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Ramón Béjar

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4 papers

2 author rows

AAAI Conference 2012 Conference Paper

The Automated Vacuum Waste Collection Optimization Problem

Ramón Béjar
César Fernández
Carles Mateu
Felip Manyà
Francina Sole-Mauri
David Vidal

One of the most challenging problems on modern urban planning and one of the goals to be solved for smart city design is that of urban waste disposal. Given urban population growth, and that the amount of waste generated by each of us citizens is also growing, the total amount of waste to be collected and treated is growing dramatically (EPA 2011), becoming one sensitive issue for local governments. A modern technique for waste collection that is steadily being adopted is automated vacuum waste collection. This technology uses air suction on a closed network of underground pipes to move waste from the collection points to the processing station, reducing greenhouse gas emissions as well as inconveniences to citizens (odors, noise, .. .) and allowing better waste reuse and recycling. This technique is open to optimize energy consumption because moving huge amounts of waste by air impulsion requires a lot of electric power. The described problem challenge here is, precisely, that of organizing and scheduling waste collection to minimize the amount of energy per ton of collected waste in such a system via the use of Artificial Intelligence techniques. This kind of problems are an inviting opportunity to showcase the possibilities that AI for Computational Sustainability offers.

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ECAI Conference 2010 Conference Paper

Solving Pseudo-Boolean Modularity Constraints

Carlos Ansótegui
Ramón Béjar
Cèsar Fernández 0001
Francesc Guitart
Carles Mateu

This paper introduces new solving strategies for the resolution of Pseudo-Boolean Modularity (PBMod) constraints. In particular, we deal with modular arithmetic constraints on Boolean variables. On the one hand, we analyze translations to Pseudo-Boolean (PB) constraints and apply PB solvers. We also look at those PB solvers that have shown that a transformation to the SAT problem can be an effective solving strategy for PB problems. Among the existing translation techniques we focus on the encoding based on a network of sorters. We extend this encoding technique to generate directly a SAT formula from the PBMod constraints. We compare our approach to other standard techniques such as Satisfiability Modulo Theories (SMT) solvers with support for the Quantifier Free Linear Integer Arithmetic (QF_LIA) theory and the GLPK package for Mixed Integer Programming. In order to conduct our experimental investigation we present a generator of random PBMod constraints and study the impact of the several parameters on the hardness of the instances.

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AIJ Journal 2005 Journal Article

Sensor networks and distributed CSP: communication, computation and complexity

Ramón Béjar
Carmel Domshlak
Cèsar Fernández
Carla Gomes
Bhaskar Krishnamachari
Bart Selman
Magda Valls

We introduce SensorDCSP, a naturally distributed benchmark based on a real-world application that arises in the context of networked distributed systems. In order to study the performance of Distributed CSP (DisCSP) algorithms in a truly distributed setting, we use a discrete-event network simulator, which allows us to model the impact of different network traffic conditions on the performance of the algorithms. We consider two complete DisCSP algorithms: asynchronous backtracking (ABT) and asynchronous weak commitment search (AWC), and perform performance comparison for these algorithms on both satisfiable and unsatisfiable instances of SensorDCSP. We found that random delays (due to network traffic or in some cases actively introduced by the agents) combined with a dynamic decentralized restart strategy can improve the performance of DisCSP algorithms. In addition, we introduce GSensorDCSP, a plain-embedded version of SensorDCSP that is closely related to various real-life dynamic tracking systems. We perform both analytical and empirical study of this benchmark domain. In particular, this benchmark allows us to study the attractiveness of solution repairing for solving a sequence of DisCSPs that represent the dynamic tracking of a set of moving objects.

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LPAR Conference 1999 Conference Paper

Solving Combinatorial Problems with Regular Local Search Algorithms

Ramón Béjar
Felip Manyà

Abstract In this paper we describe new local search algorithms for regular CNF formulas and investigate their suitability for solving problems from the domains of graph coloring and sports scheduling. First, we define suitable encodings for such problems in the logic of regular CNF formulas. Second, we describe Regular-GSAT and Regular-WSAT, as well as some variants, which are a natural generalization of two prominent local search algorithms -GSAT and WSAT- used to solve the prepositional satisfiability (SAT) problem in classical logic. Third, we report on experimental results that demonstrate that encoding graph coloring and sports scheduling problems as instances of the SAT problem in regular CNF formulas and then solving these instances with local search algorithms can outperform or compete with state-of-the-art approaches to solving hard combinatorial problems.

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