Practice Of Constraint Programming Research Articles

Introduction to the 33rd international conference on logic programming special issue

This special issue of Theory and Practice of Logic Programming (TPLP) contains the regular papers accepted for presentation at the 33rd International Conference on Logic Programming (ICLP 2017), held in Melbourne, Australia from the 28th of August to the 1st of September, 2017. ICLP 2017 was colocated with the 23rd International Conference on Principles and Practice of Constraint Programming (CP 2017) and the 20th International Conference on Theory and Applications of Satisfiability Testing (SAT 2017). Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming.

Introduction to the 31st International Conference on Logic Programming special issue

The 31st edition of the International Conference of Logic Programming (ICLP 2015) took place in Cork, Ireland, from 31 August 2015 to 4 September 2015, co-located with the 21st International Conference on Principles and Practice of Constraint Programming (CP 2015) and part of George Boole 200, a celebration of the life and work of George Boole who was born in 1815 and worked at the University College of Cork.

Necessary Conditions for Tractability of Valued CSPs

The connection between constraint languages and clone theory has been a fruitful line of research on the complexity of constraint satisfaction problems. In a recent result, Cohen et al. [SIAM J. Comput., 42 (2013), pp. 915--1939] have characterized a Galois connection between valued constraint languages and so-called weighted clones. In this paper, we study the structure of weighted clones. We extend the results of Creed and Živný from [Proceedings of the 17th International Conference on Principles and Practice of Constraint Programming, 2011, pp. 210--224] on types of weightings necessarily contained in every nontrivial weighted clone. This result has immediate computational complexity consequences as it provides necessary conditions for tractability of weighted clones and thus valued constraint languages. We demonstrate that some of the necessary conditions are also sufficient for tractability, while others are provably not.

Introduction to the special issue on the Fourteenth International Conference on Principles and Practice of Constraint Programming (CP 2008)

This special issue contains five papers that have been presented at the 14th International Conference on Principles and Practice of Constraint Programming (CP 2008) held in Sydney, Australia, September 14–18, 2008, which was held in conjunction with the International Conference on Automated Planning and Scheduling (ICAPS 2008) and the International Conference on Knowledge Representation and Reasoning (KR 2008). Held annually, the CP conference series is the premier international conference on constraint programming. All 120 submissions to the conference were reviewed by three separate reviewers and extensively discussed. After the final program was selected a subcommittee consisting of myself, Laurent Michel and Michael Trick, considered which papers should be considered for best paper awards. This gave a shortlist of six papers from which the eventual winners were selected. After the conference we invited the authors of these six papers to submit a longer version for the Constraint journal special issue from the conference. The five papers herein were the result. The first article, “A Hybrid Model for a Multiproduct Pipeline Planning and Scheduling Problem,” considers the complexity of managing a large oil pipeline an illustrates the power and flexibility of constraint programming in tackling complex real world problems. The second article, “A New Framework for Sharp and Efficient Resolution of NCSP with Manifolds of Solutions,” modifies interval solving methods to change the axes on which interval propagation is performed to substantially improve the rate of convergence of interval propagation. The third article, “A Branch and Bound Algorithm for Numerical Max-CSP,” defines an innovative approach to Max-CSP for interval problems by simultaneously computing inner and outer approximations of the solutions sets.

Introduction to the special issue on the thirteenth international conference on principles and practice of constraint programming (CP 2007)

This special issue contains four papers that have been presented at the 13th International Conference on Principles and Practice of Constraint Programming (CP 2007), which was held in Providence, RI, USA, September 23–27, 2007, in conjunction with the International Conference on Automated Planning and Scheduling (ICAPS). Held annually, the CP conference series is the premier international conference on constraint programming. All 165 papers submitted to the conference (143 submissions as research papers and 22 as application papers) received three reviews and were discussed extensively by the members of the Program Committee. From the 52 papers accepted for presentation at the conference, I selected a dozen of papers as candidates for the best paper award, based on the reviews they received. This small set of papers were even further discussed by a subcommittee consisting of Javier Larrosa, Christophe Lecoutre, Christian Schulte and myself. If only one paper finally received the best paper award, it had been chosen from a set of five outstanding papers that were very hard to separate. I thus chose these five papers to be expanded into journal form and submitted to this special issue. One of them declined the invitation because one of the authors was the editor in chief of this journal. The four other papers are those that appear in this issue. The four papers address topics of Constraint Programming that are all very active and promising, such as clause learning, constraint propagation via clause generation, visualisation in constraint-based systems, or solution counting. I would like to thank Javier Larrosa, Christophe Lecoutre and Christian Schulte, the members of the best paper subcommittee, who accepted the intensive task of reading all candidate papers. Thank you to the reviewers, who carefully read and proposed improvements to the four papers published here. Finally, thank you to the authors, who worked hard to address the recommendations of the reviewers for improving even more their excellent papers.

The Seventeenth International Conference on Automated Planning and Scheduling (ICAPS‐07)

The Seventeenth International Conference on Automated Planning and Scheduling (ICAPS-07) was held in Providence, Rhode Island in September 2007. It covered the latest theoretical and practical advances in planning and scheduling. The conference was co-located with the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP-07). The program consisted of tutorials, workshops, system demonstrations, a doctoral consortium, and three days of technical presentations mostly in parallel sessions. ICAPS-07 also hosted the second edition of the International Competition on Knowledge Engineering for Planning and Scheduling. This report describes the conference in more detail.

A simulated annealing and hill-climbing algorithm for the traveling tournament problem

The Traveling Tournament Problem (TTP) [E. Easton, G. Nemhauser, M. Trick, The traveling tournament problem description and benchmarks, in: Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming, CP 2001, 2001, pp. 580–584; M. Trick, Challenge Traveling Tournament Problems, 2004] schedules a double round-robin tournament to minimize the total distance traveled by competing teams. It involves issues of feasibility and optimality and is a challenge to constraint and integer programming. In this work, we divide the search space and use simulated annealing (SA) to search a timetable space and hill-climbing to explore a team assignment space. The SA component mutates timetables using conditional local jumps to find timetables which lead to better schedules while hill-climbing is enhanced by pre-computation and dynamic cost updating to provide fast and efficient search. Computational experiments using this hybrid approach on benchmark sets give results comparable to or better than current best known solutions.

Introduction to the Special Issue on Principles and Practice of Constraint Programming (CP 2005)

The Eleventh International Conference on the Principles and Practice of Constraint Programming (CP 2005) was held in Sitges (Barcelona), Spain, October 1–5, 2005. We are pleased to dedicate this special issue, arising from CP 2005, to Eugene C. Freuder. At the conference a session was devoted to honouring Gene on the occasion of his 60th birthday. Several guests gave short statements of both a professional and personal nature, while others sent messages that were read out on their behalf. In addition to ourselves, Professor David Waltz (Columbia University, USA) was present to deliver a very personal perspective on Gene’s impact on him and on the field of constraints. Other statements were read from Professor Boi Faltings (Swiss Federal Institute of Technology, Switzerland), Professor Alan Mackworth (University of British Columbia, Canada) and Professor Patrick Winston (Massachusetts Institute of Technology, USA). In this issue we have included editorials from both David Waltz and Alan Mackworth. A highlight of the session was the presentation to Gene of the first Research Excellence Award of the Association for Constraint Programming for Ba program of pioneering and sustained research in constraint programming of consistent high quality yielding many substantial and significant results.’’ Professor Francesca Rossi (University of Padova, Italy), the current President of the Association of Constraint Programming, presented the award. There is no more deserving recipient of this award than Gene, whose career has been an inspiration to many constraints researchers. Gene received his B.A. in Mathematics from Harvard in 1967 and his Ph.D. in Computer Science from the Massachusetts Institute of Technology in 1975 under the supervision of Professor Patrick Winston, and spent most of his academic career Constraints (2006) 11: 83–84 DOI 10.1007/s10601-006-8055-z

A general heuristic for vehicle routing problems

We present a unified heuristic which is able to solve five different variants of the vehicle routing problem: the vehicle routing problem with time windows (VRPTW), the capacitated vehicle routing problem (CVRP), the multi-depot vehicle routing problem (MDVRP), the site-dependent vehicle routing problem (SDVRP) and the open vehicle routing problem (OVRP). All problem variants are transformed into a rich pickup and delivery model and solved using the adaptive large neighborhood search (ALNS) framework presented in Ropke and Pisinger [An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, to appear]. The ALNS framework is an extension of the large neighborhood search framework by Shaw [Using constraint programming and local search methods to solve vehicle routing problems. In: CP-98, Fourth international conference on principles and practice of constraint programming, Lecture notes in computer science, vol. 1520, 1998. p. 417–31] with an adaptive layer. This layer adaptively chooses among a number of insertion and removal heuristics to intensify and diversify the search. The presented approach has a number of advantages: it provides solutions of very high quality, the algorithm is robust, and to some extent self-calibrating. Moreover, the unified model allows the dispatcher to mix various variants of VRP problems for individual customers or vehicles. As we believe that the ALNS framework can be applied to a large number of tightly constrained optimization problems, a general description of the framework is given, and it is discussed how the various components can be designed in a particular setting. The paper is concluded with a computational study, in which the five different variants of the vehicle routing problem are considered on standard benchmark tests from the literature. The outcome of the tests is promising as the algorithm is able to improve 183 best known solutions out of 486 benchmark tests. The heuristic has also shown promising results for a large class of vehicle routing problems with backhauls as demonstrated in Ropke and Pisinger [A unified heuristic for a large class of vehicle routing problems with backhauls. European Journal of Operational Research, 2004, to appear].

Constraint Solving in Uncertain and Dynamic Environments: A Survey

This article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 (Ninth International Conference on Principles and Practice of Constraint Programming) in Kinsale, Ireland (Verfaillie, G., & Jussien, N. (2003). It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments.

Introduction to the Special Issue on Principles and Practice of Constraint Programming

Introduction to the Special Issue on Principles and Practice of Constraint Programming

Introduction to the Special Issue on Principles and Practice of Constraint Programming

Introduction to the Special Issue on Principles and Practice of Constraint Programming

Preface

The workshop will be held in conjunction with the www.di.unipi.it/cp98/, Fourth International Conference on Principles and Practice of Constraint Programming (CP98), October 26 - 30, 1998 This workshop will explore hybrid algorithms for complex optimisation problems. These will be based on the integration of different approaches, viz. constraint programming, mathematical programming and stochastic repair techniques. These approaches have sometimes been perceived as competitors, but more recently they have come to be seen as complementary. The need for such algorithms in solving optimisation problems arises from the fact that different techniques are suited to solving different aspects of the problem. Among the key techniques are constraint propagation, Lagrangean relaxation, linear programming, randomised search and local optimisation. Constraint propagation is ideal for customising an algorithm with domain-dependent constraints. Lagrangean relaxation is the technique of choice for obtaining lower bounds in a branch and bound approach. Linear programming is the only technique that can solve to optimiality large linear problems. Randomised search is necessary for problems that are too large for branch and bound. Local optimisation is the preferred technique for problems that have a specific neighbourhood structure, such as routing problems. Many optimisation problems are hybrid, embracing several different requirements. For example real-life dispatching problems involve both matching, routing and scheduling. For such problems there is a clear need to mix techniques. This mixing can take different forms. On one hand generic methods, such as tabu search or constraint propagation can be significantly enhanced by importing techniques developed for branch and bound (such as edge-finding) or for local optimisation (such as neighbourhood structure). On the other hand different techniques can be applied at different stages of the search. For instance initial solutions can be found using constructive search with constraint propagation, and the solution can then be improved through local optimisation. Mark Wallace IC-Parc William Penney Laboratory Imperial College London SW7 2BZ UK Tel: (+44)171.594834 Fax: (+44)171.5948432 [email protected] Yves Caseau Bouygues Challenger, 1 Av Eugene Freyssinet, 78061 St-Quentin-Yvelines Cedex FRANCE Tel: (+33)1 30 60 51 57 Fax: (+33)1 30 60 27 27 [email protected] Eric Jacquet-Lagreze EuroDecision Helmut Simonis Cosytec Gilles Pesant Centre for Research on Transportation, Univ. Montreal Additional information will be available at the CP98 web site: http://www.di.unipi.it/cp98/

Principles and practice of constraint programming: The Newport papers

Part 1 Concurrency: a concurrent semantics for concurrent constraint programmes via contextual nets, Ugo Montanari and Francesca Rossi object-oriented concurrent constraint programming in Oz, Martin Henz et al. Part 2 Reactive systems: constraint programming in constraint nets, Ying Zhang and Alan K. Mackworth robot programming and constraints, Dinesh K. Pai constraint logic programming - hybrid control, logic as linear programming, Wolf Kohn et al. Part 3 Languages and environments: 2LP - linear programming and logic programming, Ken McAloon and Carol Tretkoff a constraint-based scientific programming language, Richard Zippel designing constraint logic programming langauges using computational systems, Claude Kirchner et al aggregation in constraint databases, Gabriel M. Kuper synthesis on constraint algorithms, Douglas R. Smith and Stephen J Westfold exploiting constraint dependency information for debugging and explanation, Walid T. Keirouz et al constraining the structure and style of object-oriented programmes, Scott Meyers et al. Part 4 Constraint-solving algorithms: an approach for solving systems of parametric polynomial equations, Deepak Kapur Fourier's elimination - which to choose?, Jean-Louis Imbert verifying logic circuits by benders decomposition, J.N. Hooker and H. Yan an incremental hierarchical constraint solver, Francisco Menezes and Pedro Barahona. Part 5 Artificial intelligence: a disjunctive decomposition control schema for constraint satisfaction, Eugene C. Freuder and Paul D. Hubbe local consistency in parallel constraint-satisfaction networks, Simon Kasif and Arthur L. Delcher terminological reasoning with constraint handling rules, Thom Fruhwirth and Philipp Hanschke. Part 6 Computer graphics: the SkyBlue constraint solver with its applications, Michael Sanella practical issues in graphical constraints, Michael Gleicher constraint management in a declarative design method for 3D scene sketch modelling, Stephane Donikian and Gerard Hegron expressing constraints for data display specification - a visual approach, Isabel F. Cruz.