Items related to Hybrid Optimization: The Ten Years of CPAIOR
Synopsis
- PART 1: MODELING.- CHAPTER 2 - Hybrid Modeling; John Hooker.- PART 2: SOLVING.- CHAPTER 3 - Global Constraints; Jean-Charles Regin.- CHAPTER 4 Hybrid Solvers; J. Grossmann L. Wolsey.- CHAPTER 5 OverConstrained Problems; Willem van Hoeve.- PART 3 SEARCH.- CHAPTER 6 - Hybrid Search Strategies-> Integration of LS and tree search; Gilles Pesant.- CHAPTER 7 - Hybrid metaheuristics; Andrea Roli, Christian Blum.- CHAPTER 8 Learning in search/modeling; Philippe Refalo.- CHAPTER 9 Autonomous search; Yussef Hamadi, Frederic Saubion.- PART 4 TOOLS.- CHAPTER 10 - TOOLS; Mark Wallace, Peter Stuckey.- CHAPTER 11 Connections/Integration with SAT solvers; Youssef Hamadi and Lucas Bordeaux.- PART 5 INNOVATIVE APPLICATIONS.- CHAPTER 12 Bioinformatics; Pedro Barahona.- CHAPTER 13 Processor Validation; Yehuda Naveh.- CHAPTER 14 Networks; Helmut Simonis.- CHAPTER 15 Airline scheduling; Cynthia Barnhart.- CHAPTER 16 sport scheduling; Mike Trick.- CHAPTER 17 Optimal control of hybrid systems and embdded system design; Alberto Bemporad, Luca Benini.
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From the Back Cover
This volume focuses on the integration of artificial intelligence and constraint programming to solve problems in operations research and combinatorial optimization. This volume collects the contributions of experts from various research areas including decision theory, systems engineering, propositional satisfiability, mathematical optimization, and artificial intelligence. These invited scholars describe and demonstrate some of the most important topics and results from the last ten years of research related to hybrid optimization. Key Features: - Includes both well established research results, and directions for future research. - Provides several solution methods for common combinatorial optimization and decision problems. - Presents both theoretical techniques and real-world applications in artificial intelligence and operations research. Hybrid Optimization can serve as a valuable resource for graduate students, researchers and practitioners studying artificial intelligence or operations research who are interested in investigating or applying constraint programming techniques.
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