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Integration of Constraint Programming, Artificial Intelligence, and Operations Research

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Cover of 'Integration of Constraint Programming, Artificial Intelligence, and Operations Research'

Table of Contents

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    Book Overview
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    Chapter 1 Constraint Programming for Dynamic Symbolic Execution of JavaScript
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    Chapter 2 Sequential and Parallel Solution-Biased Search for Subgraph Algorithms
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    Chapter 3 Core-Boosted Linear Search for Incomplete MaxSAT
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    Chapter 4 Binary Decision Diagrams for Bin Packing with Minimum Color Fragmentation
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    Chapter 5 Local Rapid Learning for Integer Programs
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    Chapter 6 A Status Report on Conflict Analysis in Mixed Integer Nonlinear Programming
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    Chapter 7 Generating Compound Moves in Local Search by Hybridisation with Complete Search
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    Chapter 8 SAT Encodings of Pseudo-Boolean Constraints with At-Most-One Relations
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    Chapter 9 A Constraint Programming Approach to Electric Vehicle Routing with Time Windows
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    Chapter 10 A Sampling-Free Anticipatory Algorithm for the Kidney Exchange Problem
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    Chapter 11 Evaluating Ising Processing Units with Integer Programming
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    Chapter 12 Using Cost-Based Solution Densities from TSP Relaxations to Solve Routing Problems
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    Chapter 13 A Counting-Based Approach to Scalable Micro-service Deployment
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    Chapter 14 An Optimization Approach to the Ordering Phase of an Attended Home Delivery Service
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    Chapter 15 Consistency for 0–1 Programming
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    Chapter 16 An Investigation into Prediction + Optimisation for the Knapsack Problem
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    Chapter 17 The Maximum Weighted Submatrix Coverage Problem: A CP Approach
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    Chapter 18 Learning MILP Resolution Outcomes Before Reaching Time-Limit
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    Chapter 19 An Improved Subsumption Testing Algorithm for the Optimal-Size Sorting Network Problem
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    Chapter 20 Investigating Constraint Programming for Real World Industrial Test Laboratory Scheduling
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    Chapter 21 An Approach to Robustness in the Stable Roommates Problem and Its Comparison with the Stable Marriage Problem
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    Chapter 22 Optimality Clue for Graph Coloring Problem
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    Chapter 23 Computing Wasserstein Barycenters via Linear Programming
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    Chapter 24 Repairing Learned Controllers with Convex Optimization: A Case Study
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    Chapter 25 A Hybrid Approach for Exact Coloring of Massive Graphs
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    Chapter 26 Modelling and Solving the Minimum Shift Design Problem
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    Chapter 27 A Computational Comparison of Optimization Methods for the Golomb Ruler Problem
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    Chapter 28 A New CP-Approach for a Parallel Machine Scheduling Problem with Time Constraints on Machine Qualifications
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    Chapter 29 Efficient Solution Methods for the Cumulative-Interference Channel Assignment Problem Using Integer Optimization and Constraint Programming
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    Chapter 30 Heat Exchanger Circuitry Design by Decision Diagrams
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    Chapter 31 Column Generation for Real-Time Ride-Sharing Operations
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    Chapter 32 Some Experiments with Submodular Function Maximization via Integer Programming
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    Chapter 33 Metric Hybrid Factored Planning in Nonlinear Domains with Constraint Generation
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    Chapter 34 Last-Mile Scheduling Under Uncertainty
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    Chapter 35 Building Optimal Steiner Trees on Supercomputers by Using up to 43,000 Cores
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    Chapter 36 Deep Inverse Optimization
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    Chapter 37 A Study on the Traveling Salesman Problem with a Drone
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    Chapter 38 Lower Bounds for Uniform Machine Scheduling Using Decision Diagrams
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    Chapter 39 Extending Compact-Diagram to Basic Smart Multi-Valued Variable Diagrams
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    Chapter 40 Arc Consistency Revisited
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    Chapter 41 Embedding Decision Diagrams into Generative Adversarial Networks
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    Chapter 42 Time Table Edge Finding with Energy Variables
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    Chapter 43 Quadratic Reformulation of Nonlinear Pseudo-Boolean Functions via the Constraint Composite Graph
Attention for Chapter 16: An Investigation into Prediction + Optimisation for the Knapsack Problem
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Chapter title
An Investigation into Prediction + Optimisation for the Knapsack Problem
Chapter number 16
Book title
Integration of Constraint Programming, Artificial Intelligence, and Operations Research
Published by
Springer, Cham, June 2019
DOI 10.1007/978-3-030-19212-9_16
Book ISBNs
978-3-03-019211-2, 978-3-03-019212-9

Emir Demirović, Peter J. Stuckey, James Bailey, Jeffrey Chan, Chris Leckie, Kotagiri Ramamohanarao, Tias Guns

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Student > Bachelor 3 21%
Researcher 3 21%
Student > Master 2 14%
Unknown 2 14%
Readers by discipline Count As %
Computer Science 6 43%
Mathematics 1 7%
Biochemistry, Genetics and Molecular Biology 1 7%
Chemical Engineering 1 7%
Economics, Econometrics and Finance 1 7%
Other 1 7%
Unknown 3 21%