Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry.

To manage uncertainty in reservoir development projects, the Value of Information is one of the main factors on which the decision is based to determine whether it is necessary to acquire additional data. However, subsurface data is not always precise and is characterized by a certain level of fuzziness. In this paper, a model is formulated to assess the Value of Information in the oil and gas industry in cases where the data proposed to be acquired is imprecise. The methodology is based on the use of fuzzy data modelling and analysis aimed at providing decision support for oil field developers. An oilfield from North Africa is used as a case study to show how the methodology works. This work shows how the analysis can be utilized to reach financial decisions on the necessity of additional data acquisition

FUZZY COMPARATIVE CONCORDANCE ANALYSIS. Proposal and evaluation by a case study

In this paper it is proposed a fuzzy multiple attribute analysis, that we have called comparative concordance, as a help instrument to the decision-making process in an environment of lack of precise information as it generally is the decision-making in regional planning. Through an application to the selection of proceeding programs of the Environmental Plan of Andalusia, 1995-2000, it will be compared to other methods.fuzzy sets, multiple attribute decision, environmental planning

Performance evaluation of the social security branches in Tehran using a combination of fuzzy data analysis model and balanced scorecard

Speed ​​and diversity of the changes in the world around us have had a profound impact on all institutions of societies and faced them with multiple challenges. These challenges make organizations to achieve continuous progress using new management systems and implementing them practically. In this regard, one of the main tasks of managers is undoubtedly monitoring and evaluating the performance of their organization under their supervision. Senior managers have always been looking for a way to make sure of the implementation of their strategies and in this regard they consider the evaluation of organization performance as an inevitable necessity. But, what is raised as the main question before organizations and managerial advisors is that; by what means and how to identify basic problems and issues and areas to improve the organization and prepare oneself to participate in international competitions successfully? Therefore, existence of a model seems to be necessary and reason able to improve the performance of various branches of organizations and to achieve a tool to meet this need. One of these models is using a combination of data envelopment analysis model and balanced scorecard. Using fuzzy theory in this model can precisely control the factors affecting the performance of organization and also provides a clear picture of it. In this model, after determining and estimating the dimensions of balanced scored card in four aspects of: customer, internal processes, growth and learning and finance, the efficiency of branches is rated and ranked by using fuzzy data envelopment analysis and eventually the effective and ineffective branches are identified

Performance evaluation of the social security branches in Tehran using a combination of fuzzy data analysis model and balanced scorecard

Speed ​​and diversity of the changes in the world around us have had a profound impact on all institutions of societies and faced them with multiple challenges. These challenges make organizations to achieve continuous progress using new management systems and implementing them practically. In this regard, one of the main tasks of managers is undoubtedly monitoring and evaluating the performance of their organization under their supervision. Senior managers have always been looking for a way to make sure of the implementation of their strategies and in this regard they consider the evaluation of organization performance as an inevitable necessity. But, what is raised as the main question before organizations and managerial advisors is that; by what means and how to identify basic problems and issues and areas to improve the organization and prepare oneself to participate in international competitions successfully? Therefore, existence of a model seems to be necessary and reason able to improve the performance of various branches of organizations and to achieve a tool to meet this need. One of these models is using a combination of data envelopment analysis model and balanced scorecard. Using fuzzy theory in this model can precisely control the factors affecting the performance of organization and also provides a clear picture of it. In this model, after determining and estimating the dimensions of balanced scored card in four aspects of: customer, internal processes, growth and learning and finance, the efficiency of branches is rated and ranked by using fuzzy data envelopment analysis and eventually the effective and ineffective branches are identified

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

Different distance measures for fuzzy linear regression with Monte Carlo methods

The aim of this study was to determine the best distance measure for estimating the fuzzy linear regression model parameters with Monte Carlo (MC) methods. It is pointed out that only one distance measure is used for fuzzy linear regression with MC methods within the literature. Therefore, three different definitions of distance measure between two fuzzy numbers are introduced. Estimation accuracies of existing and proposed distance measures are explored with the simulation study. Distance measures are compared to each other in terms of estimation accuracy; hence this study demonstrates that the best distance measures to estimate fuzzy linear regression model parameters with MC methods are the distance measures defined by Kaufmann and Gupta (Introduction to fuzzy arithmetic theory and applications. Van Nostrand Reinhold, New York, 1991), Heilpern-2 (Fuzzy Sets Syst 91(2):259–268, 1997) and Chen and Hsieh (Aust J Intell Inf Process Syst 6(4):217–229, 2000). One the other hand, the worst distance measure is the distance measure used by Abdalla and Buckley (Soft Comput 11:991–996, 2007; Soft Comput 12:463–468, 2008). These results would be useful to enrich the studies that have already focused on fuzzy linear regression models

Behavioural pattern identification and prediction in intelligent environments

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments