Introduction to Fuzzy Arithmetic, Theory and Applications.

Bohlender, Gerd; Kaufmann, Arnold; Gupta, Madan M.

doi:10.2307/2008199

Cited by 171 publications

(20 citation statements)

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“…Such sets are characterised by a function of membership which is assigned to each object of the class with a rank that moves within the interval [0,1]. The mathematical operations that are allowed on the sets are: addition, subtraction, multiplication and division (Dubois & Prade, 1979;Kaufmann & Gupta, 1991).…”

Section: The Theory Of Fuzzy Setsmentioning

confidence: 99%

Analysis of the efficiency of insurance companies in Serbia using the fuzzy AHP and TOPSIS methods

Mandic

Delibašić

Knežević

et al. 2017

Economic Research-Ekonomska Istraživanja

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The aim of this study is to propose a fuzzy multi-criteria model that will facilitate the assessment of insurance companies' efficiency. This study includes all companies operating within the insurance sector in Serbia in the period from 2007 to 2014 and the data were used from the published financial statements of insurance companies. Five key indicators were identified for the assessment and rating of insurance companies. Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) were used for building the proposed model. In the first stage, priority weights of criteria were defined by using the FAHP, while in the second phase the insurance companies were ranked using the TOPSIS method. JEL CODES c2; c6; G22 ARTICLE HISTORY

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Section: The Theory Of Fuzzy Setsmentioning

confidence: 99%

Analysis of the efficiency of insurance companies in Serbia using the fuzzy AHP and TOPSIS methods

Mandic

Delibašić

Knežević

et al. 2017

Economic Research-Ekonomska Istraživanja

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“…Their findings show that TOPSIS when integrated with SVNS performs better with incomplete, undetermined and inconsistent information in MCDA problems. As most of the membership functions in the research mentioned above are assumed to be triangular, to find another way to capture the vagueness of the qualitative information, Positive Trapezoidal Fuzzy Number (PTFN) was proposed by Bohlender, Kaufmann, and Gupta (1986) and was introduced by Herrera and Herrera-Viedma (2000) in group decision making problems. C.-T. Chen, Lin, and Huang (2006) adopted PTFN to present a fuzzy decision-making framework to deal with supplier selection problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Resilient supplier selection in logistics 4.0 with heterogeneous information

Hasan

Jiang

Ullah

et al. 2020

Expert Systems with Applications

100

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Supplier selection problem has gained extensive attention in the prior studies. However, research based on Fuzzy Multi-Attribute Decision Making (F-MADM) approach in ranking resilient suppliers in logistic 4.0 is still in its infancy. Traditional MADM approach fails to address the resilient supplier selection problem in logistic 4.0 primarily because of the large amount of data concerning some attributes that are quantitative, yet difficult to process while making decisions. Besides, some qualitative attributes prevalent in logistic 4.0 entail imprecise perceptual or judgmental decision relevant information, and are substantially different than those considered in traditional suppler selection problems. This study develops a Decision Support System (DSS) that will help the decision maker to incorporate and process such imprecise heterogeneous data in a unified framework to rank a set of resilient suppliers in the logistic 4.0 environment.The proposed framework induces a triangular fuzzy number from large-scale temporal data using probability-possibility consistency principle. Large number of non-temporal data presented graphically are computed by extracting granular information that are imprecise in nature. Fuzzy linguistic variables are used to map the qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to generate the ranking score of alternative suppliers. These ranking scores are used as input in a Multi-Choice Goal Programming (MCGP) model to determine optimal order allocation for respective suppliers. Finally, a sensitivity analysis assesses how the Supplier's Cost versus Resilience Index (SCRI) changes when differential priorities are set for respective cost and resilience attributes.

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“…In addition to the aforementioned approximate reasoning view, the concept of linguistic variables, fuzzy quantifiers, fuzzy rules, canonical forms, and connectives plays a key role, and another significant fuzzy logic development arises from mathematically developing the fuzzy set theory [71,72], which is quite vast. Indeed, fuzzy logic is a branch of the fuzzy set theory, and other branches are fuzzy arithmetic, fuzzy mathematical programming, fuzzy topology, and so on [73,74].…”

Section: Inherent Characteristics Of Fuzzy Systems For Handling Uncermentioning

confidence: 99%

“…The second one depends on the discretized fuzzy number proposed in [123]. Additionally, on the basis of the reduced decomposition of the fuzzy number of level cut operations proposed in [71], the third one can be considered as a generalized version of the second.…”

Section: The Goal Is To Determine μQðzþmentioning

confidence: 99%

A survey of fuzzy logic in wireless localization

Alakhras

Oussalah

Hussein

2020

J Wireless Com Network

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Positioning and location estimation of targets either for mobile or stationary nodes is very important, especially after the astonishing developments of the wired and/or wireless communications, as well as the popularity of handheld devices. To assist in accessing services or managing resources, numerous users, application developers, and service providers require accurate position information. Owing to the boom of multisensory systems, wireless technology, and autonomous systems, many positioning systems have been proposed. These positioning systems differ based on the adopted technology, type of sensory information, communication protocol, algorithm, and accuracy level. These different approaches impose a non-negligible amount of uncertainty and challenges; in such case, fuzzy-logic-based localization systems offer an attractive and feasible approach to overcome some of the reported challenges and handle uncertainties, depending on its proven mathematical framework for handling vagueness and uncertainty. Intuitively, fuzzy sets and fuzzy inference systems are initially intended to incorporate granularity and flexibility of human knowledge, which characterize complex system behaviors without explicitly requiring precise mathematical models or when a complete and exhaustive list of comprehending factors are ill known. This study demonstrates the benefits of fuzzy sets, fuzzy logic, and fuzzy inference systems in wireless positioning problems. Notably, as a way to benefit both the academic community and practitioners of system positioning, we review and investigate various fuzzy-related techniques and methodologies. To the best of our knowledge, this is the first comprehensive review that focuses solely on fuzzy-based positioning systems.

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Introduction to Fuzzy Arithmetic, Theory and Applications.

Cited by 171 publications

References 0 publications

Analysis of the efficiency of insurance companies in Serbia using the fuzzy AHP and TOPSIS methods

Analysis of the efficiency of insurance companies in Serbia using the fuzzy AHP and TOPSIS methods

Resilient supplier selection in logistics 4.0 with heterogeneous information

A survey of fuzzy logic in wireless localization

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