A survey on metaheuristics for stochastic combinatorial optimization

Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed. Issues common to all metaheuristics, open problems, and possible directions of research are proposed and discussed. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, and useful informations to start working on this problem domain, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are currently being applied to optimization under uncertainty, and motivations for interest in this fiel

Parasitosis zoonóticas en un asentamiento a orillas del Río de La Plata

El barrio “El Molino”, alberga una población precarizada con conductas higiénico-sanitarias inadecuadas para la salud. El equipo de trabajo integra proyectos de Voluntariado Universitario, Extensión Universitaria e Incentivos docentes destinados a contribuir en la prevención, diagnóstico y mejora sanitaria del lugar, integrados por docentes y alumnos de 4 Facultades de la UNLP. Objetivo Diagnosticar parasitosis zoonóticas en la población de un área de riesgo sanitario y analizar su relación con diversos factores de riesgo.Facultad de Ciencias Veterinaria

Parasitosis zoonóticas en un asentamiento a orillas del Río de La Plata

El barrio “El Molino”, alberga una población precarizada con conductas higiénico-sanitarias inadecuadas para la salud. El equipo de trabajo integra proyectos de Voluntariado Universitario, Extensión Universitaria e Incentivos docentes destinados a contribuir en la prevención, diagnóstico y mejora sanitaria del lugar, integrados por docentes y alumnos de 4 Facultades de la UNLP. Objetivo Diagnosticar parasitosis zoonóticas en la población de un área de riesgo sanitario y analizar su relación con diversos factores de riesgo.Facultad de Ciencias Veterinaria

Parasitosis zoonóticas en un asentamiento a orillas del Río de la Plata

El barrio “El Molino”, alberga una población precarizada con conductas higiénico-sanitarias inadecuadas para la salud. El equipo de trabajo integra proyectos de Voluntariado Universitario, Extensión Universitaria e Incentivos docentes destinados a contribuir en la prevención, diagnóstico y mejora sanitaria del lugar, integrados por docentes y alumnos de 4 Facultades de la UNLP. Objetivo: Diagnosticar parasitosis zoonóticas en la población de un área de riesgo sanitario y analizar su relación con diversos factores de riesgo.Facultad de Ciencias Veterinaria

Ant colony optimization and local search for the probabilistic traveling salesman problem: a case study in stochastic combinatorial optimization

In this thesis we focus on Stochastic combinatorial Optimization Problems (SCOPs), a wide class of combinatorial optimization problems under uncertainty, where part of the information about the problem data is unknown at the planning stage, but some knowledge about its probability distribution is assumed.Optimization problems under uncertainty are complex and difficult, and often classical algorithmic approaches based on mathematical and dynamic programming are able to solve only very small problem instances. For this reason, in recent years metaheuristic algorithms such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others, are emerging as successful alternatives to classical approaches.In this thesis, metaheuristics that have been applied so far to SCOPs are introduced and the related literature is thoroughly reviewed. In particular, two properties of metaheuristics emerge from the survey: they are a valid alternative to exact classical methods for addressing real-sized SCOPs, and they are flexible, since they can be quite easily adapted to solve different SCOPs formulations, both static and dynamic. On the base of the current literature, we identify the following as the key open issues in solving SCOPs via metaheuristics: (1) the design and integration of ad hoc, fast and effective objective function approximations inside the optimization algorithm;(2) the estimation of the objective function by sampling when no closed-form expression for the objective function is available, and the study of methods to reduce the time complexity and noise inherent to this type of estimation;(3) the characterization of the efficiency of metaheuristic variants with respect to different levels of stochasticity in the problem instances. We investigate the above issues by focusing in particular on a SCOP belonging to the class of vehicle routing problems: the Probabilistic Traveling Salesman Problem (PTSP). For the PTSP, we consider the Ant Colony Optimization metaheuristic and we design efficient local search algorithms that can enhance its performance. We obtain state-of-the-art algorithms, but we show that they are effective only for instances above a certain level of stochasticity, otherwise it is more convenient to solve the problem as if it were deterministic.The algorithmic variants based on an estimation of the objective function by sampling obtain worse results, but qualitatively have the same behavior of the algorithms based on the exact objective function, with respect to the level of stochasticity. Moreover, we show that the performance of algorithmic variants based on ad hoc approximations is strongly correlated with the absolute error of the approximation, and that the effect on local search of ad hoc approximations can be very degrading.Finally, we briefly address another SCOP belonging to the class of vehicle routing problems: the Vehicle Routing Problem with Stochastic Demands (VRPSD). For this problem, we have implemented and tested several metaheuristics, and we have studied the impact of integrating in them different ad hoc approximations.Doctorat en sciences appliquéesinfo:eu-repo/semantics/nonPublishe

Ant colony optimization and local search for the probabilistic traveling salesman problem: a case study in stochastic combinatorial optimization

In this thesis we focus on Stochastic combinatorial Optimization Problems (SCOPs), a wide class of combinatorial optimization problems under uncertainty, where part of the information about the problem data is unknown at the planning stage, but some knowledge about its probability distribution is assumed.Optimization problems under uncertainty are complex and difficult, and often classical algorithmic approaches based on mathematical and dynamic programming are able to solve only very small problem instances. For this reason, in recent years metaheuristic algorithms such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others, are emerging as successful alternatives to classical approaches.In this thesis, metaheuristics that have been applied so far to SCOPs are introduced and the related literature is thoroughly reviewed. In particular, two properties of metaheuristics emerge from the survey: they are a valid alternative to exact classical methods for addressing real-sized SCOPs, and they are flexible, since they can be quite easily adapted to solve different SCOPs formulations, both static and dynamic. On the base of the current literature, we identify the following as the key open issues in solving SCOPs via metaheuristics: (1) the design and integration of ad hoc, fast and effective objective function approximations inside the optimization algorithm;(2) the estimation of the objective function by sampling when no closed-form expression for the objective function is available, and the study of methods to reduce the time complexity and noise inherent to this type of estimation;(3) the characterization of the efficiency of metaheuristic variants with respect to different levels of stochasticity in the problem instances. We investigate the above issues by focusing in particular on a SCOP belonging to the class of vehicle routing problems: the Probabilistic Traveling Salesman Problem (PTSP). For the PTSP, we consider the Ant Colony Optimization metaheuristic and we design efficient local search algorithms that can enhance its performance. We obtain state-of-the-art algorithms, but we show that they are effective only for instances above a certain level of stochasticity, otherwise it is more convenient to solve the problem as if it were deterministic.The algorithmic variants based on an estimation of the objective function by sampling obtain worse results, but qualitatively have the same behavior of the algorithms based on the exact objective function, with respect to the level of stochasticity. Moreover, we show that the performance of algorithmic variants based on ad hoc approximations is strongly correlated with the absolute error of the approximation, and that the effect on local search of ad hoc approximations can be very degrading.Finally, we briefly address another SCOP belonging to the class of vehicle routing problems: the Vehicle Routing Problem with Stochastic Demands (VRPSD). For this problem, we have implemented and tested several metaheuristics, and we have studied the impact of integrating in them different ad hoc approximations.Doctorat en sciences appliquéesinfo:eu-repo/semantics/nonPublishe

Ant colony optimization and local search based on exact and estimated objective values for the probabilistic traveling salesman problem

This paper deals with a general choice that one faces when developing an algorithm for a stochastic optimization problem: either design problem-specific algorithms that exploit the exact objective function, or to consider algorithms that only use estimated values of the objective function, which are very general and for which simple non-sophisticated versions can be quite easily designed. The Probabilistic Traveling Salesman Problem and the Ant Colony Optimization metaheuristic are used as a case study for this general issue. We consider four Ant Colony Optimization algorithms with different characteristics. Two algorithms exploit the exact objective function of the problem, and the other two use only estimated values of the objective function by Monte Carlo sampling. For each of these two groups, we consider both hybrid and non-hybrid versions (that is, with and without the application of a local search procedure). Computational experiments show that the hybrid version based on exact objective values outperforms the other variants and other state-of-the-art metaheuristics from the literature. Experimental analysis on a benchmark of instances designed on purpose let us identify in which conditions the performance of estimationbased variants can be competitive with the others.