12,086 research outputs found
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 âvery highâ, âhighâ,
âmediumâ, âlowâ and âvery lowâ, 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 âthe most likely possible valueâ,
âthe most pessimistic valueâ and âthe most optimistic valueâ. 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
Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of B.W.M. and interval valued intuitionistic fuzzy T.O.D.I.M.
open access articleDeveloping and accepting industry 4.0 influences the industry structure and customer willingness. To a successful transition to industry 4.0, implementation strategies should be selected with a systematic and comprehensive view to responding to the changes flexibly. This research aims to identify and prioritise the strategies for implementing industry 4.0. For this purpose, at first, evaluation attributes of strategies and also strategies to put industry 4.0 in practice are recognised. Then, the attributes are weighted to the expertsâ opinion by using the Best Worst Method (BWM). Subsequently, the strategies for implementing industry 4.0 in Fara-Sanat Company, as a case study, have been ranked based on the Interval Valued Intuitionistic Fuzzy (IVIF) of the TODIM method. The results indicated that the attributes of âTechnologyâ, âQualityâ, and âOperationâ have respectively the highest importance. Furthermore, the strategies for ânew business models developmentâ, âImproving information systemsâ and âHuman resource managementâ received a higher rank. Eventually, some research and executive recommendations are provided. Having strategies for implementing industry 4.0 is a very important solution. Accordingly, multi-criteria decision-making (MCDM) methods are a useful tool for adopting and selecting appropriate strategies. In this research, a novel and hybrid combination of BWM-TODIM is presented under IVIF information
Multi-criteria decision making with linguistic labels: a comparison of two methodologies applied to energy planning
This paper compares two multi-criteria decision making (MCDM) approaches based on linguistic label assessment. The first approach consists of a modified fuzzy TOPSIS methodology introduced by Kaya and Kahraman in 2011. The
second approach, introduced by Agell et al. in 2012, is based on qualitative reasoning techniques for ranking multi-attribute alternatives in group decision-making with linguistic labels. Both approaches are applied to a case of assessment and selection of the most suitable types of energy in a geographical area.Peer ReviewedPostprint (published version
A framework for the selection of the right nuclear power plant
Civil nuclear reactors are used for the production of electrical energy. In the nuclear industry vendors propose several nuclear reactor designs with a size from 35â45âMWe up to 1600â1700âMWe. The choice of the right design is a multidimensional problem since a utility has to include not only financial factors as levelised cost of electricity (LCOE) and internal rate of return (IRR), but also the so called âexternal factorsâ like the required spinning reserve, the impact on local industry and the social acceptability. Therefore it is necessary to balance advantages and disadvantages of each design during the entire life cycle of the plant, usually 40â60 years. In the scientific literature there are several techniques for solving this multidimensional problem. Unfortunately it does not seem possible to apply these methodologies as they are, since the problem is too complex and it is difficult to provide consistent and trustworthy expert judgments. This paper fills the gap, proposing a two-step framework to choosing the best nuclear reactor at the pre-feasibility study phase. The paper shows in detail how to use the methodology, comparing the choice of a small-medium reactor (SMR) with a large reactor (LR), characterised, according to the International Atomic Energy Agency (2006), by an electrical output respectively lower and higher than 700âMWe
Fuzzy subjective evaluation of Asia Pacific airport services
This paper presents a fuzzy decision-making model to determine the ranking of fourteen Asia Pacific airports based on the services provided to passengers. Airport services were represented by six attributes namely comfort, processing time, convenience, courtesy of staff, information visibility and security. Data for the attributes given by travel experts are in the triangular fuzzy number form. Based on fuzzy set and approximate reasoning, the model allows decision makers to make the best choice in accordance with human thinking and reasoning processes.The use of fuzzy rules which are extracted directly from the input data in making evaluation, contributes to a better decision and is less dependent on experts.Experimental results show that the proposed model is comparable to previous studies.The model is suitable for various fuzzy environments
Can fuzzy Multi-Criteria Decision Making improve Strategic planning by balanced scorecard?
Strategic management is momentous for organizational success and competitive advantage in an increasingly turbulent business environment. Balanced scorecard (BSC) is a framework for evaluating strategic management performance which translates strategy into action via various sets of performance measurement indicators. The main objective of this research is to develop a new fuzzy group Multi-Criteria Decision Making (MCDM) model for strategic plans selection process in the BSC. For this to happen, the current study has implemented linguistic extension of MCDM model for robust selection of strategic plans. The new linguistic reasoning for group decision making is able to aggregate subjective evaluation of the decision makers and hence create an opportunity to perform more robust strategic plans, despite of the vagueness and uncertainty of strategic plans selection process. A numerical example demonstrates possibilities for the improvement of BSC through applying the proposed model
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