Laure Devendeville

A Hybrid Approach for SAT

Exploiting variable dependencies has been shown very useful in local search algorithms for SAT. In this paper, we extend the use of such dependencies by hybridizing a local search algorithm, Walksat, and the DPLL procedure, Satz. At each node reached in the DPLL search tree to a fixed depth, we construct the literal implication graph. Its strongly connected components are viewed as equivalency classes. Each one is substituted by a unique representative literal to reduce the constructed graph and the input formula. Finally, the implication dependencies are closed under transitivity. The resulted implications and equivalencies are exploited by Walksat at each node of the DPLL tree. Our approach is motivated by the power of the branching rule used in Satz that may provide a valid path to a solution, and generate more implications at deep nodes. Experimental results confirm the efficiency of our approach.

On inconsistent clause-subsets for Max-SAT solving

Recent research has focused on using the power of look-ahead to speed up the resolution of the Max-SAT problem. Indeed, look-ahead techniques such as Unit Propagation (UP) allow to find conflicts and to quickly reach the upper bound in a Branch-and-Bound algorithm, reducing the search-space of the resolution. In previous works, the Max-SAT solvers maxsatz9 and maxsatz14 use unit propagation to compute, at each node of the branch and bound search-tree, disjoint inconsistent subsets of clauses in the current subformula to estimate the minimum number of clauses that cannot be satisfied by any assignment extended from the current node. The same subsets may still be present in the subtrees, that is why we present in this paper a new method to memorize them and then spare their recomputation time. Furthermore, we propose a heuristic so that the memorized subsets of clauses induce an ordering among unit clauses to detect more inconsistent subsets of clauses. We show that this new approach improves maxsatz9 and maxsatz14 and suggest that the approach can also be used to improve other state-of-the-art Max-SAT solvers.

A Memetic Algorithm for staff scheduling problem in airport security service

Abstract The staff scheduling problem is widely studied in Operational Research. Various surveys are available in the literature dealing with this problem which concerns various objectives and various constraints. In this article, we present a staff scheduling problem in airport security service. First, a modeling of the problem, and a representation of solutions are shown. The problem is solved in three steps, days-off scheduling, shift scheduling, and staff assignment. We focus on the last step, by providing a Memetic Algorithm (MA) which merged an Evolutionary Algorithm and Local Search techniques. We propose a chromosome encoding, a crossover operator and a combined neighborhood function, specially dedicated to this staff assignment problem. Besides providing better solutions than software currently used, this algorithm provides up to 50% of improvement from initial feasible solutions.

Using Boolean Constraint Propagation for sub-clauses deduction

The Boolean Constraint Propagation (BCP) is a well-known helpful technique implemented in most state-of-the-art efficient satisfiability solvers. We propose in this paper a new use of the BCP to deduce sub-clauses from the associated implication graph. Our aim is to reduce the length of clauses thanks to the subsumption rule. We show how such extension can be grafted to modern SAT solvers and we provide some experimental results of the sub-clauses deduction as a pretreatment process. This work is supported by the Region Picardie under HTSC project.

Stochastic Local Search for Omnidirectional Catadioptric Stereovision Design

This paper deals with a compact catadioptric omnidirectional stereovision system based on a single camera and multi-mirrors (at least two mirrors). Many configurations were empirically designed in previous works with the aim to obtain a good 3D reconstruction accuracy. In this paper, we propose to use optimization techniques for omnidirectional catadioptric stereovision design, by using a stochastic local search method in order to find a good sensor (number, relative positions and sizes of mirrors). We explain principles of our approach and provide automatically designed sensors with a number of mirrors from two to nine. We finally simulate the 3D-reconstruction of a real environment modeled under a ray-tracing software with some of these sensors.

Staff scheduling in airport security service

Abstract The staff scheduling problem is widely studied in Operational Research. Various surveys are available in the literature dealing with this problem which concerns various objectives and various constraints. In this article, we present a staff scheduling problem in airport security service. The problem is solved in three steps, days-off scheduling, shift scheduling, and staff assignment. We focus on the last step, by providing two algorithms, a greedy algorithm and a global assignment algorithm which provides an initial solution. This solution is then improved by an iterative time out destruction/construction algorithm which alternates between partial destruction and reconstruction steps. Besides providing better solutions than software currently used, this algorithm enables to deal with new further constraints.

Optimal omnidirectional sensor for urban traffic diagnosis in crossroads

In this paper we present an optimal omnidirectional visual sensor which can replace perspective camera network for traffic diagnosis. The proposed system has the advantage, by the number and the designed mirror, to generate a single view of the crown and junction ways of the crossroads by maximizing the number of useless pixels. So, the percentage of pixels utilized directly for subsequent phases of image processing is optimal. We describe the methodology used to design such a sensor. In addition, to assess our sensor, we also developed image processing methods that provide useful indicators for estimating the state of the traffic as the crossroads occupancy rate, the vehicle speed and the flow of vehicles. Finally, we compare this optimal sensor to others that consist of parabolic, hyperbolic or spherical mirror to observe the scene. We prove that optimal sensor has better results than others.

A new Omnidirectional Stereovision Sensor

This paper describes a new compact omnidirectional stereovision sensor that combines a single orthographic camera and four paraboloidal mirrors. Its geometry has been designed with the help of a stochastic optimization approach in order to minimize the 3D reconstruction error. In comparison with state-of-the-art sensors described in the literature, better results are obtained for this sensor during simulations. We will especially compare it with a classical configuration with two mirrors. Two criteria will be used: the 3D reconstruction accuracy and the field of view. We illustrate the advantages of our sensor within a framework of a simulation using a realistic environment and a ray-tracing software.