Padova, Italy

June 5 - June 8, 2017


CPAIOR 2017, the Fourteenth International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming, will be held in Padova, Italy, June 5 - June 8, 2017. A Master Class on Computational Techniques for Combinatorial Optimization on June 5, and the Main Conference on June 6 - June 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 constraint relaxation.
  • Search methods: branch and bound, intelligent backtracking, incomplete search, randomized search, portfolios, column generation, Benders decompositions or any other decomposition methods, local search, meta­heuristics.
  • Integration methods: solver communication, model transformations and solver selection, parallel and distributed resolution techniques, models, and solvers.
  • Modelling 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.

The proceedings of the conference are published on the Lecture Notes on Computer Science series. as LNCS 10335.

Important Dates

Submission Schedule (for both long and short papers):

  • Abstracts: November 16, 2016
  • Long Papers: November 22, 2016
  • Short Papers: November 25, 2016
  • Rebuttal Phase: December 23-28, 2016
  • Final notification: January 16, 2017
  • Camera-ready version: February 10, 2017

All deadlines are intended until 23:59 UCT-12: for example, if it is still November 16 anywhere in the world, then you can still submit an abstract.

Registration Deadlines:

  • Early: April 15, 2017
  • Regular: May 31, 2017

Conference Schedule:

  • Master Class: June 5, 2017
  • Main Conference: June 6-8, 2017


Paper submissions are of two types:

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

Long papers should present original unpublished work and be at most 15 LNCS pages plus references in length, and should be prepared in the format used for the Springer Lecture Notes in Computer Science series. These papers will undergo rigorous review. The proceedings will be published in the Springer Lecture Notes in Computer Science series.

Short papers are also encouraged, limited to 8 LNCS pages plus references and should be prepared with the same format as long papers. Although containing less material, short papers should describe original unpublished work and will be reviewed to the same criteria of quality as long papers. It is also encouraged to submit short papers about work in progress on ideas that are interesting but for which the practical or theoretical relevance is not yet fully identified. Short papers will be presented at the conference and published in the conference proceedings.

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.

For any queries on the submission process, please contact the program chairs at and

Abstracts and papers must be submitted on EasyChair by following this link:


Program chairs:

Conference chair:

Program Committee:

  • Chris Beck, University of Toronto
  • David Bergman, University of Connecticut
  • Timo Berthold, Fair Isaac Germany
  • Pierre Bonami, IBM Spain
  • 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 (ZIB)
  • 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, NICTA and Australian 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
  • Michele Monaci, University of Bologna
  • Siegfried Nijssen, Leiden University
  • 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, LIRIS CNRS UMR 5205
  • 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, NICTA and UNSW
  • Alessandro Zanarini, ABB CRC
  • Yingqian Zhang, TU Eindoven


Registration can be done online here.

The registration website also provides information about:

  • registration fees and deadlines
  • travel
  • recommended hotels for accomodation


You can download a PDF version of the program here.

Master Class

The master class will be on Computational Techniques for Combinatorial Optimization, and will be made of four talks:

  • Solving Mixed Integer Programs in Practice
    Tobias Achterberg

    Many optimization problems appearing in industry applications can be modeled as mixed integer programs (MIP). Typically, such a model is passed to a general-purpose MIP solver, which needs to find a solution to the problem in a black-box fashion without any additional user interaction.

    This lecture provides an overview of the main ingredients of state-of-the-art MIP solvers. We will show how MIPs are tackled in practice and evaluate the performance impact of the individual components of a MIP solver. In particular, we will cover presolving, linear programming, cutting planes, branching, and primal heuristics.[PDF]

  • MiniCP: A Minimalistic Educational Solver
    Pierre Schaus, Pascal Van Hentenryck, Laurent Michel

    The tutorial-style lecture will first delve into MiniCP, a minimalistic Java micro-kernel solver designed expressly for teaching Constraint Programming. It will gradually introduce key concepts such as domains, variables, constraints, fixpoints and the fundamentals of search and state management. Architectural design decisions as well as implementation issues will be front and center.[PDF]

  • Modeling, Constraint Solving and Model Manipulation
    Pierre Schaus, Pascal Van Hentenryck, Laurent Michel

    The tutorial will revisit the mantra CP = Model + Search to explore design decisions pertaining to modeling and propagation implementation. This includes, for instance, rich variables, views, constraint classes, event models, propagation, consistency handling and priorities. It will also explore the issues surrounding technology agnostic model reformulation and manipulations such as linearizations, relaxations and parallel support.[PDF]

  • Searching in Style and coping with Hybrids
    Pierre Schaus, Pascal Van Hentenryck, Laurent Michel

    The tutorial will explore the spectrum of issues arising from the implementation of search techniques. For instance, it will review goal-directed search procedures and detail continuation-based search as well as search combinators. The tutorial will also explore the issues that surface with tree-based parallelization, semantic decomposition and embarrassingly parallel search. Advanced hybrid techniques (including LNS) will complete the study of hybrid solvers.

DSO Workshop

Parallel to the master class, a DSO (Data Science meets Optimization) workshop will also take place, jointly with CEC 2017. You can find all the relevant information here.

Invited Speakers

  • On the role of (machine) learning in (mathematical) optimization
    Andrea Lodi

    In this talk, I try to explain my point of view as a Mathematical Optimizer -- especially concerned with discrete (integer) decisions -- on Big Data. I advocate a tight integration of Machine Learning and Mathematical Optimization (among others) to deal with the challenges of decision-making in Data Science. For such an integration I concentrate on three questions: 1) what can optimization do for machine learning? 2) what can machine learning do for optimization? 3) which new applications can be solved by the combination of machine learning and optimization? Finally, I will discuss in details two areas in which machine learning techniques have been (successfully) applied in the area of mixed-integer programming.[PDF]

  • Relational Quadratic Programming: Exploiting Symmetries for Modelling and Solving Quadratic Programs
    Kristian Kersting

    Symmetry is the essential element of lifted inference that has recently demonstrated the possibility to perform very efficient inference in highly-connected, but symmetric probabilistic models models aka. relational probabilistic models. This raises the question, whether this holds for optimization problems in general. In this talk I shall demonstrate that for a large class of mathematical programs this is actually the case. More precisely, I shall introduce the concept of fractional symmetries of linear and convex quadratic programs (QPs), which lie at the heart of many machine learning approaches, and exploit it to lift, i.e., to compress them. These lifted QPs can then be tackled with the usual optimization toolbox (off-the-shelf solvers, cutting plane algorithms, stochastic gradients etc.): If the original QP exhibits symmetry, then the lifted one will generally be more compact, and hence their optimization is likely to be more efficient.[PDF]

    This talk is based on joint works with Martin Mladenov, Martin Grohe, Leonard Kleinhans, Pavel Tokmakov, Babak Ahmadi, Amir Globerson, and many others.

Conference Talks

The proceedings of the conference are available online for a limited period here.

Talks in order of presentation:

  1. Sharpening Constraint Programming approaches for Bit-Vector Theory
    Z. Chihani, B. Marre, F. Bobot, S. Bardin
  2. Range-Consistent Forbidden Regions of Allen's Relations
    N. Beldiceanu, M. Carlsson, A. Derrien, C. Prud'Homme, A. Schutt, P.J. Stuckey
  3. MDDs are Efficient Modeling Tools: An Application to Dispersion Constraints
    G. Perez, J.-C. Régin
  4. On Finding the Optimal Relaxed Decision Diagram
    D. Bergman, A.A. Cire
  5. Design and Implementation of Bounded-Length Sequence Variables
    J.D. Scott, P. Flener, J. Pearson, C. Schulte
  6. Auto-Tabling for Subproblem Presolving in MiniZinc
    J.J. Dekker, G. Björdal, M. Carlsson, P. Flener, J.-N. Monette
  7. In Search of Balance: The Challenge of Generating Balanced Latin Rectangles
    M. Diaz, R. Le Bras, C. Gomes
  8. Debugging Unsatisfiable Constraint Models
    K. Leo, G. Tack
  9. Learning decision trees with flexible constraints and objectives using integer optimization
    S. Verwer, Y. Zhang
  10. Mining Time-constrained Sequential Patterns with Constraint Programming
    J.O.R. Aoga, T. Guns, P. Schaus
  11. Relaxation Methods for Constrained Matrix Factorization Problems: Solving the Phase Mapping Problem in Materials Discovery
    J. Bai, J. Bjorck, Y. Xue, S.K. Suram, J. Gregoire, C. Gomes
  12. Minimizing Landscape Resistance for Habitat Conservation
    D. De Uña, G. Gange, P. Schachte, P.J. Stuckey
  13. A Hybrid approach for Stator Winding design optimization
    A. Zanarini, J. Poland
  14. A Distributed Optimal Method for the Geographically Distributed Data Centres Problem
    M. Wahbi, D. Grimes, D. Mehta, K.N. Brown, B. O'Sullivan
  15. Explanation-Based Weighted Degree
    E. Hebrard, M. Siala
  16. Counting Weighted Spanning Trees to Solve Constrained Minimum Spanning Tree Problems
    A. Delaite, G. Pesant
  17. Efficient Filtering for the Energy-Cost AllDifferent Constraint
    S. Van Cauwelaert, P. Schaus
  18. The Weighted Arborescence Constraint
    V.R. Houndji, P. Schaus, M.N. Hounkonnou, L. Wolsey
  19. Learning when to use a decomposition
    M. Kruber, M.E. Lübbecke, A. Parmentier
  20. Experiments with Conflict Analysis in Mixed Integer Programming
    J. Witzig, T. Berthold, S. Heinz
  21. A first look at picking dual variables for maximizing reduced-cost based fixing
    O.S. Bajgiran, A.A. Cire, L.-M. Rousseau
  22. Experimental validation of volume-based comparison for double-McCormick relaxations
    E. Speakman, H. Yu, J. Lee
  23. Vehicle Routing Problem with Min-max Objective and Heterogeneous Fleet
    M. Yu, V. Nagarajan, S. Shen
  24. Solving the Traveling Salesman Problem with Time Windows with Dynamic Discretization Discovery
    N. Boland, M. Hewitt, M. Savelsbergh, D.M. Vu
  25. A fast Prize-collecting Steiner Forest algorithm for Functional Analyses in Biological Networks
    M. Akhmedov, A. Lenail, F. Bertoni, I. Kwee, E. Fraenkel, R. Montemanni
  26. Scenario based Learning for Stochastic Combinatorial Optimisation
    D. Hemmi, G. Tack, M. Wallace
  27. Optimal Stock Sizing in a Cutting Stock Problem with Stochastic Demands
    A. Zanarini
  28. Stochastic task networks: Trading performance for stability
    K.S. Mountakis, T. Klos, C. Witteveen
  29. Rescheduling Railway Traffic on Real Time Situations using Time-Interval Variables
    Q. Cappart, P. Schaus
  30. Dynamic Temporal Decoupling
    C. Witteveen, S. Mountakis, T. Klos
  31. A Multi-stage Simulated Annealing Algorithm for the Torpedo Scheduling Problem
    L. Kletzander, N. Musliu
  32. Cumulative scheduling with variable task profiles and concave piecewise linear processing rate functions
    M. Nattaf, C. Artigues, P. Lopez
  33. Combining CP and ILP in a tree decomposition of bounded height to solve the sum colouring problem
    M. Minot, S.N. Ndiaye, C. Solnon
  34. htd - A Free, Open-Source Framework for (Customized) Tree Decompositions and Beyond
    M. Abseher, N. Musliu, S. Woltran
  35. The Nemhauser-Trotter Reduction and Lifted Message Passing for Weighted CSPs
    H. Xu, T.K. Satish Kumar, S. Koenig
  36. A Local Search Approach for Incomplete Soft Constraint Problems: Experimental Results on Meeting Scheduling Problems
    M. Gelain, M.S. Pini, F. Rossi, K.B. Venable, T. Walsh


We would like to thank our sponsors for their generous support of CPAIOR 2017.



The conference will take place in the Aula Magna of the Department of Information Engineering, University of Padova (Via Gradenigo 6b, 35131 Padova).

Additional travel information can be found on the registration website here.