Monica Gentili

On finding dissimilar Pareto-optimal paths

The aim of the present paper is to provide a methodology for finding a set of alternative paths between an origin and a destination site on which routing one or a set of dangerous goods. Finding a set of paths allows one to equally distribute the total risk among the population exposed. The concept of equity of risk is here related to the concept of determining spatially dissimilar paths. We divide our approach into two phases. In the first phase we find a set of Pareto-Optimal paths between an origin and a destination, by implementing a multicriteria shortest path algorithm. In the second one, for each path previously found, and by using a geographical information system, we construct a Buffer Zone approximating the impact area of a material being released after an accident. Based on these Buffer Zones, a dissimilarity index between every pair of paths can be derived in order to find the most spatially different routes. We then compare our method with an iterative penalty method and discuss computational results based both on a real application and on test problems.

Locating Active Sensors on Traffic Networks

Sensors are used to monitor traffic in networks. For example, in transportation networks, they may be used to measure traffic volumes on given arcs and paths of the network. This paper refers to an active sensor when it reads identifications of vehicles, including their routes in the network, that the vehicles actively provide when they use the network. On the other hand, the conventional inductance loop detectors are passive sensors that mostly count vehicles at points in a network to obtain traffic volumes (e.g., vehicles per hour) on a lane or road of the network.This paper introduces a new set of network location problems that determine where to locate active sensors in order to monitor or manage particular classes of identified traffic streams. In particular, it focuses on the development of two generic locational decision models for active sensors, which seek to answer these questions: (1) “How many and where should such sensors be located to obtain sufficient information on flow volumes on specified paths?”, and (2) “Given that the traffic management planners have already located count detectors on some network arcs, how many and where should active sensors be located to get the maximum information on flow volumes on specified paths?”The problem is formulated and analyzed for three different scenarios depending on whether there are already count detectors on arcs and if so, whether all the arcs or a fraction of them have them. Location of an active sensor results in a set of linear equations in path flow variables, whose solution provide the path flows. The general problem, which is related to the set-covering problem, is shown to be NP-Hard, but special cases are devised, where an arc may carry only two routes, that are shown to be polynomially solvable. New graph theoretic models and theorems are obtained for the latter cases, including the introduction of the generalized edge-covering by nodes problem on the path intersection graph for these special cases. An exact algorithm for the special cases and an approximate one for the general case are presented.

Combinatorial aspects of the sensor location problem

In this paper we address the Sensor Location Problem, that is the location of the minimum number of counting sensors, on the nodes of a network, in order to determine the arc flow volume of all the network. Despite the relevance of the problem from a practical point of view, there are very few contributions in the literature and no combinatorial analysis is performed to take into account particular structure of the network. We prove the problem is

α-Coverage to extend network lifetime on wireless sensor networks

\cal N \cal P

Bounded-degree spanning tree problems: models and new algorithms

-complete in different cases. We analyze special classes of graphs that are particularly interesting from an application point of view, for which we give low order polynomial solution algorithms.

The labeled maximum matching problem

We study the Minimum Labelling Spanning Tree Problem: Given a graph G with a color (label) assigned to each edge (not necessarily properly) we look for a spanning tree of G with the minimum number of different colors. The problem has several applications in telecommunication networks, electric networks, multimodal transportation networks, among others, where one aims to ensure connectivity by means of homogeneous connections. For this NP-hard problem very few heuristics are presented in the literature giving good quality solutions. In this paper we apply several metaheuristic approaches to solve the problem. These approaches are able to improve over existing heuristics presented in the literature. Furthermore, a comparison with the results provided by an exact approach existing in the literature shows that we may quite easily obtain optimal or close to optimal solutions.

Vehicle-ID sensor location for route flow recognition: Models and algorithms

An important problem in the context of wireless sensor networks is the Maximum Network Lifetime Problem (MLP): find a collection of subset of sensors (cover) each covering the whole set of targets and assign them an activation time so that network lifetime is maximized. In this paper we consider a variant of MLP, where we allow each cover to neglect a certain fraction (1 − α) of the targets. We analyze the problem and show that the total network lifetime can be hugely improved by neglecting a very small portion of the targets. An exact approach, based on a Column Generation scheme, is presented and a heuristic solution algorithm is also provided to initialize the approach. The proposed approaches are tested on a wide set of instances. The experimentation shows the effectiveness of both the proposed problems and solution algorithms in extending network lifetime and improving target coverage time when some regularity conditions are taken into account.

Exact and metaheuristic approaches to extend lifetime and maintain connectivity in wireless sensors networks

Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times.