NordConsNet Workshop 2017
The 16th workshop of NordConsNet, the Nordic Network for researchers and practitioners of Constraint Programming
The Nordic Network for researchers and practitioners of Constraint programming (NordConsNet) kindly invites you to participate in the yearly NordConsNet Workshop. The purpose of the workshop is to learn about ongoing research in Constraint Programming, existing projects and products, and further development of the network. NordConsNet Workshop 2017 is the 16th edition of this annual event.
The workshop will take place at the Department of Information Technology, Uppsala University, on Monday 22 May 2017, in room 1311 on the third floor of house 1 at Polacksbacken.
Registration is now closed but you are still very welcome to attend any talks during the day, just be mindful not to enter during a talk and don't expect catering.
Please forward this information to anyone who might be interested in this workshop but is not yet on the NordConsNet mailing list: they can subscribe to it by applying to Justin Pearson.
The NordConsNet Workshop 2017 is sponsored by the new Optimisation Arena of Uppsala University.
|09:15-09:20||Mats Carlsson (RISE)||Welcome||ITC 1311||N/A|
|09:20-09:30||Pierre Flener (Uppsala University)||Constraint Programming in a Nutshell||ITC 1311|
|09:30-10:00||Jacopo Mauro (University of Oslo)||sunny-cp: A Multicore Tool for Constraint Solving||ITC 1311|
|10:00-10:30||Di Yuan (Uppsala University)||Optimal Scheduling in Wireless Networks: Modelling, Solution via Integer Programming, and Recent Developments||ITC 1311|
|10:30-10:45||Fika||Tea & Coffee||ITC 1311||N/A|
|10:45-11:15||Jip Dekker (Uppsala University)||Auto-Tabling for Subproblem Presolving in MiniZinc||ITC 1311|
|11:15-11:45||Jacopo Mauro (University of Oslo)||Context Aware Reconfiguration in Software Product Lines||ITC 1311|
|13:15-13:45||Leslie Sheng||VRP for Smart Municipal Solid-Waste Management||ITC 1311|
|13:45-14:15||Masoumeh Mansouri (Örebro University)||Hybrid Constraint-Based Planning for Autonomous Industrial Vehicles||ITC 1311||-|
|14:15-14:45||Maryana Wånggren (Chalmers)||Protein modelling using dynamic programming and constraints||ITC 1311|
|14:45-15:15||Fika||Tea & Coffee||ITC 1311||N/A|
|15:15-15:45||Robin Strand (Uppsala University)||Discrete Optimisation in Image Processing||ITC 1311|
|15:45-16:15||Nicolas Beldiceanu (IMT Atlantique, France)|| Handling Combinatorial Aspects of Time-Series Constraints
Based on Regular-Expression Characteristics
Submit a Presentation Proposal (closed)
We hope for your participation, and highly encourage you to submit a proposal for a presentation of your ongoing work, recent results, or of a relevant discussion topic. There are no paper submissions, reviews, or proceedings, hence recent conference/journal papers may also be presented. Please contact Mats Carlsson if you would like to present.
sunny-cp: A Multicore Tool for Constraint Solving
Jacopo Mauro (University of Oslo)
In Constraint Programming (CP), a portfolio solver uses a variety of different solvers for solving a given Constraint Satisfaction / Optimisation Problem. We introduce sunny-cp2: the first parallel CP portfolio solver that enables a dynamic, cooperative, and simultaneous execution of its solvers in a multicore setting. It incorporates state-of-the-art solvers, providing also a usable and configurable framework. Empirical results are very promising. sunny-cp2 can even outperform the performance of the oracle solver which always selects the best solver of the portfolio for a given problem.
Optimal Scheduling in Wireless Networks:
Modelling, Solution via Integer Programming, and Recent Developments
Di Yuan (Uppsala University)
Optimal scheduling is a fundamental aspect of resource sharing in wireless networks. In general, the problem consists in determining which communication links shall concurrently transmit and for how long, so as to optimise some performance metric. Concurrent transmissions generate interference to each other, giving rise to the main constraint in the optimisation problem. In this presentation, we will review the minimum-time scheduling problem in wireless networks. Specifically, we will highlight the importance of mathematical modelling for problem solving, and present solution methods for scheduling and its sub-problem by means of integer linear programming. Next, we will discuss some recent developments, with focus on extensions to scheduling with rate regions and deadline constraints. Insights from studying the extensions, in fact, generalise to optimal scheduling for serving processes in any system, where the serving rate of one process (i.e., the amount of demand completed per time unit) monotonically decreases as more processes are jointly served – a condition that holds in virtual all applications in which processes compete with each other in resource sharing.
Auto-Tabling for Subproblem Presolving in MiniZinc
Jip Dekker (Uppsala University)
A well-known and powerful constraint model reformulation is to compute the solutions to a model part, say a custom constraint predicate, and tabulate them within an extensional constraint that replaces that model part. Despite the possibility of achieving higher solving performance, this tabling reformulation is often not tried, because it is tedious to perform; further, if successful, it obfuscates the original model. In order to encourage modellers to try tabling, we extend the MiniZinc toolchain to perform the automatic tabling of suitably annotated predicate definitions, without requiring any changes to solvers, thereby eliminating both the tedium and the obfuscation.
Context Aware Reconfiguration in Software Product Lines
Jacopo Mauro (University of Oslo, Norway)
Software Product Lines (SPLs) are a mechanism to capture families of closely related software systems by modelling commonalities and variability. Although user customisation has a growing importance in software systems and is a vital sales argument, SPLs currently only allow user customisation at deploy-time. We extend the notion of context-aware SPLs by means of user profiles, containing a linearly ordered set of preferences. We present a reconfiguration engine checking the validity of the current configuration and, if necessary, reconfiguring the SPL while trying to fulfil the preferences of the active user profile.
VRP for Smart Municipal Solid-Waste Management
Sircle provides a real time data for solid waste bins fill-levels and local traffic conditions. Sircle can quantify and monitor solid waste bin fill levels ultrasonically to alert solid waste collection trucks when the bin needs to be emptied. Optimised collection routes are then identified by Sircle, which diverts solid waste collection fleets away from traffic jams and smaller roads and identifies the best routes to take. This improves the efficiency of the collection of municipal solid waste (MSW) and reduces operational costs.
Hybrid Constraint-Based Planning for Autonomous Industrial Vehicles
Masoumeh Mansouri (Örebro University)
Fleet automation often involves solving several sub-problems, including task allocation, motion planning, and coordination. The sub-problems are also strongly correlated, meaning that each imposes additional constraints on other subproblems. We propose to cast the problem of autonomous fleet management to a meta-CSP, that is, a CSP whose constraints capture the dependencies between the sub-problems in the fleet automation application. We instantiate the approach in several industrial fleet management applications, including a drill pattern planning problem in open-pit mining studied in cooperation with Atlas Copco.
Protein modelling using dynamic programming and constraints
Maryana Wånggren (Chalmers)
Molecular dynamics simulations, often combined with simulated annealing, are commonly used when calculating structural models of proteins, e.g. based on NMR experiments. However, one is often faced with limited and sometimes insufficient information for determining a well-resolved 3D structure. In addition, the type of data available for different proteins may vary: ranges for torsion angles, distance approximations, relative orientation of different molecular parts etc. We are using whatever structural information is around, together with a dynamic programming approach for searching the space of feasible conformations to rapidly determine 3D structures that are consistent with the input constraints.
We are currently investigating how sets of constraints can be used to focus the computational effort on the most important parts of the search space in order to improve runtime performance and memory usage.
Discrete Optimisation in Image Processing
Robin Strand (Uppsala University)
With the fast development of imaging hardware and the explosion of image data being collected, image processing is today used in most people’s everyday life, and it has applications in many research areas, including digital humanities, material science, medicine, biology and biomedicine.
Two of the most important, and difficult, image processing problems are image segmentation, where the goal is to partition a given image into object and background components, and registration, where the goal is to align a target image with a reference image.
Many different image segmentation and registration methods are available, spanning from fully automatic to fully interactive (guided by a user), where some are model-based and some operate directly on image intensities. A common thread among these methods is the need for reliable optimisation methods. Since the digital image data being processed is inherently discrete, discrete and combinatorial optimisation is well-suited for the task of finding optimal image segmentation and registration results.
In this talk, I will present some challenging, and hopefully interesting, problems within image processing with examples from ongoing research at the Department of Information Technology and at the Radiology Division at the Akademiska hospital, including image segmentation and registration.
Handling Combinatorial Aspects of Time-Series Constraints Based on
Nicolas Beldiceanu (IMT Atlantique, France)
After presenting an overview of our work on time-series constraints in the context of CP and LP, the presentation focusses on the introduction of the concept of regular expression characteristics as a unified way to express concisely combinatorial properties on time-series constraints. This allows going from a compositional way of defining time-series constraints to a compositional way to deal with their combinatorial aspect without developing ad-hoc methods for each time-series constraint separately.