LIBERALIZATION, PUBLIC SPENDING AND CONVERGENCE IN LATIN AMERICA: AN SPATIAL ECONOMETRIC MODEL

The study focuses on the analysis of economic growth of the Latin American countries, during the period 1950-2000 The methodology of the paper is based on the use of spatial econometric thechniques applied to a cross section and panel data. The results showed conditional convergence for the region from 1950 to 1975, and both absolute and conditional divergence from 1980 to 2000.Convergence; Latin America

A space biased-sampling approach for the vehicle routing problem with stochastic demands

International audienceThe vehicle routing problem with stochastic demands consists of designing transportation routes of minimal expected cost to satisfy a set of customers with random demands of known probability distribution. We propose a metaheuristic that uses randomized heuristics for the traveling salesman problem, a tour partitioning procedure, and a set-partitioning formulation to sample the solution space and find solutions for the problem. Computational experiments show that our approach is competitive with state-of-the-art algorithms for the problem in terms of both accuracy and efficiency

APERTURA. GASTO PÚBLICO Y CONVERGENCIA EN AMÉRICA LATIN: UN MODELO ECONOMETRICO ESPACIAL

The study focuses on the analysis of economic growth of the Latin American countries, during the period 1950-2000 The methodology of the paper is based on the use of spatial econometric thechniques applied to a cross section and panel data. The results showed conditional convergence for the region from 1950 to 1975, and both absolute and conditional divergence from 1980 to 2000

APERTURA. GASTO PÚBLICO Y CONVERGENCIA EN AMÉRICA LATIN: UN MODELO ECONOMETRICO ESPACIAL

The study focuses on the analysis of economic growth of the Latin American countries, during the period 1950-2000 The methodology of the paper is based on the use of spatial econometric thechniques applied to a cross section and panel data. The results showed conditional convergence for the region from 1950 to 1975, and both absolute and conditional divergence from 1980 to 2000

On the vehicle routing problem with stochastic demands and duration constraints: formulations and a hybrid metaheuristic

International audienceThe vehicle routing problem with stochastic demands (VRPSD) consists in designing transportation routes of minimal expected cost to satisfy a set of customers with random demands of known probability distributions. In this research we present two strategies to deal with route duration constraints in the VRPSD. To solve the resulting problem, we proposed a greedy randomized adaptive search procedure (GRASP) with a post optimization procedure. The GRASP component uses a set of randomized route-first, cluster-second heuristics to generate starting solutions and a variable neighborhood descent (VND) procedure to carry on the local search phase. The post optimizer selects the best possible routes to assemble the final solution from the set of all routes found in the local optima reached by the GRASP. We discuss extensive computational experiments analysing the cost of considering route duration constraints on the VRPSD. In addition, we report state-of-the-art solutions for a established set of benchmarks for the classical VRPSD

The electric vehicle routing problem with partial charging and nonlinear charging function

SDOElectric vehicle routing problems (eVRPs) extend classical routing problems to consider the limited driving range of electric vehicles. In general, this limitation is overcome by introducing planned detours to battery charging stations. Most existing eVRP models rely on one (or both) of the following assumptions: (i) the vehicles fully charge their batteries every time they reach a charging station, and (ii) the battery charge level is a linear function of the charging time. In practical situations, however, the amount of charge is a decision variable, and the battery charge level is a concave function of the charging time.In this paper we extend current eVRP models to consider partial charging and nonlinear charging functions. We present a computational study comparing our assumptions with those commonly made in the literature. Our results suggest that neglecting partial and nonlinear charging may lead to infeasible or overly expensive solutions

Microscopic mass estimations

The quest to build a mass formula which have in it the most relevant microscopic contributions is analyzed. Inspired in the successful Duflo-Zuker mass description, the challenges to describe the shell closures in a more transparent but equally powerful formalism are discussed.Comment: 14 pages, 6 figures, submitted to Journal of Physics G, Focus issue on Open Problems in Nuclear Structure Theor

The electric vehicle routing problem with capacitated charging stations

Much of the existing research on electric vehicle routing problems (E-VRPs) assumes that the charging stations (CSs) can simultaneously charge an unlimited number of electric vehicles, but this is not the case. In this research, we investigate how to model and solve E-VRPs taking into account these capacity restrictions. In particular, we study an E-VRP with non-linear charging functions, multiple charging technologies, en route charging, and variable charging quantities, while explicitly accounting for the capacity of CSs expressed in the number of chargers. We refer to this problem as the E-VRP with non-linear charging functions and capacitated stations (E-VRP-NL-C). This problem advances the E-VRP literature by considering the scheduling of charging operations at each CS. We first introduce two mixed integer linear programming formulations showing how CS capacity constraints can be incorporated into E-VRP models. We then introduce an algorithmic framework to the E-VRP-NL-C, that iterates between two main components: a route generator and a solution assembler. The route generator uses an iterated local search algorithm to build a pool of high-quality routes. The solution assembler applies a branch-and-cut algorithm to select a subset of routes from the pool. We report on computational experiments comparing four different assembly strategies on a large and diverse set of instances. Our results show that our algorithm deals with the CS capacity constraints effectively. Furthermore, considering the well-known uncapacitated version of the E-VRP-NL-C, our solution method identifies new best-known solutions for 80 out of 120 instances

The electric bus fleet transition problem

The use of electric bus fleets has become a topical issue in recent years. Several companies and municipalities, either voluntarily or to comply with legal requirements, will transition to greener bus fleets in the next decades. Such transitions are often established by fleet electrification targets, which dictate the number of electric buses that should be in the fleet by a given time period. In this paper we introduce a comprehensive optimization-based decision making tool to support such transitions. More precisely, we present a fleet replacement problem which allows organizations to determine bus replacement plans that will meet their fleet electrification targets in a cost-effective way, namely considering purchase costs, salvage revenues, operating costs, charging infrastructure investments, and demand charges. We account for several charging infrastructure options, such as slow and fast plug-in stations, overhead pantograph chargers, and inductive (wireless) chargers. We refer to this problem as the electric bus fleet transition problem, and we model it as an integer linear program. We apply our model to conduct computational experiments based on several scenarios. We use real data provided by a public transit agency in order to draw insights into optimal transition plans