Scielo RSS <![CDATA[Pesquisa Operacional]]> vol. 35 num. 2 lang. en <![CDATA[SciELO Logo]]> <![CDATA[GRAPH PROPERTIES OF MINIMIZATION OF OPEN STACKS PROBLEMS AND A NEW INTEGER PROGRAMMING MODEL]]> The Minimization of Open Stacks Problem (MOSP) is a Pattern Sequencing Problem that often arises in industry. Besides the MOSP, there are also other related Pattern Sequencing Problems of similar relevance. In this paper, we show that each feasible solution to the MOSP results from an ordering of the vertices of a graph that defines the instance to solve, and that the MOSP can be seen as an edge completion problem that renders that graph an interval graph. We review concepts from graph theory, in particular related to interval graphs, comparability graphs and chordal graphs, to provide insight to the structural properties of the admissible solutions of Pattern Sequencing Problems. Then, using Olariu'scharacterization and other structural properties of interval graphs, we derive an integer programming model for the MOSP. Some computational results for the model are presented. <![CDATA[THERMAL PERFORMANCE OF REFRIGERATED VEHICLES IN THE DISTRIBUTION OF PERISHABLE FOOD]]> The temperature of refrigerated products along the distribution process must be kept within close limits to ensure optimum food safety levels and high product quality. The variation of product temperature along the vehicle routing sequence is represented by non-linear functions. The temperature variability is also correlated with the time required for the refrigerated unit to recover after cargo unloading, due to the cargo discharging process. The vehicle routing optimization methods employed in traditional cargo distribution problems are generally based on the Travelling Salesman Problem with the objective of minimizing travelled distance or time. The thermal quality of routing alternatives is evaluated in this analysis with Process Capability Indices (PCI). Since temperature does not vary linearly with time, a Simulated Annealing algorithm was developed to get the optimal solution in which the minimum vehicle traveling distance is searched, but respecting the quality level expressed by a required minimum PCI value. <![CDATA[PROJECT SCHEDULING OPTIMIZATION IN ELECTRICAL POWER UTILITIES]]> The problem of choosing from a set of projects which ones should be executed and whenthey should start, depending on several restrictions involving project costs, risks, limited resources, dependencies among projects, and aiming at different, even conflicting, goals is known as the project portfolio selection (PPS) problem. We study a particular version of the PPS problem stemming from the operation of a real power generation company. It includes distinct categories of resources, intricate dependencies between projects, which are especially important for the management of power plants, and the prevention of risks. We present an algorithm based on the GRASP meta-heuristic for finding better results thanmanual solutions produced by specialists. The algorithm yielded solutions that decreased the risk by 47%, as measured by the company's standard methodology. <![CDATA[MRP OPTIMIZATION MODEL FOR A PRODUCTION SYSTEM WITH REMANUFACTURING]]> Science and technology practitioners have been studying how to take economical, social and environmental advantage of industrial residuals and discarded products. In this sense, this paper presents a Material Requirements Planning (MRP) optimization model for a particular production system that, besides manufacturing a final product and its main assembling component, it recovers units of the component from units of the returned product as well. It is assumed that the demand for the final product is independent of the amount of the returned product available, and that the market for the final product is not segmented. Considering that all parameters of the MRP mathematical model have deterministic nature, we prove that this production planning problem is NP-hard. We also show computational experiments with the model using an optimization solver and analyze some possible industrial scenarios, as well. <![CDATA[CLASSIFICATION OF POWER QUALITY CONSIDERING VOLTAGE SAGS IN DISTRIBUTION SYSTEMS USING KDD PROCESS]]> In this paper, we propose a methodology to classify Power Quality (PQ) in distribution systems based on voltage sags. The methodology uses the KDD process (Knowledge Discovery in Databases) in order to establish a quality level to be printed in labels. The methodology was applied to feeders on a substation located in Curitiba, Paraná, Brazil, considering attributes such as sag length (remnant voltage), duration and frequency (number of occurrences on a given period of time). On the Data Mining Stage (the main stage on KDD Process), three different techniques were used, in a comparative way, for pattern recognition, in order to achieve the quality classification for the feeders: Artificial Neural Networks (ANN); Support Vector Machines (SVM) and Genetic Algorithms (GA). By printing a label with quality level information, utilities companies (power concessionaires) can get better organized for mitigation procedures by establishing clear targets. Moreover, the same way costumers already receive information regarding PQ based on interruptions, they will also be able to receive information based on voltage sags. <![CDATA[ANALYTIC NETWORK PROCESS AND BALANCED SCORECARD APPLIED TO THE PERFORMANCE EVALUATION OF PUBLIC HEALTH SYSTEMS]]> The performance of public health systems is an issue of great concern. After all, to assure people's quality of life, public health systems need different kinds of resources. Balanced Scorecard provides a multi-dimensional evaluation framework. This paper presents the application of the Analytic Network Process and Balanced Scorecard in the performance evaluation of a public health system in a typical medium-sized Southeastern town in Brazil. <![CDATA[VENDOR AND LOGISTICS PROVIDER SELECTION IN THE CONSTRUCTION SECTOR: A PROBABILISTIC PREFERENCES COMPOSITION APPROACH]]> The main purpose of the present work is to present an application of the probabilisticpreferences composition method to the vendor and carrier selection process in the construction sector of the Southern Region of Rio de Janeiro state, in Brazil. The traditional measures associated with vendor and carrier evaluation are considered in the model and are combined through a preferences composition, which considers the options' uncertainties. In the application, we considered three different suppliers of sand for construction and three different logistics providers, two of them outsourced. Our intention was to verify if outsourcing can be considered a better solution, as proposed by many authors. The results show thatoutsourcing is the best option for different probabilistic composition points of view as well as when the overall evaluation is obtained by average composition. <![CDATA[A MODEL SELECTION PROCEDURE IN MIXTURE-PROCESS EXPERIMENTS FOR INDUSTRIAL PROCESS OPTIMIZATION]]> We present a model selection procedure for use in Mixture and Mixture-Process Experiments. Certain combinations of restrictions on the proportions of the mixture components can result in a very constrained experimental region. This results in collinearity among the covariates of the model, which can make it difficult to fit the model using the traditional method based on the significance of the coefficients. For this reason, a model selection methodology based on information criteria will be proposed for process optimization. Two examples are presented to illustrate this model selection procedure. <![CDATA[SELECTING PROFILES OF IN DEBT CLIENTS OF A BRAZILIAN TELEPHONE COMPANY: NEW LASSO AND ADAPTIVE LASSO ALGORITHMS IN THE COX MODEL]]> Variable selection plays an important rule in identifying possible factors that could predict the behavior of clients with respect to the bill payments. The Cox model is the standard approach for modeling the time until starting the lack of payments. Parsimony and capacity of predicting are some desirable characteristics of statistical models. This paper aims at proposing a new forward stagewise Lasso (least absolute shrinkage and selection operator) algorithm and applying it for variable selection in the Cox model. The algorithm can be easily extended to run the Adaptive Lasso (ALasso) approach. <![CDATA[SOME ILLUSTRATIVE EXAMPLES ON THE USE OF HASH TABLES]]> Hash tables are among the most important data structures known to mankind. Throughhashing, the address of each stored object is calculated as a function of the object's contents. Because they do not require exorbitant space and, in practice, allow for constant-time dictionary operations (insertion, lookup, deletion), hash tables are often employed in the indexation of large amounts of data. Nevertheless, there are numerous problems of somewhat different nature that can be solved in elegant fashion using hashing, with significant economy of time and space. The purpose of this paper is to reinforce the applicability of such technique in the area of Operations Research and to stimulate further reading, for which adequate references are given. To our knowledge, the proposed solutions to the problems presented herein have never appeared in the literature, and some of the problems are novel themselves.