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Integration of Constraint Programming, Artificial Intelligence, and Operations Research

19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022, Proceedings

  • Conference proceedings
  • © 2022

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Editors:
  1. Pierre Schaus

    1. UCLouvain, Louvain-la-Neuve, Belgium

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13292)

Included in the following conference series:

Conference proceedings info: CPAIOR 2022.

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About this book

This book constitutes the proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization”.

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Table of contents (28 papers)

  1. Front Matter

    Pages i-xix
    Download chapter PDF

  2. A Two-Phase Hybrid Approach for the Hybrid Flexible Flowshop with Transportation Times

    • Eddie Armstrong, Michele Garraffa, Barry O’Sullivan, Helmut Simonis
    Pages 1-13

  3. A SAT Encoding to Compute Aperiodic Tiling Rhythmic Canons

    • Gennaro Auricchio, Luca Ferrarini, Stefano Gualandi, Greta Lanzarotto, Ludovico Pernazza
    Pages 14-23

  4. Transferring Information Across Restarts in MIP

    • Timo Berthold, Gregor Hendel, Domenico Salvagnin
    Pages 24-33

  5. Towards Copeland Optimization in Combinatorial Problems

    • Sidhant Bhavnani, Alexander Schiendorfer
    Pages 34-43

  6. Coupling Different Integer Encodings for SAT

    • Hendrik Bierlee, Graeme Gange, Guido Tack, Jip J. Dekker, Peter J. Stuckey
    Pages 44-63

  7. Model-Based Algorithm Configuration with Adaptive Cap** and Prior Distributions

    • Ignace Bleukx, Senne Berden, Lize Coenen, Nicholas Decleyre, Tias Guns
    Pages 64-73

  8. Shattering Inequalities for Learning Optimal Decision Trees

    • Justin J. Boutilier, Carla Michini, Zachary Zhou
    Pages 74-90

  9. Learning Pseudo-Backdoors for Mixed Integer Programs

    • Aaron Ferber, Jialin Song, Bistra Dilkina, Yisong Yue
    Pages 91-102

  10. Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists

    • Ulrich Aïvodji, Julien Ferry, Sébastien Gambs, Marie-José Huguet, Mohamed Siala
    Pages 103-119

  11. Solving the Extended Job Shop Scheduling Problem with AGVs – Classical and Quantum Approaches

    • Marc Geitz, Cristian Grozea, Wolfgang Steigerwald, Robin Stöhr, Armin Wolf
    Pages 120-137

  12. Stochastic Decision Diagrams

    • J. N. Hooker
    Pages 138-154

  13. Improving the Robustness of EPS to Solve the TSP

    • Nicolas Isoart, Jean-Charles Régin
    Pages 155-172

  14. Efficient Operations Between MDDs and Constraints

    • Victor Jung, Jean-Charles Régin
    Pages 173-189

  15. Deep Policy Dynamic Programming for Vehicle Routing Problems

    • Wouter Kool, Herke van Hoof, Joaquim Gromicho, Max Welling
    Pages 190-213

  16. Learning a Propagation Complete Formula

    • Petr Kučera
    Pages 214-231

  17. A FastMap-Based Algorithm for Block Modeling

    • Ang Li, Peter Stuckey, Sven Koenig, T. K. Satish Kumar
    Pages 232-248

  18. Packing by Scheduling: Using Constraint Programming to Solve a Complex 2D Cutting Stock Problem

    • Yiqing L. Luo, J. Christopher Beck
    Pages 249-265

  19. Dealing with the Product Constraint

    • Steve Malalel, Victor Jung, Jean-Charles Régin, Marie Pelleau
    Pages 266-281

  20. Multiple-choice Knapsack Constraint in Graphical Models

    • Pierre Montalbano, Simon de Givry, George Katsirelos
    Pages 282-299
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  1. Integration of Constraint Programming, Artificial Intelligence, and Operations Research

Editors and Affiliations

  • UCLouvain, Louvain-la-Neuve, Belgium

    Pierre Schaus

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