Decision Making on Oil Extraction under Z-information

Intelligent Automation & Soft Computing, 2014

Decision making theories are based on decision relevant information much of which is uncertain, imprecise or incomplete. An important qualitative attribute of information on which decisions are based is its reliability. The concept of Z-number relates to the issue of reliability of information, especially in the realms of economics and decision analysis. In this study we suggest Z-information based decision-making method which is more realistic in comparison with the existing methods. An example of investment problem is used to illustrate the proposed approach.

ICTACT Journal on Soft Computing, 2014

The concept of decision making under uncertainty is usually associated with information that may be incomplete, not reliable or imprecise, so there are several types of uncertainty. A partial absence of beliefs and fuzziness are some of the aspects of uncertainty. In this paper we consider a somewhat different framework for representing our knowledge. Zadeh suggested a Z-number notion, based on a reliability of the given information. In this study we apply Zinformation to decision making in business problem and suggest the framework for decision making on a base of Z-numbers. The method associates with the construction of a non-additive measure as a lower prevision and uses this capacity in Choquet integral for constructing a utility function. An example of real-world business problem is used to illustrate the proposed approach.

International Journal of Energy Economics and Policy, 2020

In this paper, we study models for ranking renewables and a mix of energy resources in the development of long-term energy policy. Energy resource selection is a multi-criteria decision-making problem, characterized by incomplete information, uncertainties and intangibles, competitive priorities and contradictory requirements. The uniqueness of the problem and the limited relevant data necessitates the use of expert opinion as a principal source of information. All these factors contribute to subjectivity and vagueness in the decision-making process. Taking into consideration these circumstances, fuzzy information and a Z-number-based analytic hierarchy process (AHP) has been used as a decision-making tool. Z-number represents both the restriction and the reliability of evaluation and, due to these characteristics, it provides a better description of the uncertainty. Numerical examples and comparative analysis of fuzzy and Z-number-based models illustrate the process to solution and results.

Decision Making: Applications in Management and Engineering, 2019

Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the decision reached following Boolean logic. However, human thinking logic is more complex and include the ability to process uncertainty. In addition, in value of information assessment, it is often desirable to make decisions based on multiple economic criteria, which, independently evaluated, may suggest opposite decisions. Artificial intelligence has been used successfully in several areas of knowledge, increasing and enhancing analytical capabilities. This paper aims to enrich the value of information methodology by integrating fuzzy logic into the decisionmaking process; this integration makes it possible to develop a human thinking assessment and coherently combine several economic criteria. To the authors' knowledge, this is the first use of a fuzzy inference system in the domain of value of information. The methodology is successfully applied to a case study of an oil and gas subsurface assessment where the results of the standard and fuzzy methodologies are compared, leading to a more robust and complete evaluation.

Procedia Computer Science, 2016

The decision of shipping lines as to which port to use is a strategic one, and it is one of the most crucial factors to influence the operational and business performance of the organizations. The existing decision theories are not sufficiently adequate to account for imprecision and partial reliability of decision-relevant information in real-world problems. Prof. Zadeh suggested the concept of a Z-number which is able to formalize imprecision and partial reliability of information. In this article, we consider a hierarchical multiattribute decision problem of an optimal port choice under Z-number-based information. The solution of the problem is based on the use of Z-number-valued weighted average aggregation operator. The obtained results show validity of the suggested approach.

2010

Sound decision making requires the elicitation and quantification of key uncertainties. Probabilities are, in general, subjective and most petro-technical experts find assessing them challenging. Furthermore, much evidence shows that, although they may not be aware of it, assessors find it difficult to make unbiased assessments.

2018

The Z-number concept is the attempt to model real-world uncertainty and relates to the issue of reliability of information, especially in the realms of economics and decision analysis. In this paper, we present an approach that can handle Z-numbers in the context of multi-criteria decision-making applying direct computations. In this paper we consider an Anаlytical Hierarchy Process based on Znumber valuations, taking into account the uncertаinty of the experts` opinion in estimаtion of the options. We considered a case of estimation of technical institutions with 7 criteria: campus infrastructure, faculty, students, academic ambience, teaching learning process, use of advance teaching aid, supplementary process and 3 alternatives: technical institutions.

International Journal of Applied Decision Sciences, 2020

Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the decision reached by following Boolean logic. However, human thinking is based on a more complex logic that includes the ability to process uncertainty. In value of information assessment, it is often desirable to make decisions based on multiple economic criteria which, if independently evaluated, may suggest opposite decisions. Artificial intelligence has been used successfully in several areas of knowledge, increasing and enhancing analytical capabilities. This paper aims at enriching the value of information methodology by integrating fuzzy logic into the decision-making process; this integration makes it possible to develop a human thinking assessment and coherently combine several economic criteria. To the authors' knowledge, this is the first use of a fuzzy inference system in the specified knowledge domain. The methodology is successfully applied to a case study of an oil and gas subsurface assessment where the results of the standard and fuzzy methodologies are compared, leading to a more robust and complete evaluation. Sensitivity analysis is undertaken for several membership functions used in the case study to assess the impact that shifting, narrowing and stretching the membership relationship has on the value of information. The results of the sensitivity study show that, depending on the shifting, the membership functions lead to different decisions; additional sensitivities to the type of membership functions are investigated, including the functions' parameters.

Intelligent Automation and Soft Computing, 2017

Regression analysis is widely used for modeling of real-world processes in various fields. It should be noted that information relevant to real-world processes is characterized by imprecision and partial reliability. This involves combination of fuzzy and probabilistic uncertainties. Prof.. L. Zadeh introduced the concept of a Z-number as a formal construct for dealing with such information. The present stateof-the-art of regression analysis under Z-number valued information is very scarce. In this paper we consider a Z-number valued multiple regression analysis and its application to a real-world decisionmaking problem. The obtained results show applicability of the proposed approach.

Mathematical Models for Handling Partial Knowledge in Artificial Intelligence, 1995

We motivate situations in which knowledge can take different forms. Depending upon the form of the available knowledge (data), appropriate mathematical tools for analysis are considered. These include subjective probabilities, lower probabilities, the Choquet integral, random sets, measure-free representation of conditionals (rules), and rule-based procedures.