Juan Francisco Díaz

Integrating Constraints and Concurrent Objects in MusicalApplications: A Calculus and its Visual Language

We propose PiCO, a calculus integrating concurrent objects and constraints, as a base for music composition tools. In contrast with calculi such as NiehrenMueller:Free, milner.parrow.ea:calculus-mobile or TyCO vasconcelos:typed-concurrent, both constraints and objects are primitive notions in PiCO. In PiCO a base object model is extended with constraints by orthogonally adding the notion of constraint system found in the ρ-calculus OzCalculus. Concurrent processes make use of a constraint store to synchronize communications either via the ask and tell operations of the constraint model or the standard message-passing mechanism of the object model. A message delegation mechanism built into the calculus allows encoding of general forms of inheritance. This paper includes encodings in PiCO of the concepts of class and sub-class. These allow us to represent complex partially defined objects such as musical structures in a compact way. We illustrate the transparent interaction of constraints and objects by a musical example involving harmonic and temporal relations. The relationship between Cordial, a visual language for music composition applications, and its underlying model PiCO is described.

Using constraint programming for reconfiguration of electrical power distribution networks

The problem of reconfiguring power distribution systems to reduce power losses has been extensively studied because of its significant economic impact. A variety of approximation computational models have recently been proposed. We describe a constraint programming model for this problem, using the Mozart system. To handle real world reconfiguration systems we implemented and integrated into Mozart an efficient constraint propagation system for the real numbers. We show how the CP approach leads to a simpler model and allows more flexible control of reconfiguration parameters. We analyze the performance of our system in canonical distribution networks of up to 60 nodes. We describe how the adaptability of the Mozart search engine allows defining effective strategies for tackling a real distribution system reconfiguration of around 600 nodes.

An interactive tool for the controlled execution of an automated timetabling constraint engine

Here we introduce DePathos, a graphical tool for a time-tabling constraint engine (Pathos). Since the core of Pathos is text-based and provides little user-interaction, finding an appropriate solution for large problems (1000-2000 variables) can be a very time consuming process requiring the constant supervision of a constraint programming expert. DePathos uses an incremental solution strategy. Such strategy subdivides the problem and checks the consistency of the resulting subdivisions before incrementally unifying them. This has shown to be useful in finding inconsistencies and discovering over-constrained situations. Our incremental solution is based on hierarchical groupings defined at the problem domain level. This allows users to direct the timetabling engine in finding partial solutions that are meaningful in practice. We discuss the lessons learned from using Pathos in real settings, as well as the experiences of coupling DePathos to the timetabling engine.

CRE2 : A CP application for reconfiguring a power distribution network for power losses reduction

CRE2 is a CP application written in MOzArt (www.mozart-oz.org) for reconfiguring power distribution networks for power losses reduction. This includes two distinct interacting processes: load flow computation and selecting switches to open or close. Load flow is computed for each radial network obtained from switching operations. We developed a real intervals constraint system (XRI) and integrated it to MOzArt. CRE2 uses XRI for load flow and the MOzArt finite domain constraint system for switch state changes. This work was partially supported by COLCIENCIAS and EPSA, under contract No.254-2002 and by COLCIENCIAS and Parquesoft, under contract No.298-2002.

Solving the aircraft sequencing problem using concurrent constraint programming

In this paper we describe an application that solves the problem of aircraft sequencing in airports using a single runway. In this problem, the air traffic controller must compute a landing (take off) time for each plane in the horizon or airport. The cost is associated with the difference between the plane preferred time (for landing or taking off) and the time assigned to it. There is also a minimum separation time between planes that must be respected to avoid accidents. We have implemented an application using Mozart with finite domain constraints, GUIs to interact with the user, and a propagator with a simple, but very helpful operation to cut domains. The basis of the application is the engine that implements the model of the problem; it is easily extensible through the implementation of new distributors. This paper shows how the powerful features of Mozart could be exploited to implement practical applications.

Off-line and On-line Scheduling of SAT Instances with Time Processing Constraints

Many combinatorial problems (e.g., SAT) are well-known NP-complete problems. Therefore, many instances cannot be solved within a reasonable time, and the runtime varies from few seconds to hours or more depending on the instance. Cloud computing offers an interesting opportunity to solve combinatorial problems in different domains. Computational time can be rented by the hour and for a given number of processors, therefore it is extremely important to find a good balance between the number of solved instances and the requested resources in the cloud.

Constraint Programming and Machine Learning for Interactive Soccer Analysis

A soccer competition consists of n teams playing against each other in a league or tournament system, according to a single or double round-robin schedule. These competitions offer an excellent opportunity to model interesting problems related to questions that soccer fans frequently ask about their favourite teams. For instance, at some stage of the competition, fans might be interested in determining whether a given team still has chances of winning the competition (i.e., finishing first in a league or being within the first k teams in a tournament to qualify to the playoff). This problem relates to the elimination problem, which is NP-complete for the actual FIFA pointing rule system (0, 1, 3), zero point to a loss, one point to a tie, and three points to a win. In this paper, we combine constraint programming with machine learning to model a general soccer scenario in a real-time application.

Constraint programming heuristics for configuring optimal products in multi product lines

Abstract Nowadays, complex application domains require configuring multi-product lines where product features and constraints among them are specified in several variability models. These variability models are enriched with inter-model constraints representing the existing relations among domain concerns, and with non-functional properties modeled as attributes attached to product features. Multiple techniques use constraint programming to automate the cumbersome task of manually configuring a suitable product. Currently, there are some proposals to improve the performance of constraint solvers when configuring single-model product lines, however configuration scenarios with multiple interrelated and attributed models are not yet targeted. This paper proposes and evaluates three search heuristics used to configure optimal products regarding multi-objective criteria. Results are compared against the default search strategy of the Choco constraint solver. We evaluated the performance for configuring optimal products in four state-of-the-art product lines and 130 generated variability models representing multi-product lines that scale up to 6400 features and 960 constraints. As a result, we observe that the proposed heuristics perform better than the default solver strategy when the variability models scale in terms of features. In contrast, the default strategy and one of the proposed heuristics perform better as the number of interdependencies between variability models increases.

Online over time processing of combinatorial problems

In an online environment, jobs arrive over time and there is no information in advance about how many jobs are going to be processed and what their processing times are going to be. In this paper, we study the online scheduling of Boolean Satisfiability (SAT) and Mixed Integer Programming (MIP) instances that are well-known NP-complete problems. Typical online machine scheduling approaches assume that jobs are completed at some point in order to minimize functions related to completion time (e.g., makespan, minimum lateness, total weighted tardiness, etc). In this work, we formalize and present an online over time problem where arriving instances are subject to waiting time constraints. We propose computational approaches that combine the use of machine learning, MIP, and instance interruption heuristics. Unlike other approaches, we attempt to maximize the number of solved instances using single and multiple machine configurations. Our empirical evaluation with well-known SAT and MIP instances, suggest that our interruption heuristics can improve generic ordering policies to solve up to 21.6x and 12.2x more SAT and MIP instances. Additionally, our hybrid approach observed up to 90% of solved instances with respect to a semi clairvoyant policy (SCP).

Making Decisions on the Student Quota Problem: A Case Study Using a MIP Model

The aim of this paper is to present a model to support the decision making process on the Student Quota Problem (i.e., the maximum number of students that could be admitted in a university program). The number of students attended by universities is a key factor of national and international policies. The Organization for Economic Co-operation and Development (OECD) and Colombian official entities use this indicator to define goals of the educational level of young population. However, while the expectations of increasing the number of attended students are high, there are limits of growth based on resource limitations.