Carmine Cerrone

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

Abstract We address an important problem in the context of traffic management and control related to the optimum location of vehicle-ID sensors on the links of a network to derive route flow volumes. We consider both the full observability version of the problem, where one seeks for the minimum number of sensors (or minimum cost) such that all the route flow volumes can be derived, and the estimation version of the problem, that arises when there is a limited budget in the location of sensors. Four mathematical formulations are presented. These formulations improve the existing ones in the literature since they better define the feasible region of the problem by taking into account the temporal dimension of the license plate scanning process. The resulting mathematical formulations are solved to optimality and compared with the existing mathematical formulations. The results show that new and better solutions can be achieved with less computational effort. We also present two heuristic approaches: a greedy algorithm and a tabu search algorithm that are able to efficiently solve the analyzed problems and they are a useful tool able to find a very good trade-off between quality of the solution and computational time.

Relations, models and a memetic approach for three degree-dependent spanning tree problems

In this paper we take into account three different spanning tree problems with degree-dependent objective functions. The main application of these problems is in the field of optical network design. In particular, we propose the classical Minimum Leaves Spanning Tree problem as a relevant problem in this field and show its relations with the Minimum Branch Vertices and the Minimum Degree Sum Problems. We present a unified memetic algorithm for the three problems and show its effectiveness on a wide range of test instances.

A novel discretization scheme for the close enough traveling salesman problem

This paper addresses a variant of the Euclidean traveling salesman problem in which the traveler visits a node if it passes through the neighborhood set of that node. The problem is known as the close-enough traveling salesman problem. We introduce a new effective discretization scheme that allows us to compute both a lower and an upper bound for the optimal solution. Moreover, we apply a graph reduction algorithm that significantly reduces the problem size and speeds up computation of the bounds. We evaluate the effectiveness and the performance of our approach on several benchmark instances. The computational results show that our algorithm is faster than the other algorithms available in the literature and that the bounds it provides are almost always more accurate. HighlightsWe introduce a novel discretization scheme for the close enough TSP problem.By reducing the discretization error, the new scheme allows to compute tighter upper and lower bounds for the problem.We apply an enhanced convex hull strategy to save the number of discretization points to be used.The discretization strategy allows us to assign an adaptively variable number of discretization points to each neighborhood.Numerical comparisons with some algorithms proposed in the literature are presented.

Locating sensors to observe network arc flows: Exact and heuristic approaches

The problem of optimally locating sensors on a traffic network to monitor flows has been an object of growing interest in the past few years, due to its relevance in the field of traffic management and control. Sensors are often located in a network in order to observe and record traffic flows on arcs and/or nodes. Given traffic levels on arcs within the range or covered by the sensors, traffic levels on unobserved portions of a network can then be computed. In this paper, the problem of identifying a sensor configuration of minimal size that would permit traffic on any unobserved arcs to be exactly inferred is discussed. The problem being addressed, which is referred to in the literature as the Sensor Location Problem (SLP), is known to be NP-complete, and the existing studies about the problem analyze some polynomial cases and present local search heuristics to solve it. In this paper we further extend the study of the problem by providing a mathematical formulation that up to now has been still missing in the literature and present an exact branch and bound approach, based on a binary branching rule, that embeds the existing heuristics to obtain bounds on the solution value. Moreover, we apply a genetic approach to find good quality solutions. Extended computational results show the effectiveness of the proposed approaches in solving medium-large instances.

A Tabu Search Approach for the Circle Packing Problem

This paper concerns the problem to place N non overlapping circles in a circular container with minimum radius. This is a well known and widely studied problem with applications in manufacturing and logistics and, in particular, to problems related to cutting and packing. In this paper we propose an algorithm that by applying a strength along a selected direction on each circle, simulates the shifting of circles on the plane and tries to reduce the radius of the circular container during this movements. The algorithm is based on a multistart technique where the starting solutions are produced by a tabu search heuristic that uses also the current best solution. The algorithm takes part in a public international contest in order to find optimal solutions to a special case in circle packing. The contest saw the participation of 155 teams and our algorithm achieved the tenth position.

Carousel greedy

We present a new, generalized greedy algorithm for solving a class of optimization problems.Our primary focus is on optimizing the cardinality of a set.The approach, however, can be applied more generally, even to problems in statistics.The approach generates high-quality results, but is much faster than typical metaheuristics. In this paper, we introduce carousel greedy, an enhanced greedy algorithm which seeks to overcome the traditional weaknesses of greedy approaches. We have applied carousel greedy to a variety of well-known problems in combinatorial optimization such as the minimum label spanning tree problem, the minimum vertex cover problem, the maximum independent set problem, and the minimum weight vertex cover problem. In all cases, the results are very promising. Since carousel greedy is very fast, it can be used to solve very large problems. In addition, it can be combined with other approaches to create a powerful, new metaheuristic. Our goal in this paper is to motivate and explain the new approach and present extensive computational results.

OMEGA one multi ethnic genetic approach

The genetic algorithm (GA) is a quite efficient paradigm to solve several optimization problems. It is substantially a search technique that uses an ever-changing neighborhood structure related to a population which evolves according to a number of genetic operators. In the GA framework many techniques have been devised to escape from a local optimum when the algorithm fails in locating the global one. To this aim we present a variant of the GA which we call OMEGA (One Multi Ethnic Genetic Approach). The main difference is that, starting from an initial population,

A memetic algorithm for the weighted feedback vertex set problem

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