39,284 research outputs found
Fuzzy Logic Use for Decision Making in Construction Industry
Decision making in a construction industry is a tough job of project manager due to complexnature of industry. This project presents a fuzzy approach for decision making using a fuzzy expert system to give an additional tool to the project manager while taking adecision. This project defines various selection criteria for contractor selection with using fuzzy logic and fuzzy approach to select best suited from alternatives. Fuzzy set theory is related to inexact and vague information which we deal in the construction industry. Construction clients had realized through the last decades that the lowest price bid is not always the best. Evaluation of contractors based on multi-criteria basis is, therefore, becoming more important to the construction industry. This project describes the decision-making model for selection of a contractor for construction projects
Multi crteria decision making and its applications : a literature review
This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM
A methodology for the selection of new technologies in the aviation industry
The purpose of this report is to present a technology selection methodology to
quantify both tangible and intangible benefits of certain technology
alternatives within a fuzzy environment. Specifically, it describes an
application of the theory of fuzzy sets to hierarchical structural analysis and
economic evaluations for utilisation in the industry. The report proposes a
complete methodology to accurately select new technologies. A computer based
prototype model has been developed to handle the more complex fuzzy
calculations. Decision-makers are only required to express their opinions on
comparative importance of various factors in linguistic terms rather than exact
numerical values. These linguistic variable scales, such as ‘very high’, ‘high’,
‘medium’, ‘low’ and ‘very low’, are then converted into fuzzy numbers, since it
becomes more meaningful to quantify a subjective measurement into a range rather
than in an exact value. By aggregating the hierarchy, the preferential weight of
each alternative technology is found, which is called fuzzy appropriate index.
The fuzzy appropriate indices of different technologies are then ranked and
preferential ranking orders of technologies are found. From the economic
evaluation perspective, a fuzzy cash flow analysis is employed. This deals
quantitatively with imprecision or uncertainties, as the cash flows are modelled
as triangular fuzzy numbers which represent ‘the most likely possible value’,
‘the most pessimistic value’ and ‘the most optimistic value’. By using this
methodology, the ambiguities involved in the assessment data can be effectively
represented and processed to assure a more convincing and effective decision-
making process when selecting new technologies in which to invest. The prototype
model was validated with a case study within the aviation industry that ensured
it was properly configured to meet the
Optimization of fuzzy analogy in software cost estimation using linguistic variables
One of the most important objectives of software engineering community has
been the increase of useful models that beneficially explain the development of
life cycle and precisely calculate the effort of software cost estimation. In
analogy concept, there is deficiency in handling the datasets containing
categorical variables though there are innumerable methods to estimate the
cost. Due to the nature of software engineering domain, generally project
attributes are often measured in terms of linguistic values such as very low,
low, high and very high. The imprecise nature of such value represents the
uncertainty and vagueness in their elucidation. However, there is no efficient
method that can directly deal with the categorical variables and tolerate such
imprecision and uncertainty without taking the classical intervals and numeric
value approaches. In this paper, a new approach for optimization based on fuzzy
logic, linguistic quantifiers and analogy based reasoning is proposed to
improve the performance of the effort in software project when they are
described in either numerical or categorical data. The performance of this
proposed method exemplifies a pragmatic validation based on the historical NASA
dataset. The results were analyzed using the prediction criterion and indicates
that the proposed method can produce more explainable results than other
machine learning methods.Comment: 14 pages, 8 figures; Journal of Systems and Software, 2011. arXiv
admin note: text overlap with arXiv:1112.3877 by other author
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Applying concepts of fuzzy cognitive mapping to model IT/IS investment evaluation factors
The justification process is a major concern for many organisations that are considering the adoption of Information Technology (IT) and Information Systems (IS), and is a barrier to its implementation. As a result, the competitive advantage of many companies is being put at risk because of management's inability to evaluate the holistic implication of adopting new technology, both in terms of on the benefit and cost portfolios. This paper identifies a number of well-known project appraisal techniques used in IT/IS investment justification. Furthermore, the concept of multivalent, or fuzzy logic, is used to demonstrate how inter-relationships can be modeled between key dimensions identified in the proposed conceptual evaluation model. This is highlighted using fuzzy cognitive mapping (FCM) as a technique to model each IT/IS evaluation factor (integrating strategic, tactical, operational and investment considerations). The use of an FCM is then shown to be as a complementary tool which can serve to highlight interdependencies between contributory justification factors
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