A framework for derivative free algorithm hybridization

Column generation is a basic tool for the solution of largescale mathematical programming problems. We present a class of column generation algorithms in which the columns are generated by derivative free algorithms, like population-based algorithms. This class can be viewed as a framework to define hybridization of free derivative algorithms. This framework has been illustrated in this article using the Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms, combining them with the Nelder-Mead (NM) method. Finally a set of computational experiments has been carried out to illustrate the potential of this framework

A comparison of different soft-computing techniques for the evaluation of handball goalkeepers.

The efficiency of handball goalkeepers is a good predictor of team ranking in tournaments, but despite this, very few studies have been carried out into the performance characteristics of elite goalkeepers. This paper provides the criteria for evaluating a handball goalkeeper and applies a variety of soft-computingmethodologies for estimating their weights. More specifically, a fuzzy multi-criteria decision-making method, a metaheuristic optimisation algorithm, and statistical and domain-knowledgebased methods were used to evaluate the actions of goalkeepers during the game. Computer experiments were performed for all the proposed methodologies, using data from the 2020 European Men’s Handball Championship, in order to estimate the weights of the indicators

A Methodology for the Automatic Regulation of Intersections in Real Time using Soft-computing Techniques

This work presents an application of diverse soft-computing techniques to the resolution of semaphoric regulation problems. First, clustering techniques are used to discover the prototypes which characterize the mobility patterns at an intersection. A prediction model is then constructed on the basis of the prototypes found. Fuzzy logic techniques are used to formally represent the prototypes in this prediction model and these prototypes are parametrically defined through frameworks. The use of these techniques supposes a substancial contribution to the significance of the prediction model, making it robust in the face of anomalous mobility patterns, and efficient from the point of view of real-time computatio

Determining highway corridors

In the highway development process, the first planning stage is that of selecting a corridor along which the highway is to pass. Highway corridor selection represents a multicriteria decision process in which a variety of social, enviromental and economic factors must be evaluated and weighted for a large number of corridor alternatives. This paper proposes a demand-based approach to provide a set of potential corridors. The problem is formulated as a continuous location model which seeks a set of optimal corridors with respect to the demand of potential users while satisfying budget constraints. This model uses geographical information in order to estimate the length-dependent costs (such as pavement and construction cost) and the cost of earth movement. A procedure for finding the best local minima of the optimization model is proposed. This method is tested using the Particle Swarm Optimization algorithm, two algorithms of the Simulated Annealing type and the Simplex Nedelmar method. An application using the Castilla-La Mancha\s geographic database is presented

A modeling framework for the estimation of optimal CO2 emission taxes for private transport

In this paper, a novel modeling framework is proposed for the estimation of optimal CO2 emission taxes for urban traffic. The framework is based on a bi-level model comprising a combined equilibrium model with elastic demand and a \\\\pollution taxes\\\\ (PTs) estimation model based on vehicle kilometers traveled and emissions produced. A bi-level optimization problem is proposed for the PT estimation model (PTM) in order to provide the minimum price which reduces emissions generated in an urban area to a desired value dependent on the environmental goals. To solve this problem, the Regula Falsi method is proposed and it exhibits a high enough rate of convergence. Two tests using the Nguyen and Dupuis network and Barcelona network (Spain) have been performed to test the convergence of our resolution method and the applicability of the proposal over networks with different sizes. The results are very promising and allow the implicit definition of the behavior of users against different PT prices

Modeling of the behavior of alternative fuel vehicle buyers. A model for the location of alternative refueling stations

This paper addresses the problem of estimating the infrastructure to be made available for refueling alternative fuel vehicles as a function of the profitability thresholds required by the investment. A methodology has been devised based on sales forecasts for alternative fuel vehicles. These methods use discrete choice models in which the factor of refueling infrastructure, rather than being considered simply as one more attribute of the model, acts as a constraint on the choice set for vehicle buyers. This methodology is used to estimate the infrastructure of hydrogen refueling stations and electricity charging stations for Spain (8,112 population centers) in 2030. Evolution of fuel cell vehicles over the years 2016 and 2030 is also estimated and compared with forecasts for countries such as France, Germany and the United Kingdom

A Fuzzy Framework to Evaluate Players' Performance in Handball

The evaluation of the players' performance in sports teams is commonly based on the opinion of experts who do not always agree on the importance of the chosen indicators. This paper presents a novel approach based on fuzzy multi-criteria group decision-making tools for selecting those criteria that best represent the handball player's performance in a match and for setting their relevance weights. Our approach consists of a fuzzy model to aggregate expert judgments. This methodology overcomes some drawbacks of classical systems, including the definition of the relevance of each criteria using linguistic labels. A preliminary evaluation analyzes handball players' performance indicators and their application to a short tournament. Considering the obtained results, we can conclude that the proposal is relevant and provides useful insights regarding player performance in different matches. The proposed methodology has also been compared with a basic plus-minus rating methodology. This comparison illustrates the feasibility of our approach. Results suggest that plus-minus rating is not the best solution to represent the performance of specialized players who only play when their team attack or defense. Our approach demonstrates being more appropriate for sports such as handball because it includes the valuation of a full set of positive actions in defense and attack

An adaptive approach to enhanced traffic signal optimization by using soft-computing techniques

This paper presents an application of diverse soft-computing techniques to adaptive traffic light controls. The proposed methodology consists of two main phases: off-line and on-line. First, clustering techniques and optimization methods are used at the off-line stage to discover the prototypes which characterize the traffic mobility patterns at an intersection. After this process an optimum timing plan is decided for each mobility pattern detected. In the on-line phase, a prediction model is then constructed on the basis of the prototypes found. Fuzzy Logic based techniques are used to formally represent the prototypes in the prediction model and these prototypes are parametrically defined through frameworks. During the on-line phase an intelligent transportation system, by using the prediction model, matches the current traffic conditions to the mobility patterns detected at the off-line stage in order to identify the most suitable one to be used. The use of these techniques supposes a substantial contribution to the significance of the prediction model, making it robust in the face of anomalous mobility patterns, and efficient from the point of view of real-time computation

Constrained nested logit model: Formulation and estimation

A model of traveller behaviour should recognise the exogenous and endogenous factors that limit the choice set of users. These factors impose constraints on the decision maker, which constraints may be considered implicitly, as soft constraints imposing thresholds on the perception of changes in attribute values, or explicitly as hard constraints. The purpose of this paper is twofold: (1) To present a constrained nested logit-type choice model to cope with hard constraints. This model is derived from the entropy-maximizing framework. (2) To describe a general framework to deal with (dynamic) non-linear utilities. This approach is based on Reproducing Kernel Hilbert Spaces. The resulting model allows the dynamic aspect and the constraints on the choice process to be represented simultaneously. A novel estimation procedure is introduced in which the utilities are viewed as the parameters of the proposed model instead of attribute weights as in the classical linear models. A discussion on over-specification of the proposed model is presented. This model is applied to a synthetic test problem and to a railway service choice problem in which users choose a service depending on the timetable, ticket price, travel time and seat availability (which imposes capacity constraints). Results show (1) the relevance of incorporating constraints into the choice models, (2) that the constrained models appear to be a better fit than the counterpart unconstrained choice models; and (3) the viability of the approach, in a real case study of railway services on the Madrid–Seville corridor (Spain)