Interior point models for power system stability problems

In this paper, a detailed analysis of the use of optimization techniques in the study of voltage stability problems, leading to the incorporation of voltage stability criteria in traditional Optimal Power Flow (OPF) formulations is presented. Optimal power flow problems are highly nonlinear programming problems that are used to find the optimal control settings in electrical power systems. The relationship between the Lagrangian Multipliers of the OPF problem and the classification of the maximum loading point level of the system is given. Finally, the paper presents a sequential OPF technique to enhance voltage stability using reactive power and voltage rescheduling with no increase in real (active) generation cost.     

Using simulated annealing to optimize the feature selection problem in marketing applications

The feature selection (also, specification) problem is concerned with finding the most influential subset of predictors in predictive modeling from a much larger set of potential predictors that can contain hundreds of predictors. The problem belongs to the realm of combinatorial optimization where the objective is to find the subset of variables that optimize the value of some goodness of fit function. Due to the dimensionality of the problem, the feature selection problem belongs to the group of NP-hard problems. Most of the available predictors are noisy or redundant and add very little, if any, to the prediction power of the model. Using all the predictors in the model often results in strong over-fitting and very poor predictions. Constructing a prediction model by checking out all possible subsets is impractical due to computational volume. Looking on the contribution of each predictor separately is not accurate because it ignores the inter-correlations between predictors. As a result, no analytic solution is available for the feature selection problem, requiring that one resorts to heuristics. In this paper we employ the simulated annealing (SA) approach, which is one of the leading stochastic search methods, for specifying a large-scale linear regression model. The SA results are compared to the results of the more common stepwise regression (SWR) approach for model specification. The models are applied on realistic data sets in database marketing. We also use simulated data sets to investigate what data characteristics make the SWR approach equivalent to the supposedly more superior SA approach.     

Clustering of financial time series with application to index and enhanced index tracking portfolio

A stochastic-optimization technique based on time series cluster analysis is described for index tracking and enhanced index tracking problems. Our methodology solves the problem in two steps, i.e., by first selecting a subset of stocks and then setting the weight of each stock as a result of an optimization process (asset allocation). Present formulation takes into account constraints on the number of stocks and on the fraction of capital invested in each of them, whilst not including transaction costs. Computational results based on clustering selection are compared to those of random techniques and show the importance of clustering in noise reduction and robust forecasting applications, in particular for enhanced index tracking.     

Some Remarks on Pseudotrees

This paper provides new results on pseudotrees. First, it is shown that pseudotrees are precisely those posets for which consistent sets, directed sets, and nonempty chains coincide. Second, we show that chain-complete pseudotrees yield complete meet-semilattices. Third, we prove that pseudotrees are precisely those posets that admit a set representation by sets of appropriate chains. This latter result generalizes results needed for applications in game theory and economics.     

Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case

A comparative study of the performance of a number of classificatory devices, both parametric (LDA and Logit) and non-parametric (perceptron neural nets and fuzzy-rule-based classifiers) is conducted, and a Monte Carlo simulation-based approach is used in order to measure the average effects of sample size variations on the predictive performance of each classifier. The paper uses as a benchmark the problem of forecasting the level of profitability of Spanish commercial and industrial companies upon the basis of a set of financial ratios. This case illustrates well a distinctive feature of many financial prediction problems, namely that of being characterized by a high dimension feature space as well as a low degree of separability. Response surfaces are estimated in order to summarize the results. A higher performance of model-free classifiers is generally observed, even for fairly moderate sample sizes.     

A two-bottleneck system with binomial yields and rigid demand

This study considers multistage production systems where production is in lots and only two stages have non-zero setup costs. Yields are binomial and demand, needing to be satisfied in its entirety, is “rigid”. We refer to a stage with non-zero setup cost as a “bottleneck” (BN) and thus to the system as “a two-bottleneck system” (2-BNS). A close examination of the simplest 2-BNS reveals that costs corresponding to a particular level of work in process (WIP) depend upon costs for higher levels of WIP, making it impossible to formulate a recursive solution.For each possible configuration of intermediate inventories a production policy must specify at which stage to produce next and the number of units to be processed. We prove that any arbitrarily “fixed” production policy gives rise to a finite set of linear equations, and develop algorithms to solve the two-stage problem. We also show how the general 2-BNS can be reduced to a three-stage problem, where the middle stage is a non-BN, and that the algorithms developed can be modified to solve this problem.     

Improved local search algorithms for the rectangle packing problem with general spatial costs

The rectangle packing problem with general spatial costs is to pack given rectangles without overlap in the plane so that the maximum cost of the rectangles is minimized. This problem is very general, and various types of packing problems and scheduling problems can be formulated in this form. For this problem, we have previously presented local search algorithms using a pair of permutations of rectangles to represent a solution. In this paper, we propose speed-up techniques to evaluate solutions in various neighborhoods. Computational results for the rectangle packing problem and a real-world scheduling problem exhibit good prospects of the proposed techniques.     

Neural networks and organizational systems: Modeling non-linear relationships

For decades, organizational researchers have employed standard statistical methods to uncover relationships among variables and constructs. However, in complex organization systems, the prevalence of non-linearity and outliers is to be expected. Under such circumstances, the use of standard statistical methods becomes unreliable and, correspondingly, results in degraded predictions of the relationships within the organizational systems. We describe the use of neural network analyses to model team effectiveness so as to provide more accurate predictions for managers.     

Impulsive control and synchronization of the Lorenz systems family

In this paper, impulsive control and synchronization for the newly presented Lorenz systems family are systematically investigated. Some new and more comprehensive criteria for global exponential stability and asymptotical stability of impulsively controlled Lorenz systems family are established with varying impulsive intervals. In particular, several simple and easily verified criteria are derived with equivalent impulsive intervals. An illustrative example is also provided to show the effectiveness and feasibility of the impulsive control method.     

No limit cycle in two species second order kinetics

We give a new and direct proof of the nonexistence of limit cycle in a bimolecular system and the characterization of the unique bimolecular oscillator. The proof is an application of classification theorems on vector fields with homogeneous second degree polynomial perturbations.