Catálogo de publicaciones - libros

A supply chain network perspective for electric power production, supply, transmission, and consumption is developed. The model is sufficiently general to handle the behavior of the various decision-makers, who operate in a decentralized manner and include power generators, power suppliers, the transmitters, as well as the consumers associated with the demand markets. The optimality conditions are derived, along with the equilibrium state for the electric power supply chain network. The finite-dimensional variational inequality formulation of the equilibrium state is derived, whose solution yields the equilibrium electric power flows transacted between the tiers of the supply chain network as well as the nodal prices. The variational inequality formulation is utilized to provide qualitative properties of the equilibrium electric power flow and price patterns and to propose a computational scheme. The algorithm is then applied to compute the solutions to several numerical examples

Many economic sectors often collect significantly less data than would be required to analyze related standard decision problems. This is because the demand for some data can be intrusive to the participants of the economy in terms of time and sensitivity. The problem of modelling and solving decision models when relevant empirical information is incomplete is addressed. First, a procedure is presented for adjusting the parameters of a model which is robust against the worst-case values of unobserved data. Second, a scenario tree approach is considered to deal with the randomness of the dynamic economic model and equilibria is computed using an interior-point algorithm. This methodology is implemented in the Australian deregulated electricity market. Although a simplified model of the market and limited information on the production side are considered, the results are very encouraging since the pattern of equilibrium prices is forecasted

Logistics services form the backbone of every supply chain. Given their importance in the operation of corporations, it is interesting to determine efficient methods for optimal service procurement A typical problem faced by most managers of global firms is studied: given a set of service providers with respective quantity-discount curves, the objective is to compute the set of logistics services that should be procured from each provider, such that the overall supply chain efficiency requirements are met. Although this is a very common problem, it is actually intractable when the number of logistics providers and their services is large enough. An auction based mechanism to model this situation is developed, using a hybrid auction approach. Integer programming formulations for the problem are presented, which try to explore the combinatorial features of the problem. In order to allow for the efficient computation of large instances, a heuristic algorithm to the winner determination problem is presented. The proposed polynomial algorithm is applied to a large number of test instances. Results demonstrate that close to optimal solutions are achieved by the algorithm in reasonable time, even for large instances typically occurring in real applications

Oracle Based Optimisation (OBO) conveniently designates an approach to handle a class of convex optimisation problems in which the information pertaining to the function to be minimized and/or to the feasible set takes the form of a linear outer approximation revealed by an oracle. Three representative examples are introduced to show how one can cast difficult problems in this format, and solve them. An efficient method, Proximal-ACCPM, is presented to trigger the OBO approach. Numerical results for these examples are provided to illustrate the behavior of the method. This paper summarizes several contributions with the OBO approach and aims to give, in a single report, enough information on the method and its implementation to facilitate new applications

Eight distinct (and in some cases little known) formulations of the Travelling Salesman Problem as an Integer Programme are given. Apart from the standard formulation all the formulations are ‘compact’ in the sense that the number of constraints and variables is a polynomial function of the number of cities in the problem. Comparisons of the formulations are made by projecting out variables in order to produce polytopes in the same space. It is then possible to compare the strengths of the Linear Programming relaxations. These results are illustrated by computational results on a small problem

Threshold Accepting (TA) is a powerful optimisation heuristic from the class of evolutionary algorithms. Using several examples from economics, econometrics and statistics, the issues related to implementations of TA are discussed and demonstrated. A problem specific implementation involves the definition of a local structure on the search space, the analysis of the objective function and of constraints, if relevant, and the generation of a sequence of threshold values to be used in the acceptance-rejection-step of the algorithm. A routine approach towards setting these implementation specific details for TA is presented, which will be partially data driven. Furthermore, fine tuning of parameters and the cost and benefit of restart versions of stochastic optimisation heuristics will be discussed

The dependence structure of a family of self exciting threshold autoregressive moving average (SETARMA) models, is investigated. An alternative representation for this class of models is proposed and the exact autocorrelation function is derived in the case of two regimes. Some practical implications of the theoretical results are analysed and discussed via several examples of SETARMA structures of fixed orders

An account is given of a variety of linear filters which can be used for extracting trends from economic time series and for generating de-trended series. A family of rational square-wave filters is described which enable designated frequency ranges to be selected or rejected. Their use is advocated in preference to other filters which are commonly used in quantitative economic analysis

The conventional dyadic multiresolution analysis constructs a succession of frequency intervals in the form of (π/2, π/2); = 1, 2, …, of which the bandwidths are halved repeatedly in the descent from high frequencies to low frequencies. Whereas this scheme provides an excellent framework for encoding and transmitting signals with a high degree of data compression, it is less appropriate to statistical data analysis. A non-dyadic mixed-radix wavelet analysis which allows the wave bands to be defined more flexibly than in the case of a conventional dyadic analysis is described. The wavelets that form the basis vectors for the wave bands are derived from the Fourier transforms of a variety of functions that specify the frequency responses of the filters corresponding to the sequences of wavelet coefficients

The measurement of core inflation can be carried out by optimal signal extraction techniques based on the multivariate local level model, by imposing suitable restrictions on its parameters. The various restrictions correspond to several specialisations of the model: the core inflation measure becomes the optimal estimate of the common trend in a multivariate time series of inflation rates for a variety of goods and services, or it becomes a minimum variance linear combination of the inflation rates, or it represents the component generated by the common disturbances in a dynamic error component formulation of the multivariate local level model. Particular attention is given to the characterisation of the optimal weighting functions and to the design of signal extraction filters that can be viewed as two sided exponentially weighted moving averages applied to a cross-sectional average of individual inflation rates. An empirical application relative to U.S. monthly inflation rates for 8 expenditure categories is proposed