Proposta para mensuração de custos em cadeia de suprimentos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.

Alignment of operations strategy: Exploring the marketing interface

Purpose - The purpose of this paper is to identify factors that can help managers to overcome barriers to alignment of operations strategy at the interface with marketing. Design/methodology/approach - This objective required the application of a procedure based on strategic consensus and a deeper analysis, such that the delimitation of the study in a single case was mandatory. The strategic processes of interfacing involve managerial attributes that are subject to the influence of human aspects and, therefore, the research method used a qualitative approach. The protocol design included the following data sources: interviews, document reviews and researcher observations. The categorisation was made based on the theoretical references, the frequency of observations, common responses and information from documents. Findings - The balance between intra-functional trade-offs, joint research on the competitive context, reflections on the understanding of customer needs and operational performance, and understanding of inter-functional trade-offs were the main factors verified. They effectively support decisions associated with interface processes and promotes the integration of these processes. They can generate inputs that enable managers to achieve an appropriate balance among alternatives in light of various trade-offs. Practical implications - These factors make possible new connections between strategic processes in the context of operations and marketing functions. The formations of these strategies are aligned through a better understanding of both threats and opportunities by means of a joint analysis of the competitive context. The presented findings can be used to develop a clear definition of strategic objectives of operations and a more appropriate treatment of market needs. Originality/value - The findings from the research can be considered as new elements for promoting alignment in the formation process of the operations strategy. Little research to date has examined the operations-marketing strategic interface of companies in the context of strategic consensus. © Emerald Group Publishing Limited.

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science

Low-carbon operations and production: putting training in perspective

The purpose of this paper is mainly to show how training may support low-carbon operations and production management in a more sustainable organizational context. Design/methodology/approach – A conceptual framework to facilitate the integration between training and low-carbon operations and production is presented. Findings – To accomplish better training in a low-carbon organization, some steps should be followed. Challenges may occur, including the necessity of collaboration across the supply chain. Research limitations/implications – The proposed framework should be applied and improved based on the actual conditions in organizations.