Overview

Constraint and Integer Programming presents some of the basic ideas of constraint programming and mathematical programming, explores approaches to integration, brings us up to date on heuristic methods, and attempts to discern future directions in this fast-moving field.

Full Product Details

Author:   Michela Milano
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2004
Volume:   27
Dimensions:   Width: 15.50cm , Height: 2.10cm , Length: 23.50cm
Weight:   0.623kg
ISBN:  

9781461347194

ISBN 10:   146134719
Pages:   370
Publication Date:   28 September 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Constraint and Integer Programming.- 1 Introduction.- 2 CP(FD) Basic Concepts.- 3 Integer Linear Programming Basic Concepts.- 4 Incomplete search strategies.- 5 Conclusion.- References.- Two Generic Schemes for Efficient and Robust Cooperative Algorithms.- 1 Introduction.- 2 Operations Research Algorithms and Constraint Programming.- 3 Operations Research Algorithms and Mixed Integer Programming.- 4 Constraint Programming and Mixed Integer Programming.- 5 Operations Research Algorithms and Local Search.- 6 Mixed Integer Programming and Local Search.- 7 Constraint Programming and Local Search.- References.- Branch-and-Infer: A Framework for Combining CP and IP.- 1 Introduction.- 2 Modeling in CP and IP.- 3 An illustrating example: discrete tomography.- 4 Branch and Infer.- 5 Symbolic constraints in IP.- 6 Example: Symbolic constraints for supply chain planning.- 7 Summary.- References.- Global Constraints and FiItering Algorithms.- 1 Introduction.- 2 Global Constraints.- 3 Filtering Algorithms.- 4 Two Successful Filtering Algorithms.- 5 Global Constraints and Over-eonstrained Problems.- 6 Quality of Filtering Algorithms.- 7 Discussion.- 8 Conclusion.- References.- Exploiting relaxations in CP.- 1 Introduction and Motivation.- 2 Integer Linear Programming and Relaxations.- 3 Integrating Relaxations in CP.- 4 Relax to propagate.- 5 Relax to guide the search.- 6 A case study: global optimization constraints for a Path constraint.- References.- Hybrid Problem Solving in ECLiPSe.- 1 Introduction.- 2 Integration of Constraints and Operations Research.- 3 Language Ingredients for Hybrid Solvers.- 4 ECLiPSe as a Platform for Building Hybrid Aigorithms.- 5 Programming a Hybrid Search in ECLiPSe.- 6 Conclusion.- References.- CP Based Branch-and-Price.- 1 Introduction.- 2 Three Illustrative Examples.- 3 Implementation Issues.- 4 Future Directions for CP Based Branch-and-Price.- References.- Randomized Backtrack Search.- 1 Introduction.- 2 Randomization of Backtrack Search Methods.- 3 Formal Models of Heavy-Tailed Behavior.- 4 Heavy and Fat-Tailed Distributions.- 5 Heavy and Fat-Tailed Distributions in Backtrack Search.- 6 Restart Strategies.- 7 Portfolio Strategies.- 8 Conclusions.- References.- Local Search and Constraint Programming.- LS and CP ilLustrated on a transportation Problem.- 1 Introduction.- 2 A didactic transportation problem.- 3 A CP approach for dTP.- 4 Constructive Algorithms.- 5 LS as Post-Optimization.- 6 Metaheuristics.- 7 LS during construction.- 8 Conclusions.- References.- Open Perspectives.- 1 Motivations, Challenges and Applications.- 2 Transforming Models to Aigorithms.- 3 New Techniques.- 4 New Application Areas.- References.

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