Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems: 4th International Conference, CPAIOR 2007, Brussels, Belgium, May 23-26, 2007. Proceedings

399 Pages·2007·4.86 MB·English

by  Davaatseren Baatar

Lecture Notes in Computer Science 4510
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CarnegieMellonUniversity,Pittsburgh,PA,USA
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IndianInstituteofTechnology,Madras,India
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RiceUniversity,Houston,TX,USA
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PascalVan Hentenryck LaurenceWolsey (Eds.)
Integration
of AI and OR Techniques
in Constraint Programming
for Combinatorial
Optimization Problems
4th International Conference, CPAIOR 2007
Brussels, Belgium, May 23-26, 2007
Proceedings
1 3
VolumeEditors
PascalVanHentenryck
BrownUniversity
Box1910,Providence,RI02912,USA
E-mail:[email protected]
LaurenceWolsey
UniversitécatholiquedeLouvain
34,VoieduRomanPays,1348Louvain-la-Neuve,Belgium
E-mail:[email protected]
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Preface
This volume contains the papers presented at CP-AI-OR 2007: The Fourth In-
ternational Conference on Integration of Artificial Intelligence, Constraint Pro-
gramming,andOperationsResearchTechniquesforCombinatorialOptimization
Problems held May 23–26, 2007 in Brussels, Belgium. More information about
the conference can be found at the Web site:
http://www.cs.brown.edu/sites/cpaior07/Welcome.html.
There were 80 submissions and each submission was reviewed by at least three
Program Committee members. After careful consideration and discussion, the
committee decided to accept 28 papers (one of which was withdrawn just be-
fore the publication of the proceedings). The papers submitted this year were
of high quality and representative of a vibrant, multi-disciplinary community
at the intersection of artificial intelligence, constraint programming, and opera-
tions research.The programalso included three invited talks and two tutorials.
We were extremely pleased that Claude Le Pape, George Nemhauser, and Bart
Selman accepted our invitation to be invited speakers at the conference. Simi-
larly, we were very fortunate to attract Thierry Benoist and John Chinneck to
givetutorialsatCP-AI-OR2007.TheconferencewasalsoprecededbyaMaster
Class on constraint-basedscheduling organized by Amedeo Cesta.
We would like to thank the ProgramCommittee members who workedhard
to produce high-quality reviews for the papers under tight deadlines, as well as
the reviewers involved in the paper selection. We also would like to acknowl-
edge the contributions of Laurent Michel (Publicity Chair), Barry O’Sullivan
(Sponsorship Chair), Susanne Heipcke and Michael Juenger (Academic and In-
dustrialLiaisonChairs),andEtienneLoute(ConferenceCo-chair)tothesuccess
of CP-AI-OR 2007. It is also a great pleasure to thank Fabienne Henry for her
tremendoushelp inorganizingthe conference.The submissions,reviews,discus-
sions,andthe preparationofthe proceedingswereallhandledbytheEasyChair
system. Finally, we would also like to thank the sponsors of the conference:
TheAssociationforConstraintProgramming,theCorkConstraintComputation
Centre(Ireland),ILOGS.A.(France),theIntelligentInformationSystemsInsti-
tuteatCornellUniversity(USA),theEuropeanCommission(throughtheMarie
CurieResearchTrainingNetwork:ADONET),theFondsNationaldeRecherche
Scientifique in Belgium, the Belgian Science Policy, ORBEL (Master Class), as
wellasBrownUniversity(USA), the Facult´esSt. Louis(Brussels,Belgium)and
the Universit´e catholique de Louvain (Louvain-la-Neuve, Belgium).
March 2007 Pascal Van Hentenryck
Laurence Wolsey
Conference Organization
Program Chairs
PascalVan Hentenryck
Laurence Wolsey
Local Organization
Etienne Loute, Facult´es Universitaires de Saint-Louis, Brussels, Belgium
LaurenceWolsey,Universit´ecatholiquedeLouvain,Louvain-La-Neuve,Belgium
Program Committee
Philippe Baptiste
Chris Beck
Frederic Benhamou
Mats Carlsson
Amedeo Cesta
John Chinneck
Andrew Davenport
Rina Dechter
Yves Deville
Hani El Sakkout
Bernard Gendron
Carla Gomes
John Hooker
Stefan Karish
Andrea Lodi
Laurent Michel
Michela Milano
Yehuda Naveh
Barry O’Sullivan
Jean-Francois Puget
Jean-Charles R´egin
Andrea Roli
Louis-Martin Rousseau
Michel Rueher
Martin Savelsbergh
Meinolf Sellmann
David Shmoys
Helmut Simonis
VIII Organization
Barbara Smith
Stephen Smith
Peter Stuckey
Michael Trick
Mark Wallace
Willem-Jan van Hoeve
External Reviewers
Ionut Aron Jean-No¨el Monette
Christian Artigues Bertrand Neveu
Gregory Barlow Angelo Oddi
Marco Benedetti Gilles Pesant
John Betts Federico Pecora
Eyal Bin Lam Phuong
Pierre Bonami Nicola Policella
Sebastian Brand Jakob Puchinger
Marie-Claude Cˆot´e Luis Queseda
Emilie Danna Claude-Guy Quimper
Sophie Demassey Yossi Richter
Bistra Dilkina Ashish Sabharwal
Nizar El Hachemi Frederic Saubion
Dominique Feillet Pierre Schaus
Felix Geller Stefano Smriglio
Carmen Gervet Christine Solnon
Vibhav Gogate Peter Stuckey
Tarik Hadzic Daria Terekhov
Jin-Kao Hao Andrea Tramontani
Shlomo Hoory Gilles Trombettoni
Ayoub Insea Correa Charlotte Truchet
Manuel Iori Jeremie Vautard
Kalev Kask Petr Vilim
Serge Kruk Toby Walsh
Yahia Lebbah St´ephane Zampelli
Bernd Meyer Terry Zimmerman
Claude Michel
Table of Contents
Minimum Cardinality Matrix Decomposition into Consecutive-Ones
Matrices: CP and IP Approaches................................... 1
Davaatseren Baatar, Natashia Boland, Sebastian Brand, and
Peter J. Stuckey
Connections in Networks: Hardness of Feasibility Versus Optimality .... 16
Jon Conrad, Carla P. Gomes, Willem-Jan van Hoeve,
Ashish Sabharwal, and Jordan Suter
Modeling the Regular Constraint with Integer Programming........... 29
Marie-Claude Coˆt´e, Bernard Gendron, and Louis-Martin Rousseau
Hybrid Local Search for Constrained Financial Portfolio Selection
Problems ....................................................... 44
Luca Di Gaspero, Giacomo di Tollo, Andrea Roli, and Andrea Schaerf
The “Not-Too-HeavySpanning Tree” Constraint..................... 59
Gr´egoire Dooms and Irit Katriel
Eliminating Redundant Clauses in SAT Instances .................... 71
Olivier Fourdrinoy, E´ric Gr´egoire, Bertrand Mazure, and
Lakhdar Sa¨ıs
Cost-Bounded Binary Decision Diagrams for 0–1 Programming ........ 84
Tarik Hadˇzi´c and J.N. Hooker
YIELDS: A Yet Improved Limited Discrepancy Search for CSPs ....... 99
Wafa Karoui, Marie-Jos´e Huguet, Pierre Lopez, and Wady Naanaa
A Global Constraint for Total Weighted Completion Time ............ 112
Andr´as Kov´acs and J. Christopher Beck
Computing Tight Time Windows for RCPSPWET with the
Primal-Dual Method ............................................. 127
Andr´as K´eri and Tama´s Kis
Necessary Condition for Path Partitioning Constraints................ 141
Nicolas Beldiceanu and Xavier Lorca
A Constraint Programming Approach to the Hospitals / Residents
Problem ........................................................ 155
David F. Manlove, Gregg O’Malley,
Patrick Prosser, and Chris Unsworth
Best-First AND/OR Search for 0/1 Integer Programming ............. 171
Radu Marinescu and Rina Dechter
A Position-BasedPropagatorfor the Open-Shop Problem ............. 186
Jean-No¨el Monette, Yves Deville, and Pierre Dupont
X Table of Contents
Directional Interchangeability for Enhancing CSP Solving............. 200
Wady Naanaa
A Continuous Multi-resources cumulative Constraint with
Positive-NegativeResource Consumption-Production ................. 214
Nicolas Beldiceanu and Emmanuel Poder
Replenishment Planning for Stochastic Inventory Systems with
Shortage Cost ................................................... 229
Roberto Rossi, S. Armagan Tarim, Brahim Hnich, and
Steven Prestwich
Preprocessing Expression-Based Constraint Satisfaction Problems for
Stochastic Local Search........................................... 244
Sivan Sabato and Yehuda Naveh
The Deviation Constraint ......................................... 260
Pierre Schaus, Yves Deville, Pierre Dupont, and Jean-Charles R´egin
The LinearProgrammingPolytopeofBinaryConstraintProblemswith
Bounded Tree-Width ............................................. 275
Meinolf Sellmann, Luc Mercier, and Daniel H. Leventhal
On Boolean Functions Encodable as a Single Linear Pseudo-Boolean
Constraint ...................................................... 288
Jan-Georg Smaus
Solving a Stochastic Queueing Control Problem with Constraint
Programming.................................................... 303
Daria Terekhov and J. Christopher Beck
Constrained Clustering Via Concavity Cuts ......................... 318
Yu Xia
Bender’s Cuts Guided Large Neighborhood Search for the Traveling
Umpire Problem ................................................. 332
Michael A. Trick and Hakan Yildiz
A Large Neighborhood Search Heuristic for Graph Coloring ........... 346
Michael A. Trick and Hakan Yildiz
Generalizations of the Global Cardinality Constraint for Hierarchical
Resources....................................................... 361
Alessandro Zanarini and Gilles Pesant
A Column Generation Based Destructive Lower Bound for Resource
Constrained Project Scheduling Problems ........................... 376
J. Marjan van den Akker, Guido Diepen, and J.A. Hoogeveen
Author Index.................................................. 391
Minimum Cardinality Matrix Decomposition
into Consecutive-Ones Matrices: CP and IP
Approaches
Davaatseren Baatar1, Natashia Boland1, Sebastian Brand2,
and Peter J. Stuckey2
1 Department of Mathematics, Universityof Melbourne, Australia
2 NICTA Victoria Research Lab, Department of Comp. Sci. and Soft. Eng.
Universityof Melbourne, Australia
Abstract. We consider the problem of decomposing an integer ma-
trix into a positively weighted sum of binary matrices that have the
consecutive-ones property. This problem is well-known and of practical
relevance. It has an important application in cancer radiation therapy
treatment planning: the sequencing of multileaf collimators to deliver
a given radiation intensity matrix, representing (a component of) the
treatment plan.
Two criteria characterise the efficacy of a decomposition: the beam-
on time (length of time the radiation source is switched on during the
treatment),andthecardinality (thenumberofmachineset-upsrequired
to deliver theplanned treatment).
Minimisingtheformerisknowntobeeasy.Howeverfindingadecom-
position of minimal cardinality is NP-hard. Progress so far has largely
been restricted to heuristic algorithms, mostly using linear program-
ming, integer programming and combinatorial enumerative methods as
the solving technologies. We present a novel model, with correspond-
ing constraint programming and integerprogramming formulations. We
comparethesecomputationallywithpreviousformulations,andweshow
that constraint programming performs very well bycomparison.
1 Introduction
The problem of decomposing an integer matrix into a weighted sum of binary
matrices has received much attention in recent years, largely due to its applica-
tion in radiation treatment for cancer.
Intensity-modulated radiation therapy (IMRT) has been increasingly used for
the treatmentofa varietyofcancers[17]. This treatmentapproachemploys two
devices that allow higher doses of radiation to be administered to the tumour,
while decreasing the exposure of sensitive organs (Fig. 1). The first is that the
sourceof radiationcanbe rotatedabout the body of the patient:by positioning
the tumour at a “focal point”, and aiming the radiation beam at this point
from various angles, the tumour receives a high dose from all angles, while the
surrounding tissue only gets high exposure from some angles.
P.VanHentenryckandL.Wolsey(Eds.):CPAIOR2007,LNCS4510,pp.1–15,2007.
(cid:2)c Springer-VerlagBerlinHeidelberg2007
2 D. Baatar et al.
Target
volume 2Dtransaxialsliceofthebody
Beamhead
Organsatrisk
Fig.1. Intensity-modulatedradiotherapy
The second is more subtle, and involves repeated exposures from the same
angle,wheretheuniform-intensityrectangularfieldofradiationdeliveredbythe
radiationsourceis“shaped”inadifferentwayforeachexposure,andeachexpo-
surecanbeforadifferentlengthoftime.Thisprocessbuildsupacomplexprofile
ofreceivedradiationinthepatient’sbody,effectivelyconvertingtheuniformra-
diationfielddeliveredbythemachinetoanintensity-modulatedfield.Thelatter
isusuallydescribedbydiscretisingthe2-dimensionalrectangularfield,andspec-
ifying a radiation intensity level in each discrete element, representing the total
length of time for which that element should be exposed to radiation.
A treatment plan for a single IMRT treatment session with a patient thus
typicallyconsistsofasetofangles,togetherwithamatrixforeachangle,known
as the intensity matrix, which represents the modulated field to be delivered at
that angle. Typically the intensity is scaled so that the entries in the intensity
matrixareinteger.Indeed,they areusuallyquite smallintegers.Findingagood
treatmentplanisachallengingprobleminitsownright,andhasbeenthesubject
ofagreatdealofresearch.Werecommendthereaderrefertothepapers[13,9,15]
and references therein.
In this paper, we assume a treatment plan is given, and focus on the
deliveryofthemodulatedfield(intensitymatrix)atagivenangle.IMRT canbe