Tuesday, October 18, 2016

[DMANET] CPAIOR 2017 Second CfP - Integration of AI, OR and CP for optimization

CPAIOR 2017, Call for Papers

Padova, June 5-8 2017 

[Apologies for cross-posting]

General Information

The Fourteenth International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming will be held in Padova, Italy, June 5 - 8, 2017, with a Master Class on "Computational Techniques for Combinatorial Optimization" on June 5, and the Main Conference on June 6 - 8, 2017.

The aim of the conference is to bring together interested researchers from Constraint Programming (CP), Artificial Intelligence (AI), and Operations Research (OR) to present new techniques or applications in combinatorial optimization and to provide an opportunity for researchers in one area to learn about techniques in the others.

A main objective of this conference series is also to give these researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems.

Therefore papers that actively combine, integrate, or contrast approaches from more than one of the areas are especially solicited. High quality papers from a single area are also welcome, provided that they are of interest to other communities involved. Application papers showcasing CP/AI/OR techniques on novel and challenging applications or experience reports on such applications are strongly encouraged.

The program committee invites submissions that include but are not limited to the following topics:

* Inference and relaxation methods: constraint propagation, cutting planes, global constraints, graph algorithms, dynamic programming, Lagrangian and convex relaxations, heuristic functions based on relaxations. 
* Search methods: branch and bound, intelligent backtracking, incomplete search, randomized search, portfolios, column generation, Benders decomposition or any other decomposition methods, local search and meta­heuristics
* Integration of machine learning and optimization: learning-based search and heuristics, use of predictive models in optimization, constraint acquisition, optimization for training machine learning models
* Integration methods: solver communication, model transformations and solver selection, parallel and distributed resolution techniques, models, and solvers. 
* Modeling methods: comparison of models, symmetry breaking, uncertainty, dominance relationships. 
* Innovative Applications of CP/AI/OR techniques. 
* Implementation of CP/AI/OR techniques and optimization systems.

More information is available on the conference web site: http://cpaior2017.dei.unipd.it/

Important Dates

* Abstract submission deadline: 14 Nov
* Paper submission deadline: 21 Nov
* Rebuttal period: 20-23 Dec
* Final notification: 16 Jan
* Camera-ready version: 31 Jan

Submission process and formats

Paper submissions are of two types:

* Long papers (15 pages, plus references)
* Short papers (8 pages, plus references)

The conference proceedings will be published on the LNCS series.

Additionally, outstanding submissions to the technical program will be offered the opportunity to be published exclusively through a "fast track" process in the "Constraint" Journal. Journal fast track paper will still be regularly presented at the conference.

All papers are to be submitted electronically in PDF format via easychair:


Authors should follow the submission instructions on the conference website. In the particular, they should comply with the required format (LNCS style) and page limits.

For any queries on the submission process, please contact the program chairs at dominiqs@gmail.com and michele.lombardi2@unibo.it


* Program chairs:
  - Domenico Salvagnin (DEI, University of Padova), http://www.dei.unipd.it/~salvagni/
  - Michele Lombardi (DISI, University of Bologna), http://ai.unibo.it/people/MicheleLombardi

* Conference chair:
  - Domenico Salvagnin (DEI, University of Padova), http://www.dei.unipd.it/~salvagni/

* Program Committee:
  - Chris Beck, University of Toronto
  - David Bergman, University of Connecticut
  - Timo Berthold, Fair Isaac Germany GmbH
  - Hadrien Cambazard, Grenoble INP
  - Andre A. Cire, University of Toronto
  - Matteo Fischetti, University of Padova
  - Bernard Gendron, Université de Montréal
  - Ambros Gleixner, Zuse Institute Berlin
  - Carla Gomes, Cornell University
  - Tias Guns, KU Leuven
  - John Hooker, Tepper School of Business, Carnegie Mellon University
  - Matti Järvisalo, University of Helsinki
  - Serdar Kadioglu, Oracle Corporation
  - Philip Kilby, Australia National University
  - Joris Kinable, Carnegie Mellon University
  - Jeff Linderoth, University of Wisconsin-Madison
  - Andrea Lodi, École Polytechnique de Montréal
  - Ines Lynce, Instituto Superior Técnico, Lisboa
  - Laurent Michel, University of Connecticut
  - Michela Milano, University of Bologna
  - Michele Monaci, University of Bologna
  - Siegfried Nijssen, UC Louvain
  - Barry O'Sullivan, University College Cork, Insight center
  - Claude-Guy Quimper, Université Laval
  - Jean-Charles Régin, Université de Nice-Sophia Antipolis
  - Louis-Martin Rousseau, École Polytechnique de Montréal
  - Ashish Sabharwal, Allen Institute for Artificial Intelligence
  - Scott Sanner, University of Toronto
  - Pierre Schaus, UC Louvain
  - Christian Schulte, KTH Royal Institute of Technology
  - Helmut Simonis, University College Cork
  - Christine Solnon, INSA Lyon
  - Peter-J. Stuckey, University of Melbourne
  - Michael Trick, Carnegie Mellon University
  - Pascal Van-Hentenryck, University of Michigan
  - Willem-Jan Van-Hoeve, Tepper School of Business, Carnegie Mellon University
  - Sicco Verwer, Delft University of Technology
  - Toby Walsh, University of New South Wales and Data61
  - Alessandro Zanarini, ABB CRC
  - Yingqian Zhang, TU Eindoven

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