Genetic Programming for Production Scheduling: An Evolutionary Learning Approach

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.

本書介紹了一種演化學習方法，具體而言是基因編程（GP），用於生產排程。本書分為六個部分。第一部分介紹了生產排程、現有解決方法以及GP方法在生產排程中的應用。還介紹了生產環境的特徵、問題定義、生產排程的抽象GP框架以及評估標準。第二部分展示了GP解決靜態生產排程問題的各種方法，以及它們與傳統運營研究方法的聯繫。接著，第三部分介紹了如何設計GP算法來解決動態生產排程問題，並描述了提高GP性能的高級技術，包括特徵選擇、替代建模和專門的遺傳操作符。第四部分討論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，並提出了一種基於合作共進化技術或多樹表示的高級多目標方法。第五部分演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。它還展示了如何利用替代技術和輔助任務選擇策略來改進GP在生產排程上的多任務學習。第六部分對未來進行了展望。

鑑於本書的範圍，它對機器學習、人工智能、演化計算、運營研究和工業工程等領域的科學家、工程師、研究人員、從業人員、研究生和本科生都有益處。