Empresas Integradas Formando Cadeias de Valor Digitais que Competem Entre Si: Por que o Contexto Aponta Nesta Direção e uma Proposta de Modelos de Negócio para sua Implementação

The Business environment and the technological evolution are pushing the company’s integration toward the formation of extended enterprises. In this environment the competition won’t be between firms, but will hapen at the level of the digital value chain. The DVC will be formed by the eletronic connection of all actors at the value chain. In this article we present the business and technological scenario that sign the trends for this new competition model. In the second part of the article we define the concepts of the digital value chain presenting three possible business models to implement it. Keywords: Extended enterprise, supply chain management, digital value chai

Comparação entre genetic fuzzy system e neuro fuzzy system para seleção de poços de petróleo para fraturamento hidráulico

A operação de fraturamento hidráulico é muito utilizada para estimular a produção de poços de petróleo e na remoção de danos de formação. Para selecionar os poços candidatos a sofrerem esta operação, engenheiros utilizam estudos de reservatórios e análises de engenharia. Nos últimos anos, sistemas de inferência fuzzy tem atraído o interesse de pesquisadores desta área devido às características nebulosas das variáveis envolvidas neste processo de seleção. Este estudo compara a performance de um neuro fuzzy system e um genetic fuzzy system, aplicados à seleção de poços de petróleo para a operação de fraturamento hidráulico, com aquisição de conhecimento de uma base de dados para ajuste de suas funções de pertinência. Foram utilizando dados de treinamento e dados de validação idênticos nos dois sistemas e concluímos que, embora mais recente, o genetic fuzzy system obteve melhores resultados neste processo de seleção. Também concluímos que, devido à possibilidade de inclusão de restrições, o genetic fuzzy system resultou em funções de pertinência mais coerentes com o que se esperava dos valores lingüísticos das principais variáveis.</p

Oil industry value chain simulation with learning agents

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.</p

Learning-agent-based simulation for queue network systems

Established simulation methods generally require from the modeller a broad and detailed knowledge of the system under study. This paper proposes the application of Reinforcement Learning in an Agent-Based Simulation model to enable agents to define the necessary interaction rules. The model is applied to queue network systems, which are a proxy for broader applications, in order to be validated. Simulation tests compare results obtained from learning agents and results obtained from known good rules. The comparison shows that the learning model is able to learn efficient policies on the go, providing an interesting framework for simulation.</p

A Markov Chain approach to multicriteria decision analysis with an application to offshore decommissioning

This paper proposes a novel approach that makes use of continuous-time Markov chains and regret functions to find an appropriate compromise in the context of multicriteria decision analysis (MCDA). This method was an innovation in the relationship between uncertainty and decision parameters, and it allows for a much more robust sensitivity analysis. The proposed approach avoids the drawbacks of arbitrary user-defined and method-specific parameters by defining transition rates that depend only upon the performances of the alternatives. This results in a flexible and easy-to-use tool that is completely transparent, reproducible, and easy to interpret. Furthermore, because it is based on Markov chains, the model allows for a seamless and innovative treatment of uncertainty. We apply the approach to an oil and gas decommissioning problem, which seeks a responsible manner in which to dismantle and deactivate production facilities. The experiments, which make use of published data on the decommissioning of the field of Brent, account for 12 criteria and illustrate the application of the proposed approach

Optimal preventive policies for parallel systems using Markov decision process: application to an offshore power plant

This work proposes a Markov Decision Process (MDP) model for identifying windows of opportunities to perform preventive maintenance for multi-unit parallel systems subject to a varying demand. The main contribution lies in proposing: (i) a reward function that does not depend on maintenance costs, which are typically difficult to assess and classify; and (ii) a new metric for prevention.By optimizing the capacity utilization rate and the decision flexibility, which is denoted in terms of standby units, for a set of typical operational scenarios, the optimal opportunities for preventive interventions are identified within the respective prevention ranges, in relation to an offshore power plant (case study). The sequential decision problem is solved using the Value Iteration algorithm to obtain the optimal long-term policies.As a result, a backlog management decision-support solution is developed, using a low-cost computational model, which provides scenario-dependent preventive policies and promotes the integration of operations with maintenance, being easy to implement, maintain and communicate with stakeholders