Virgílio José Martins Ferreira Filho

Lower and upper bounds for a two-level hierarchical location problem in computer networks

In this paper a two-level hierarchical model for the location of concentrators and routers in computers networks is presented. Given a set of candidate locations and the capacities of concentrators and routers the problem is to determine how many concentrators and routers should be used, where they should be located and which users to assign to each concentrator and which concentrators to allocate to each router. A Lagrangean relaxation is used to provide lower bounds for this problem, which are tentatively strengthened by incomplete optimization through the use of CPLEX. A tabu search meta-heuristic is then developed. Computational experiments are performed using a set of randomly generated problems.

Scheduling a multi-product pipeline network

Abstract Daily some millions barrels of oil are moved around the world in imports and exports and domestically within countries. While ships are the main mode for intercontinental transport, pipelines are the chief form of transcontinental transport, while regional and local transports is performed by trains and trucks. Despite high installation costs, pipelines are considered highly efficient as a mode for transporting large amounts of oil and oil products over long distances, because they offer lower operation costs, higher reliability rates, lower product loss rates, less environmental impact, and less susceptibility to adverse weather conditions than other modes. This study deals with a multi-product pipeline system that transports a set of oil products (diesel, gasoline and kerosene, for example), which have to be moved from points (operating areas) where they are produced or stored (refineries, terminals) to points where they are needed (other refineries, distribution centers, terminals, ports, customers) through a pipeline or set of pipelines. The present study contributes primarily by offering an efficient tool for the problem of scheduling multi-product pipeline networks. The methodology proposed takes the approach of discretizing both pipelines and planning horizon and combines an efficient MILP model with a post-processing heuristic. When compared with previous models, we propose a more efficient one in which the set of volumetric constraints is modeled in the form of knapsack cascading constraints and constraints on products in pipeline sections, which made for significantly improved performance in the experiments that were conducted. The proposed methodology thus constitutes an advance in terms of modeling the problem, making it feasible to solve problems increasingly close to the realities confronting oil industry operators.

Logistics network planning for offshore air transport of oil rig crews

Abstract Oil discoveries of recent years, especially in the pre-salt Santos Basin, reflect a large increase in petroleum exploration and production in Brazil. Accordingly, drilling rig and production platform crew transport demands will increase. This transport will also become more complex as average distance between fields and Brazil’s coast increases. The helicopter, the modal most used for this purpose, is the most efficient means of transport in terms of speed and safety, but also entails high costs. Optimizing the crew transport logistics network thus becomes an economically significant issue. The study presents an optimization model for crew transport logistics network planning. That model aims to provide managers with accurate information to assist their decision making in logistics infrastructure planning. Such decisions involve airfield locations, distribution of demand among airfields and fleet profile. Since composing the fleet involves considerable expenditures, and once made, this composition is not easily changed, we built several scenarios varying in demand and fleet costs to evaluate the behavior of the model we are proposing as regards processing time and quality of the solution. We have obtained good results, despite the increasing complexity of the scenarios.

Design optimization of oilfield subsea infrastructures with manifold placement and pipeline layout

Abstract This paper presents a practical and effective optimization method to design subsea production networks, which accounts for the number of manifolds and platforms, their location, well assignment to these gathering systems, and pipeline diameter. It brings a fast solution that can be easily implemented as a tool for layout design optimization and simulation-based analysis. The proposed model comprises reservoir dynamics and multiphase flow, relying on multidimensional piecewise linearization to formulate the layout design problem as a MILP. Besides design validation, reservoir simulation serves the purpose of defining boundaries for optimization variables and parameters that characterize pressure decrease, reservoir dynamics and well production over time. Pressure drop in pipelines are modeled by piecewise-linear functions that approximate multiphase flow simulators. The resulting optimization model and approximation methodology were applied to a real oilfield with the aim of assessing their effectiveness.

Optimizing the Location of Platforms and Manifolds

This paper presents a general method for optimize platform and manifold location in offshore projects, by maximizing their net present value. Finding the best offshore facilities locations is further than minimize the total pipelines length. The distances between wells and platform are related to their productivity, the farther the distance the less production. Each well has a particular response to pressure loss along the pipelines due to fluids and reservoir characteristics. Therefore, not only costs related to distances must be taken in account, but also revenues from the oil production, which are a function of flow conditions in the reservoir, tubing and pipelines. Moreover, to locate a manifold also means choose which wells will be linked with it. Every possible combination of wells leads to a different production rate. Again, petroleum engineering expertise it is needed to optimize the project, working together numerical techniques. A simple optimization method is proposed and to testify it a small example model was built.Copyright

Knowledge management for improving business processes: an analysis of the transport management process for indivisible exceptional cargo

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 firms 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.

Roteamento de veículos com base em sistemas de informação geográfica

An algorithm for vehicle routing is embedded into a Geographical Information System (GIS), from the database of which it extracts the information needed for the routing and where it displays the resulting routes. The routing algorithm is a simulated annealing metaheuristic that produces good quality routes in reduced computational times. We describe the embedding of the algorithm into a specific GIS software and the application of the routing system to a simulated delivery schedule in the neighbourhood of Copacabana, in Rio de Janeiro.

Cálculo do ponto de pedido baseado em previsões de uma política de gestão de estoques

One of the most known policy of inventory management is the order quantity and reorder point, or 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.

Oil industry value chain simulation with learning agents

Abstract Simulation is an important tool to evaluate many systems, but it often requires detailed knowledge of each specific system and a long time to generate useful results and insights. A large portion of the required time stems from the need to define operational rules and build valid models that represent them properly. To shorten this model construction time, a learning-agent-based model is proposed. This technique is recommended for cases where optimal policies are not known or hard and costly to unequivocally determine, as it enables the simulation agents to learn good policies “by themselves”. A model is built with this technique and a representative case study of oil industry value chain simulation is presented as a proof of concept.

Uncertainty Analysis in the Production History Matching by Using Proxies as Reservoir Simulators

In the recent literature of the production history matching the problem of non-uniqueness of reservoir simulation models has been considered a difficult problem. Complex workflows have been proposed to solve the problem. However, the reduction of uncertainty can only be done with the definition of Probability Density Functions that are highly costly. In this article we introduce a methodology to reduce uncertainty in the history matching using techniques of Monte Carlo performed on proxies as Reservoir Simulator. This methodology is able to compare different Probability Density Functions for different reservoir simulation models to define among the models which simulation model can provide more appropriate matching.Copyright