長包括進化計算、模擬優化、自動化算法設計、人工智慧/運籌學的介面及其在物流、能源和交通運輸中的應用。阮博士於2014年至2018年間擔任IEEE進化排程與組合優化工作組的主席。他在2018年的自然問題解決平行會議及2020年的IEEE計算智能世界大會上，針對進化計算和基於人工智慧的可視化進行了技術教程。
梅毅是新西蘭威靈頓維多利亞大學工程與計算機科學學院的高級講師。他在進化計算和運籌學的知名期刊上發表了超過100篇文章，包括《IEEE進化計算期刊》、《IEEE控制論期刊》、《進化計算》、《歐洲運籌學期刊》和《ACM數學軟體期刊》。他的研究興趣包括進化排程和組合優化、機器學習、遺傳編程和超啟發式。
張孟杰是新西蘭威靈頓維多利亞大學計算機科學教授、進化計算研究小組負責人及工程學院研究與創新副院長。他目前的研究興趣包括人工智慧和機器學習，特別是遺傳編程、圖像分析、特徵選擇與降維、作業排程和轉移學習。他在國際期刊和會議論文集中發表了超過600篇研究論文。張教授是新西蘭皇家學會院士、IEEE院士及IEEE傑出講師。他曾擔任IEEE CIS智能系統與應用技術委員會、IEEE CIS新興技術技術委員會及進化計算技術委員會的主席，並在IEEE CIS獎項委員會任職。他是進化計算機視覺與圖像處理工作組的副主席，以及新西蘭IEEE計算智能分會的創始主席。他是新西蘭皇家學會院士、IEEE院士及IEEE傑出講師。
