Examinando por Autor "Escudero, Laureano F."

Mostrando 1 - 3 de 3

Mathematical Optimization models for Air Traffic Flow Management: A review
(2010-02-10) Agustín, Alba; Alonso-Ayuso, Antonio; Escudero, Laureano F.; Pizarro, Celeste
Congestion problems are becoming increasingly acute in many European and American airports and air sectors. To protect Air Traffic Control (ATC) from overload a planning activity called Air Traffic Flow Management (ATFM) tries to anticipate and prevent overload and limit resulting delays. When the traffic expects to exceed the airport arrival and departure capacities or the airsector capacity a delay in the flight arrival (so called-congestion) occurs. The casuistry to be considered in this field is very extensive. In general, most references to be found in the literature written some years ago refer to the simplest models, those which do not take into account airsector. This is so because this work was first studied in USA, where only the problems of congestion in airports basically occur. In the paper we present a state-of-the-art survey on the main optimization models encountered in the literature. They are classified as follows: (1) Single-Airport Ground-Holding Problem (SAGHP). The simplest of the methodologies of planning modelling studied proposes solutions to the problem of deciding the optimal planning for an arrival airport. (2) Multi-Airport Ground-Holding Problem (MAGHP). In this methodology the field of work is extended and the inter-relationship which exists between different airports is included. (3) Air Traffic Flow Management Problem (ATFMP). This methodology attempts to solve real situations that are much more complex than those which can be dealt with using the previous methodologies, since the air sector capacity is also considered. (4) Air Traffic Flow Management Rerouting Problem (ATFMRP). This methodology considers the more realistic situation where the flights can be diverted to alternative routes. (5) Air Traffic Flow Management Rerouting Problem (ATFMRP) with uncertainty. The ATFM problem is especially sensitive to changes in capacity. This leads to generalize the previous methodologies and to include generic uncertainty for these possible unforeseen changes in the parameters of the model, making way for stochastic methodologies. This type of problems are the most difficult ones, but alas the realistic ones.
On risk management of a two-stage stochastic mixed 0–1 model for the closed-loop supply chain design problem
(Elsevier, 2019-04-01) Pizarro, Celeste; Baptista, Susana; Barbosa-Povoa, Ana Paula; Escudero, Laureano F.; Gomes, Maria Isabel
In this work, the design and operation planning of a multi-period, multi-product closed-loop supply chain is addressed. Recovered end-of-life products from customers are evaluated in disassembly centers and accordingly are sent back to factories for remanufacturing, or leave the network either by being sold to third parties or by being sent to disposal. Typical uncertain parameters are product demand, production cost, and returned product volume and evaluation, among others. So, stochastic optimization approaches should be used for problem solving, where different topology decisions on the timing, location and capacity of some entities (factories, and distribution and sorting centers) are to be considered along a time horizon. A two-stage multi-period stochastic mixed 0–1 bilinear optimization model is introduced, where the combined definition of the available entities at the periods and the products’ flow among the entities, maximizes the net present value of the expected total profit along the time horizon. A version of the mixture of chance-constrained and second order stochastic dominance risk averse measures is considered for risk management at intermediate periods of the time horizon. Given the high dimensions of the model it is unrealistic to look for the optimality of the solution in an affordable computing effort for current hardware and optimization software resources. So, a decomposition approach is considered, namely a Fix-and-Relax decomposition algorithm. For assessing the computational validation of the modeling and algorithmic proposals, pilot cases are taken from a real-life glass supply chain network whose main features are retained
Variable Neighborhood Search for the Vertex Separation Problem
(Elsevier, 2012) Duarte, Abraham; Escudero, Laureano F.; Martí, Rafael; Mladenovic, Nenad; Pantrigo, Juan José; Sánchez-Oro, Jesús
The vertex separation problem belongs to a family of optimization problems in which the objective is to nd the best separator of vertices or edges in a generic graph. This optimization problem is strongly related to other well-known graph problems; such as the Path-Width, the Node Search Number or the Interval Thickness, among others. All of these optimization problems are NP-hard and have practical applications in VLSI, computer language compiler design or graph drawing. Up to know, they have been generally tackled with exact approaches, presenting polynomial-time algorithms to obtain the optimal solution for speci c types of graphs. However, in spite of their practical applications, these problems have been ignored from a heuristic perspective, as far as we know. In this paper we propose a pure 0-1 optimization model and a metaheuristic algorithm based on the variable neighborhood search methodology for the vertex separation problem on general graphs. Computational results show that small instances can be optimally solved with this optimization model and the proposed metaheuristic is able to nd high-quality solutions with a moderate computing time for large-scale instances.