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Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

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Table of Contents

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    Book Overview
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    Chapter 1 Preference Elicitation and Preference Learning in Social Choice
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    Chapter 2 Propagation in Constraints: How One Thing Leads to Another
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    Chapter 3 On Bilevel Programming and Its Impact in Branching, Cutting and Complexity
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    Chapter 4 Optimization Methods for the Partner Units Problem
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    Chapter 5 Manipulating MDD Relaxations for Combinatorial Optimization
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    Chapter 6 The AllDifferent Constraint with Precedences
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    Chapter 7 Retail Store Workforce Scheduling by Expected Operating Income Maximization
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    Chapter 8 Spatial and Objective Decompositions for Very Large SCAPs
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    Chapter 9 Upgrading Shortest Paths in Networks
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    Chapter 10 Parallel Machine Scheduling with Additional Resources: A Lagrangian-Based Constraint Programming Approach
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    Chapter 11 Branch-Cut-and-Propagate for the Maximum k -Colorable Subgraph Problem with Symmetry
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    Chapter 12 Climbing Depth-Bounded Adjacent Discrepancy Search for Solving Hybrid Flow Shop Scheduling Problems with Multiprocessor Tasks
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    Chapter 13 On Counting Lattice Points and Chvátal-Gomory Cutting Planes
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    Chapter 14 Precedence Constraint Posting for Cyclic Scheduling Problems
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    Chapter 15 A Probing Algorithm for MINLP with Failure Prediction by SVM
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    Chapter 16 Recovering Indirect Solution Densities for Counting-Based Branching Heuristics
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    Chapter 17 Using Hard Constraints for Representing Soft Constraints
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    Chapter 18 The Objective Sum Constraint
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    Chapter 19 Almost Square Packing
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    Chapter 20 Efficient Planning of Substation Automation System Cables
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    Chapter 21 A New Algorithm for Linear and Integer Feasibility in Horn Constraints
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    Chapter 22 Timetable Edge Finding Filtering Algorithm for Discrete Cumulative Resources
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    Chapter 23 Identifying Patterns in Sequences of Variables
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Title
Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Published by
Springer, Berlin, Heidelberg, January 2011
DOI 10.1007/978-3-642-21311-3
ISBNs
978-3-64-221310-6, 978-3-64-221311-3
Editors

Tobias Achterberg, J. Christopher Beck

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 3 3%
United States 3 3%
Malaysia 1 <1%
Italy 1 <1%
Chile 1 <1%
Belgium 1 <1%
Mexico 1 <1%
Unknown 95 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 35%
Student > Master 16 15%
Researcher 13 12%
Student > Doctoral Student 9 8%
Student > Bachelor 5 5%
Other 15 14%
Unknown 11 10%
Readers by discipline Count As %
Computer Science 36 34%
Engineering 29 27%
Business, Management and Accounting 10 9%
Agricultural and Biological Sciences 4 4%
Mathematics 3 3%
Other 8 8%
Unknown 16 15%