Journal ArticleDOI
Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach
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TLDR
An innovative approach is presented, which is capable of automatically discovering effective dispatching rules that are competitive with those in the literature, which are the results of decades of research.Abstract:
Priority-dispatching rules have been studied for many decades, and they form the backbone of much industrial scheduling practice. Developing new dispatching rules for a given environment, however, is usually a tedious process involving implementing different rules in a simulation model of the facility under study and evaluating the rule through extensive simulation experiments. In this research, an innovative approach is presented, which is capable of automatically discovering effective dispatching rules. This is a significant step beyond current applications of artificial intelligence to production scheduling, which are mainly based on learning to select a given rule from among a number of candidates rather than identifying new and potentially more effective rules. The proposed approach is evaluated in a variety of single machine environments, and discovers rules that are competitive with those in the literature, which are the results of decades of research.read more
Citations
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Journal ArticleDOI
Hyper-heuristics: a survey of the state of the art
Edmund K. Burke,Michel Gendreau,Matthew Hyde,Graham Kendall,Gabriela Ochoa,Ender Özcan,Rong Qu +6 more
TL;DR: A critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas are presented.
Book ChapterDOI
A Classification of Hyper-heuristic Approaches
TL;DR: This chapter presents an overview of previous categorisations of hyper-heuristics and provides a unified classification and definition, which capture the work that is being undertaken in this field.
Journal ArticleDOI
Automated Design of Production Scheduling Heuristics: A Review
TL;DR: The state-of-the-art approaches are summarized, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling are summarized and suggested.
Book ChapterDOI
Exploring Hyper-heuristic Methodologies with Genetic Programming
TL;DR: This chapter discusses this class of hyper-heuristics, in which Genetic Programming is the most widely used methodology, and discusses the exciting potential of this innovative approach for automating the heuristic design process.
Journal ArticleDOI
Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming
TL;DR: Four new multi-objective genetic programming-based hyperheuristic (MO-GPHH) methods for automatic design of scheduling policies, including dispatching rules and due-date assignment rules in job shop environments are developed.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book
Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.