Y Su Aplicación a Problemas Reales De La Industria

Total Page:16

File Type:pdf, Size:1020Kb

Y Su Aplicación a Problemas Reales De La Industria MejorA DE LA EfiCIENCIA DE resolución DEL SISTEMA TOY(FD) Y SU aplicación A PROBLEMAS REALES DE LA INDUSTRIA TESIS DoctorAL Ignacio CastiñeirAS Pérez Directores Fernando Sáenz Pérez FrANCISCO Javier López FrAGUAS Universidad Complutense DE Madrid Facultad DE Informática Departamento DE Sistemas Informáticos Y Computación Madrid, MARZO DE 2014 Improving THE Solving EffiCIENCY OF TOY(FD) AND ITS Application TO Real-Life Problems PhD Thesis Ignacio CastiñeirAS Pérez Advisors Fernando Sáenz Pérez FrANCISCO Javier López FrAGUAS Complutense Univesity OF Madrid Faculty OF Information TECHNOLOGY AND Computer Science Departament OF Information Systems AND Computing Madrid, March OF 2014 MejorA DE LA EfiCIENCIA DE resolución DEL SISTEMA TOY(FD) Y SU aplicación A PROBLEMAS REALES DE LA INDUSTRIA Improving THE Solving EffiCIENCY OF TOY(FD) AND ITS Application TO Real-Life Problems Ignacio CastiñeirAS Pérez TESIS DOCTORal, CON título “MejorA DE LA EfiCIENCIA DE resolución DEL SISTEMA TOY(FD) Y SU aplicación A PROBLEMAS REALES DE LA INDUSTRIA”, PRESENTADA POR Ignacio CastiñeirAS Pérez PARA LA obtención DEL título DE Doctor POR LA Universi- DAD Complutense DE Madrid. Título: MejorA DE LA EfiCIENCIA DE resolución DEL SISTEMA TOY(FD) Y SU aplicación A PROBLEMAS REALES DE LA INDUSTRIA Autor: Ignacio CastiñeirAS Pérez ([email protected]) Directores: Fernando Sáenz Pérez ([email protected]) FrANCISCO Javier López FrAGUAS ([email protected]) Universidad Complutense DE Madrid, Facultad DE Informática, Departamento DE Sistemas Informáticos Y Computación Madrid, MARZO DE 2014 PhD Thesis, WITH TITLE “Improving THE Solving EffiCIENCY OF TOY(FD) AND ITS Ap- PLICATION TO Real-Life Problems”, PRESENTED BY Ignacio CastiñeirAS Pérez FOR ob- TAINING A PhD AT THE Complutense University OF Madrid. Títle: Improving THE Solving EffiCIENCY OF TOY(FD) AND ITS Application TO Real-Life Problems Author: Ignacio CastiñeirAS Pérez ([email protected]) Advisors: Fernando Sáenz Pérez ([email protected]) FrANCISCO Javier López FrAGUAS ([email protected]) Complutense Univesity OF Madrid, Faculty OF Information TECHNOLOGY AND Com- PUTER Science, Departament OF Information Systems AND Computing Madrid, March OF 2014 - As YOU requested, WE TOOK SOME fiBRE SAMPLES FROM NordberG’S jacket. Use THIS MICROSCOPE TO TAKE A LOOK FOR yourself. - I CAN’T SEE anything. - Use YOUR OPEN eye, Frank. - Yeah, I CAN SEE IT now. Lab MANAGER TED Olsen (Ed Williams), CAPTAIN Ed Hocken (GeorGE Kennedy) AND LIEUTENANT FrANK Drebin (Leslie Nielsen). The Naked Gun, 1988. Acknowledgements The DAY I JOINED THE DeclarATIVE ProgrAMMING Group (GPD) FOR STARTING THE MASTER PROGRAM IT WAS MY 24th birthday, AND THE DAY I AM fiNISHING THIS PhD THESIS IS THE 23rd BIRTHDAY OF MY sister. If I WANTED TO ENCOURAGE HER FOR JOINING A post-grADUATE PROGRam, I WOULD NOT KNOW WHAT TO say. Research has, AS ANY OTHER discipline, SEVERAL ADVANTAGES AND DRawbacks. But, IF I THINK back, IT HAS GIVEN ME TWO MAYOR points, WHICH I WANT TO POINT out. I STARTED THE post-grADUATE FOR THE SAKE OF BECOMING A teacher. During THESE YEARS I HAVE HAD THE OPPORTUNITY OF HELPING PROFESSORS ON LAB subjects, AND I HAVE ENJOYED IT A lot. But, BESIDES ANY CONCRETE CONCEPT I TAUGHT STUDENTS (which I HOPE THEY understood), RESEARCH HAS TAUGHT ME TO ASK MYSELF FOR THE WHY OF THE things. That is, TO ZOOM OUT FROM THE CONCRETE TOPIC WE WERE WORKING on, AND START ASKING MYSELF QUESTIONS like: Why ARE WE WORKING ON this? Are WE THE fiRST ONES WORKING ON this? What DO THE OTHERS do, WHAT DO THEY ADD TO THE topic? What INTERACTIONS CAN BE DONE FROM OUR approaches? Can WE fiND NEW WAYS FOR COLLABORATING AND START NEW APPROACHES together? This VALUABLE WAY OF THINKING WILL BE VERY USEFUL TO ME FOR THE REST OF MY life, WHATEVER I DO FOR living. Thus, THANKS FOR that. The SECOND MAYOR POINT IS RELATED TO PUTTING MY BODY IN ANOTHER PART OF THE globe. Although Madrid IS A NICE PLACE FOR MEETING PEOPLE FROM OTHER countries, LEARN ABOUT THEIR culture, WHAT DO THEY like/dislike, THE WAY THEY think, etc., THE TRUTH IS I DID NOT fiND MYSELF IN SUCH AS MUCH INTERACTION AS I WOULD HAVE wanted. Research HAS ALLOWED ME TO FULLY EXPERIENCE THE NOTION OF THE Global Village, VIA CONFERENCES AND SUMMER SCHOOLS WHERE I INTERACTED WITH PEOPLE FROM NEARLY everywhere. Also, IT ALLOWED ME TO LIVE IN Ireland FOR NINE months, IMMERSING MYSELF COMPLETELY IN THE DAILY LIFE OF THE country, THE Cork CITY AND THE Cork ConstrAINT Computation Centre (4C) I WAS WORKING for. I WANT TO SAY THAT THIS HAS BEEN ONE OF THE BEST EXPERIENCES IN MY life, AND IT ALLOWS ME TO LINK WITH THE fiNAL IDEA I AM TRYING TO express. When YOU KNOW YOU LIKE A, YOU DO it, YOU ENJOY IT AND YOU ARE happy. Good. But, THERE ARE OTHER THINGS B, C, D, etc., THAT YOU WOULD ALSO like, AND YOU DO NOT EVEN KNOW them. Moreover, YOU WILL PROBABLY NOT BE AVAILABLE TO DISCOVER THEM BY yourself. It WAS RESEARCH WHICH PLACED ME IN A FOREIGN GROUP AND country, MAKING ME DISCOVERING THAT THIS WAS AN EXPERIENCE REALLY MAKING ME happy. Thus, THANKS FOR that. By NOW I HAVE FOCUSED IN THE THINGS I WANT TO SAY THANKS for. However, IT WAS NOT RESEARCH, BUT CONCRETE people, THE ONES MAKING THESE THINGS possible. I WOULD LIKE TO START ACKNOWLEDGING Paco. WE CAN SAY THAT MY post-grADUATE STORY STARTED WITH THE SUBJECT Computability AND Complexity, WHERE HE WAS MY professor. I FOUND AMAZING THE MATERIAL HE proposed, BUT SPECIALLY THE WAY HE MADE US think/reason ABOUT it. Further, I DISCOVERED that, BESIDES A GREAT pro- fessor, HE IS ALSO A GREAT RESEARCHER (head OF THE GPD). I WANT TO THANK HIM FOR OFFERING ME JOINING THE group, FOR TRUSTING IN MY CAPABILITIES and, IN summary, FOR GIVING ME THE OPPORTUNITY FOR DOING THE MASTER AND THE thesis. Finally, I WANT TO THANK HIM for, EVEN WITH SUCH A TIGHT agenda, ALWAYS BEING IN CHARGE OF ENSURING THAT EVERYTHING WAS OK WITH me. I MET Fernando WHEN I JOINED THE GPD, AND HE HAS BEEN MY ADVISOR DURING ALL THESE post-grADUATE years. As WE HAVE HAD A DAILY RELATIONSHIP SINCE then, I APPRECIATE HIM SO much, BOTH FROM A PERSONAL AND A PROFESSIONAL POINT OF view, SO I fiND DIffiCULT NOT TO MIX THE TWO THINGS now. I WANT TO THANK HIM FOR intro- DUCING ME TO THE KNOWLEDGE AREA OF ConstrAINT Functional Logic ProgrAMMING OVER Finite Domains. He HAS GUIDED ME DURING THE ENTIRE RESEARCH process, AT ANY point, AT ANY moment. He HAS SHOWN ME HOW TO CARRY OUT A project, AND HOW TO ACCOMPLISH IT ON TIME AND QUALITY BY APPLYING patience, COMMON SENSE AND HARD work. I WANT TO THANK HIM FOR HIS POSITIVE ATtitude, HIS ADVICES AND FOR BEING A RIGHT person. During ALL THESE years, AND SPECIALLY WHEN THE THINGS TURNED MORE DIfficult, I HAVE FELT HIS CONfiDENCE ON ME TO OVERCOME THE DIfficul- TIES AND ACHIEVE THE PROPOSED goals. In summary, I FEEL VERY HAPPY OF HAVING HAD THE OPPORTUNITY OF WORKING WITH him. All I SAID FOR Fernando IS ALSO APPLICABLE TO Teresa, WHO I MET AS WELL WHEN I JOINED THE GPD. Although SHE RETIRED A COUPLE OF YEARS ago, SHE HAS BEEN CRUCIAL IN THE DEVELOPMENT OF MY MASTER AND IN THE EARLY STAGES OF THIS thesis. I WANT TO THANK HER FOR TEACHING ME THE BASICS OF researching, GUIDING ME TO BRIDGE THE GAP BETWEEN under-grADUATE AND post-graduate. Looking BACK now, I AM TOTALLY SURE that, WITHOUT HER ADVISES AND support, I WOULD HAVE NOT ACHIEVED it. Enrique, Adrián, Manu, Juan, Carlos, Roberto, Pablo, Miky, Edison, Kike, Diego, J. Fran, Javier, Alaeddin, Daniele, TAMA AND VicenzO HAVE BEEN MY col- LEAGUES AT THE LAB Office. I WANT TO THANK THEM FOR THE GREAT ATMOSPHERE WE HAVE HAD ALL THIS time, FOR BEING SUCH GREAT GUYS AND FOR TURNING HARD DAYS INTO BETTER ones. I MET Barry IN THE CP Summer School OF 2008, WHERE HE WAS THE PROFESSOR OF SOME lectures. I ALSO MET THERE PhD STUDENTS EITHER DOING THEIR THESIS AT THE 4C, OR GOING THERE AS visitors. All OF THEM TOLD ME GOOD THINGS ABOUT THE group, THE RESEARCH TOPICS THEY WERE WORKING AT AND THE WAY THEY WERE treated. Thus, AS THE PhD SCHOLARSHIP I HAD AT THE GPD ALLOWED ME FOR MOVING ABROAD AS A visitor, I APPLIED TO THE 4C. I HAVE VISITED THEM IN 2011 AND 2013. In BOTH cases, Barry HAS BEEN MY supervisor. I WANT TO THANK HIM FOR ALLOWING ME TO JOIN ONE OF THE RESEARCH LINES OF THE group. For me, IT HAS BEEN AMAZING TO BE ADVISED BY him, TO JUMP INTO A NEW RESEARCH TOPIC AND TO BE ABLE TO MAKE SOME progress, AS TO CONTRIBUTE something. Besides that, I HEREBY CONfiRM ALL THE GOOD feed- BACK I RECEIVED FROM THE 4C, AND I WOULD ENCOURAGE OTHER PhD STUDENTS TO VISIT THE group, AS IT WAS SUCH A GREAT EXPERIENCE TO me. In particular, BESIDES Barry, I ALSO WANT TO THANK TO ALL MY COLLEAGUES AT THE LAB Office, FOR TREATING ME incred- IBLY WELL AND MAKE ME FEEL AT home. Thanks TO Diarmuid, Milan, Alex, Barry, Luis, Yuri, Yulia, Vincent, Massimo, Georgiana, Óscar, Giovanni, Lars, Mohamed, Deepak AND Padraig. Finally, I WOULD LIKE TO ACKNOWLEDGE THIS THESIS TO ALL MY FAMILY AND friends, FOR MAKING ME A HAPPY person. In particular, I WOULD LIKE TO THANK MY parents, Pilar AND Antonio, FOR ALL THEIR LOVE AND support, AND MY sister, Alicia, FOR THE SAME reasons, AND ALSO FOR HELPING ME WITH THE START OF THIS SECTION :-) Contents AbstrACT .
Recommended publications
  • Constraint Programming
    Constraint Programming Mikael Z. Lagerkvist Tomologic October 2015 Mikael Z. Lagerkvist (Tomologic) Constraint Programming October 2015 1 / 42 [flickr.com/photos/santos/] Who am I? Mikael Zayenz Lagerkvist Basic education at KTH 2000-2005 I Datateknik PhD studies at KTH 2005-2010 I Research in constraint programming systems I One of three core developers for Gecode, fast and well-known Constraint Programming (CP) system. http://www.gecode.org Senior developer R&D at Tomologic I Optimization systems for sheet metal cutting I Constraint programming for some tasks Mikael Z. Lagerkvist (Tomologic) Constraint Programming October 2015 3 / 42 Mikael Z. Lagerkvist (Tomologic) Constraint Programming October 2015 4 / 42 Mikael Z. Lagerkvist (Tomologic) Constraint Programming October 2015 5 / 42 Tomologic Mostly custom algorithms and heuristics Part of system implemented using CP at one point I Laser Cutting Path Planning Using CP Principles and Practice of Constraint Programming 2013 M. Z. Lagerkvist, M. Nordkvist, M. Rattfeldt Some sub-problems solved using CP I Ordering problems with side constraints I Some covering problems Mikael Z. Lagerkvist (Tomologic) Constraint Programming October 2015 6 / 42 1 Introduction 2 Sudoku example 3 Solving Sudoku with CP 4 Constraint programming basics 5 Constraint programming in perspective Constraint programming evaluation Constraint programming alternatives 6 Summary Mikael Z. Lagerkvist (Tomologic) Constraint Programming October 2015 7 / 42 Sudoku - The Rules Each square gets one value between 1 and 9 Each row has all values different Each column has all values different Each square has all values different Mikael Z. Lagerkvist (Tomologic) Constraint Programming October 2015 7 / 42 Sudoku - Example 3 6 1 9 7 5 8 9 2 8 7 4 3 6 1 2 8 9 4 5 1 Mikael Z.
    [Show full text]
  • A Stochastic Continuous Optimization Backend for Minizinc with Applications to Geometrical Placement Problems Thierry Martinez, François Fages, Abder Aggoun
    A Stochastic Continuous Optimization Backend for MiniZinc with Applications to Geometrical Placement Problems Thierry Martinez, François Fages, Abder Aggoun To cite this version: Thierry Martinez, François Fages, Abder Aggoun. A Stochastic Continuous Optimization Backend for MiniZinc with Applications to Geometrical Placement Problems. Proceedings of the 13th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming, CPAIOR’16, May 2016, Banff, Canada. pp.262-278, 10.1007/978-3-319-33954-2_19. hal-01378468 HAL Id: hal-01378468 https://hal.archives-ouvertes.fr/hal-01378468 Submitted on 30 Nov 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. A Stochastic Continuous Optimization Backend for MiniZinc with Applications to Geometrical Placement Problems Thierry Martinez1 and Fran¸cois Fages1 and Abder Aggoun2 1 Inria Paris-Rocquencourt, Team Lifeware, France 2 KLS-Optim, France Abstract. MiniZinc is a solver-independent constraint modeling lan- guage which is increasingly used in the constraint programming com- munity. It can be used to compare different solvers which are currently based on either Constraint Programming, Boolean satisfiability, Mixed Integer Linear Programming, and recently Local Search. In this paper we present a stochastic continuous optimization backend for MiniZinc mod- els over real numbers.
    [Show full text]
  • Model-Based Optimization for Effective and Reliable Decision-Making
    Model-Based Optimization for Effective and Reliable Decision-Making Robert Fourer [email protected] AMPL Optimization Inc. www.ampl.com — +1 773-336-2675 DecisionCAMP Bolzano, Italy — 18 September 2019 Model-Based Optimization DecisionCAMP — 18 September 2019 1 Model-Based Optimization for Effective and Reliable Decision-Making Optimization originated as an advanced between the human modeler’s formulation mathematical technique, but it has become an and the solver software’s needs. This talk accessible and widely used decision-making introduces model-based optimization by tool. A key factor in the spread of successful contrasting it to a method-based approach optimization applications has been the that relies on customized implementation of adoption of a model-based approach: A rules and algorithms. Model-based domain expert or operations analyst focuses implementations are illustrated using the on modeling the problem of interest, while AMPL modeling language and popular the computation of a solution is left to solvers. The presentation concludes by general-purpose, off-the-shelf solvers; surveying the variety of modeling languages powerful yet intuitive modeling software and solvers available for model-based manages the difficulties of translating optimization today. Dr. Fourer has over 40 years’ experience in institutes, and corporations worldwide; he is studying, creating, and applying large-scale also author of a popular book on AMPL. optimization software. In collaboration with Additionally, he has been a key contributor to the colleagues in Computing Science Research at NEOS Server project and other efforts to make Bell Laboratories, he initiated the design and optimization services available over the Internet, development of AMPL, which has become one and has supported development of open-source of the most widely used software systems for software for operations research through his modeling and analyzing optimization problems, service on the board of the COIN-OR with users in hundreds of universities, research Foundation.
    [Show full text]
  • Theory and Practice of Constraint Programming September 24-28, 2012
    Association for Constraint Programming ACP Summer School 2012 Theory and Practice of Constraint Programming September 24-28, 2012 Institute of Computer Science University of Wrocław Poland Association for Constraint Programming ACP Summer School 2012 Theory and Practice of Constraint Programming September 24-28, 2012 Institute of Computer Science University of Wrocław Poland Table of contents Introduction to constraint programming Willem-Jan van Hoeve Operations Research Techniques in Constraint Programming Willem-Jan van Hoewe Constraint Programming and Biology Agostino Dovier Constraints and Complexity Peter Jeavons Set Constraints Witold Charatonik Constraints: Counting and Approximation Andrei Bulatov Introduction to Constraint Programming Willem-Jan van Hoeve Tepper School of Business, Carnegie Mellon University ACP Summer School on Theory and Practice of Constraint Programming September 24-28, 2012, Wrocław, Poland Outline Constraint Programming Overview General introduction • Successful applications Artificial Operations Computer Intelligence Research Science • Modeling • Solving • CP software optimization search algorithms data structures Basic concepts logical inference formal languages • Search Constraint • Constraint propagation Programming • Complexity Evolution events of CP Successful applications 1970s: Image processing applications in AI; Search+qualitative inference 1980s: Logic Programming (Prolog); Search + logical inference 1989: CHIP System; Constraint Logic Programming 1990s: Constraint Programming; Industrial Solvers
    [Show full text]
  • A Satisfiability Modulo Theories Approach to Constraint Programming
    A SATISFIABILITY MODULO THEORIES APPROACH TO CONSTRAINT PROGRAMMING Josep SUY FRANCH Dipòsit legal: GI. 150-2013 http://hdl.handle.net/10803/98302 A satisfiability modulo theories approach to constraint programming està subjecte a una llicència de Reconeixement 3.0 No adaptada de Creative Commons ©2013, Josep Suy Franch PHDTHESIS A Satisfiability Modulo Theories Approach to Constraint Programming Author: Josep SUY FRANCH 2012 Programa de Doctorat en Tecnologia Advisors: Dr. Miquel BOFILL ARASA Dr. Mateu VILLARET AUSELLE Memoria` presentada per optar al t´ıtol de doctor per la Universitat de Girona Abstract Satisfiability Modulo Theories (SMT) is an active research area mainly focused on for- mal verification of software and hardware. The SMT problem is the problem of deter- mining the satisfiability of ground logical formulas with respect to background theories expressed in classical first-order logic with equality. Examples of theories include linear real or integer arithmetic, arrays, bit vectors, uninterpreted functions, etc., or combina- tions of them. Modern SMT solvers integrate a Boolean satisfiability (SAT) solver with specialized solvers for a set of literals belonging to each theory. On the other hand, Constraint Programming (CP) is a programming paradigm de- voted to solve Constraint Satisfaction Problems (CSP). In a CSP, relations between vari- ables are stated in the form of constraints. Each constraint restricts the combination of values that a set of variables may take simultaneously. The constraints are stated over specific domains, typically: Booleans, integers, rationals, reals, finite domains, or com- binations of them. The problem consists in finding an assignment to the variables that satisfy all constraints.
    [Show full text]
  • Introduction to Constraint Programming Outline Constraint
    Introduction to Constraint Programming Willem-Jan van Hoeve Tepper School of Business, Carnegie Mellon University ACP Summer School on Theory and Practice of Constraint Programming September 24-28, 2012, Wrocław, Poland Outline General introduction • Successful applications • Modeling • Solving • CP software Basic concepts • Search • Constraint propagation • Complexity Constraint Programming Overview Artificial Operations Computer Intelligence Research Science optimization search algorithms data structures logical inference formal languages Constraint Programming 1 Evolution events of CP 1970s: Image processing applications in AI; Search+qualitative inference 1980s: Logic Programming (Prolog); Search + logical inference 1989: CHIP System; Constraint Logic Programming 1990s: Constraint Programming; Industrial Solvers (ILOG, Eclipse,…) 1994: Advanced inference for alldifferent and resource scheduling 2000s: Global constraints; integrated methods; modeling languages 2006: CISCO Systems acquires Eclipse CLP solver 2009: IBM acquires ILOG CP Solver & Cplex Successful applications Sport Scheduling Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Period 1 0 vs1 0 vs2 4 vs7 3 vs6 3 vs 7 1 vs 5 2 vs4 Period 2 2 vs3 1 vs7 0 vs3 5 vs7 1 vs4 0 vs6 5 vs 6 Period 3 4 vs 5 3 vs 5 1 vs6 0 vs4 2 vs6 2 vs7 0 vs7 Period 4 6 vs 7 4 vs 6 2 vs 5 1 vs 2 0 vs 5 3 vs 4 1 vs 3 Schedule of 1997/1998 ACC basketball league (9 teams) • various complicated side constraints • all 179 solutions were found in 24h using enumeration and integer linear programming [Nemhauser
    [Show full text]
  • Constraint Programming
    Constraint Programming Justin Pearson Uppsala University 1st July 2016 Special thanks to Pierre Flener, Joseph Scott and Jun He CP MiniZinc Strings Other Final Outline 1 Introduction to Constraint Programming (CP) 2 Introduction to MiniZinc 3 Constraint Programming with Strings 4 Some recent, and not so Recent Advances 5 Final Thoughts Uppsala University Justin Pearson Constraint Programming CP MiniZinc Strings Other Final Constraint Programming in a Nutshell Slogan of CP Constraint Program = Model [ + Search ] CP provides: high level declarative modelling abstractions, a framework to separate search from from modelling. Search proceeds by intelligent backtracking with intelligent inference called propagation that is guided by high-level modelling constructs called global constraints. Uppsala University Justin Pearson Constraint Programming CP MiniZinc Strings Other Final Outline 1 Introduction to Constraint Programming (CP) Modelling Propagation Systematic Search History of CP 2 Introduction to MiniZinc 3 Constraint Programming with Strings 4 Some recent, and not so Recent Advances 5 Final Thoughts Uppsala University Justin Pearson Constraint Programming CP MiniZinc Strings Other Final Constraint-Based Modelling Example (Port tasting, PT) Alva Dan Eva Jim Leo Mia Ulla 2011 2003 2000 1994 1977 1970 1966 Constraints to be satisfied: Equal jury size: Every wine is evaluated by 3 judges. Equal drinking load: Every judge evaluates 3 wines. Fairness: Every port pair has 1 judge in common. Uppsala University Justin Pearson Constraint Programming CP MiniZinc Strings Other Final Constraint-Based Modelling Example (Port tasting, PT) Alva Dan Eva Jim Leo Mia Ulla 2011 3 3 3 2003 3 3 3 2000 3 3 3 1994 3 3 3 1977 3 3 3 1970 3 3 3 1966 3 3 3 Constraints to be satisfied: Equal jury size: Every wine is evaluated by 3 judges.
    [Show full text]
  • A GPU Implementation of Large Neighborhood Search for Solving Constraint Optimization Problems
    A GPU Implementation of Large Neighborhood Search for Solving Constraint Optimization Problems F. Campeotto and A. Dovier and F. Fioretto and E. Pontelli 1 Abstract. Constraint programming has gained prominence as an Processing Units—GPGPUs). Several libraries and programming en- effective and declarative paradigm for modeling and solving com- vironments (e.g., the Compute Unified Device Architecture (CUDA) plex combinatorial problems. In particular, techniques based on lo- created by NVIDIA) have been made available to allow programmers cal search have proved practical to solve real-world problems, pro- to access GPGPUs and exploit their computational power. viding a good compromise between optimality and efficiency. In In this paper, we propose the design and implementation of a spite of the natural presence of concurrency, there has been rela- novel constraint solver that exploits parallel Local Search (LS) us- tively limited effort to use novel massively parallel architectures— ing GPGPU architectures to solve constraint optimization problems. such as those found in modern Graphical Processing Units (GPUs)— The optimization process is performed in parallel on multiple large to speedup constraint programming algorithms, in general, and local promising regions of the search space, with the aim of improving the search methods, in particular. This paper describes a novel frame- quality of the current solution. The local search model pursued is a work which exploits parallelism from a popular local search method variant of Large Neighborhood Search (LNS) [18, 3]. LNS is a local (the Large Neighborhood Search method), using GPUs. search techniques characterized by an alternation of destroy and re- pair methods—used to destroy and repair part of a solution of a prob- lem.
    [Show full text]
  • Constraint Modelling and Solving of Some Verification Problems Anicet Bart
    Constraint modelling and solving of some verification problems Anicet Bart To cite this version: Anicet Bart. Constraint modelling and solving of some verification problems. Programming Languages [cs.PL]. Ecole nationale supérieure Mines-Télécom Atlantique, 2017. English. NNT : 2017IMTA0031. tel-01743851 HAL Id: tel-01743851 https://tel.archives-ouvertes.fr/tel-01743851 Submitted on 26 Mar 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Thèse de Doctorat Anicet BART Mémoire présenté en vue de l’obtention du grade de Docteur de l’École nationale supérieure Mines-Télécom Atlantique Bretagne-Pays de la Loire sous le sceau de l’Université Bretagne Loire École doctorale : Mathématiques et STIC (MathSTIC) Discipline : Informatique, section CNU 27 Unité de recherche : Laboratoire des Sciences du Numérique de Nantes (LS2N) Soutenue le 17 octobre 2017 Thèse n°: 2017IMTA0031 Constraint Modelling and Solving of some Verification Problems JURY Présidente : Mme Béatrice BÉRARD, Professeur, Université Pierre et Marie Curie, Paris-VI Rapporteurs : M. Salvador ABREU, Professeur, Universidade de Évora, Portugal Mme Béatrice BÉRARD, Professeur, Université Pierre et Marie Curie, Paris-VI Examinateurs : M. Nicolas BELDICEANU, Professeur, Institut Mines-Télécom Atlantique, Nantes M. Philippe CODOGNET, Professeur, Université Pierre et Marie Curie, Paris-VI Invité : M.
    [Show full text]
  • What Is Constraint Programming (CP)?
    ConstraintProgramming Extensions.jl An MOI/JuMP extension for constraint programming Thibaut Cuvelier — CentraleSupélec (université Paris-Saclay) What is constraint programming (CP)? ▪ Way of formulating combinatorial problems ▪ CP doesn’t really work for continuous problems (exception: Ibex, e.g.) ▪ Initial focus in CP was on feasibility, but optimisation is also possible ▪ Quite different from mathematical optimisation: ▪ No duality, no convexity, no linear/continuous relaxation, no differentiability at all ▪ More generic set of constraints, not just equations and inequalities ▪ Real focus on discrete aspect of the problem ▪ Mathematical optimisation’s support for combinatorial problems is more an afterthought ▪ CP has had many successes in operational research: ▪ Scheduling, time tabling, resource allocation An example of CP model: solving Sudokus ▪ Variables: ▪ 푑푖푗: digit for cell 푖, 푗 , a number between 1 and 9 ▪ Constraints: ▪ Known digits (hints) ▪ Each digit appears once in each row: alldifferent 푑푖푗 ∀푗 , ∀푖 ▪ Each digit appears once in each column: alldifferent 푑푖푗 ∀푖 , ∀푗 ▪ Each digit appears once in each block: 푑3푠,3푡 푑3푠,3푡+1 푑3푠,3푡+2 alldifferent 푑3푠+1,3푡 푑3푠+1,3푡+1 푑3푠+1,3푡+2 , 푠 ∈ 0,1,2 , 푡 ∈ 0,1,2 푑3푠+1,3푡 푑3푠+1,3푡+1 푑3푠+1,3푡+2 ▪ What about your typical MIP model ? ConstraintProgrammingExtensions.jl • What is the state of CP in Julia? • What is the state of the package? • What comes next? • What is missing in Julia/MOI/JuMP? • What is enabled with this package? What is the state of CP in Julia? ▪ Quite a few CP solvers
    [Show full text]
  • Thesis Structure
    UvA-DARE (Digital Academic Repository) Enabling framework for service-oriented collaborative networks Sargolzaei, M. Publication date 2018 Document Version Final published version License Other Link to publication Citation for published version (APA): Sargolzaei, M. (2018). Enabling framework for service-oriented collaborative networks. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:23 Sep 2021 Enabling Framework for Service-oriented Collaborative Networks Mahdi Sargolzaei Enabling Framework for Service-oriented Collaborative Networks Mahdi Sargolzaei Enabling Framework for Service-oriented Collaborative Networks Mahdi Sargolzaei Enabling Framework for Service-oriented Collaborative Networks Academisch Proefschrift ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof.dr. ir. K.I.J. Maex ten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op dinsdag 15 mei 2018, te 10.00 uur door Mahdi Sargolzaei geboren te Mashhad, Iran.
    [Show full text]
  • The First Constraint-Based Local Search Backend for Minizinc
    IT 14 066 Examensarbete 15 hp Oktober 2014 The First Constraint-Based Local Search Backend for MiniZinc Gustav Björdal Institutionen för informationsteknologi Department of Information Technology Abstract The First Constraint-Based Local Search Backend for MiniZinc Gustav Björdal Teknisk- naturvetenskaplig fakultet UTH-enheten MiniZinc is a modelling language used to model combinatorial optimisation and satisfaction problems, which can then be solved in a backend solver. There are many Besöksadress: different backend solvers based on different technologies such as constraint Ångströmlaboratoriet Lägerhyddsvägen 1 programming, mathematical programming, or Boolean satisfiability solving. However, Hus 4, Plan 0 there is currently no constraint-based local search (CBLS) backend. This thesis gives an overview of the design of the first CBLS backend for MiniZinc. Experimental Postadress: results show that for some relevant MiniZinc models, the CBLS backend is able to Box 536 751 21 Uppsala give high-quality results. Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student Handledare: Jean-Noël Monette Ämnesgranskare: Pierre Flener Examinator: Olle Gällmo IT 14 066 Tryckt av: Reprocentralen ITC Acknowledgements I would like to thank my supervisor Dr. Jean-No¨elMonette for the fruitful discussions throughout this project and for all of your help and feedback. I would also like to thank my reviewer Professor Pierre Flener for initially introducing me to the world of constraint programming and for being the most thorough proofreader I have ever met, all of your feedback has been greatly appreciated. Finally, I would like to thank the ASTRA group for entrusting me with this thesis project, it has been both challenging and educational, and more importantly, fun.
    [Show full text]