ABSTRACT The choice and distribution of the optimal fleet mix of helicopters to service the offshore air operation is an important logistical problem that has a potential to reduce costs considered in the oil and gas industry. The variability of helicopter models and sizes, as well as the possible operational restrictions of airport bases and airspace, requires that adequate numerical techniques of Operational Research are applied. In this context, this work develops a model to determine the trajectories and distances traveled between airport bases and offshore maritime units and vice-versa, through an airspace modeled by a directed graph. Also develop a helicopter performance model to obtain the estimated payload and flight times for each specified mission and last but not least an integer linear programming model to allocate an optimal fleet and airport mix for passenger transport through the use of different helicopter sizes. The model was applied to offshore units operating in the Santos basin due to their strategic importance in the oil and gas industry. The results obtained included a map of preferred regions for two different helicopter sizes (large and medium), the weekly flight tables showing the allocation of helicopters and airport for different demand scenarios, additionally the impact of fuel prices on different airport bases and the concentration of movements on air routes are also evaluated in order to complement the analysis.

Este artigo apresenta uma metodologia de análise organizacional que busca a capitalização do conheci-mento, focada na melhoria dos processos de negócio. Baseado em um problema real de uma empresa do setor elétrico, essa metodologia é aplicada para o transporte de cargas excepcionais indivisíveis (CEI). Nessa empresa, o capital intelectual é um ativo crítico para o desempenho dos serviços e ele é fundamental para a excelência do negócio. A abordagem aplicada ao processo de gerenciamento do transporte de uma grande empresa brasileira de suprimento de energia elétrica mostrou-se adequada às metas organizacionais do transporte de CEI. Uma revisão destaca definições e estruturas para a melhoria dos processos de negócio previamente propostas na literatura. Os resultados da aplicação da metodologia são apresentados ao final do artigo.

This paper presents an organizational analysis methodology aimed at knowledge capitalization with a view to improving business processes. Based on a real problem in a large electric sector firm, this methodology is applied to managing the transport of indivisible exceptional cargo (IEC). In the firm in question, intellectual capital is a critical asset for service performance and is fundamental for achieving business excellence. Applied to the firm's transport management process, the approach proved appropriate to IEC transport-related organizational goals. A review highlights definitions and structures for improving business processes proposed previously in the literature. The results of applying the methodology are discussed at the end of the paper.

Um das políticas de gestão de estoques mais conhecidas e utilizadas é o sistema de lote econômico com ponto de pedido, também conhecido como sistema <Q, r>. O objetivo deste artigo é apresentar uma nova abordagem para o cálculo do ponto de pedido, baseada na previsão da demanda para cada período durante o tempo de ressuprimento. Erros de previsão e tempos de ressuprimento são as variáveis aleatórias consideradas sendo o ponto de pedido calculado com base na distribuição composta desses parâmetros. Simulações são realizadas para avaliar a adequação do modelo proposto, sob condições tais como: diferentes distribuições do lead-time, da demanda e diferentes magnitudes do erro de previsão. A experimentação conduzida procura avaliar tanto a validade do modelo teórico proposto quanto os possíveis desvios das premissas consideradas, principalmente a da demanda ser considerada normalmente distribuída no lead-time.

One of the most known policy of inventory management is the order quantity and reorder point, or <Q, r> policy. The purpose of this paper is to present a new analytical approach for evaluating reorder points in inventory management policies. This approach considers the reorder point as a function of forecasted demands in the planning horizon, which can vary through time. Lead-times and forecast errors are the uncertainties present in the model. The composed distribution of these parameters is used to obtain an analytical expression for the proposed forecast based reorder point. Simulation experiments are performed in order to validate and test the accuracy of the proposed model. The experiments consider different lead-time and demand distributions and also different magnitude of forecast errors. Beyond the accuracy of the analytical model the simulations seeks to evaluate the premises adopted, mainly the one of normally distributed lead-time demand.