Fuzzy Sets and Information Granularity

Zadeh, L. A.

doi:10.1142/9789814261302_0022

Cited by 1,067 publications

(1,127 citation statements)

References 8 publications

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“…In some cases, uncertainty exists not due to randomness but fuzziness where doubt arises about the correctness of statements, exactness of concepts and judgments having little to do with occurrence of events (Luhandjula, 2007). This type of uncertainty is handled using fuzzy set theory which was developed by Zadeh (1965). Vahdani et al (2012) defined a novel approach for designing a reliable network of production in closed loop supply chain under uncertainty.…”

Section: Literature Of Past Workmentioning

confidence: 99%

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

Khalili‐Damghani¹,

Ghasemi²

2016

Industrial Engineering and Management Systems

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Complex and uncertain issues in supply chain result in integrated decision making processes in supply chains. So decentralized (distributed) decision making (DDM) approach is considered as a crucial stage in supply chain planning. In this paper, an uncertain DDM through coordination mechanism is addressed for a multi-product supply chain planning problem. The main concern of this study is comparison of DDM approach with centralized decision making (CDM) approach while some parameters of decision making are assumed to be uncertain. The uncertain DDM problem is modeled through fuzzy mathematical programming in which products' demands are assumed to be uncertain and modeled using fuzzy sets. Moreover, a CDM approach is customized and developed in presence of fuzzy parameters. Both approaches are solved using three fuzzy mathematical optimization methods. Hence, the contribution of this paper can be summarized as follows: 1) proposing a DDM approach for a multi-product supply chain planning problem; 2) Introducing a coordination mechanism in the proposed DDM approach in order to utilize the benefits of a CDM approach while using DDM approach; 3) Modeling the aforementioned problem through fuzzy mathematical programming; 4) Comparing the performance of proposed DDM and a customized uncertain CDM approach on multiproduct supply chain planning; 5) Applying three fuzzy mathematical optimization methods in order to address and compare the performance of both DDM and CDM approaches. The results of these fuzzy optimization methods are compared. Computational results illustrate that the proposed DDM approach closely approximates the optimal solutions generated by the CDM approach while the manufacturer's and retailers' decisions are optimized through a coordination mechanism making lasting relationship. .

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Section: Literature Of Past Workmentioning

confidence: 99%

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

Khalili‐Damghani¹,

Ghasemi²

2016

Industrial Engineering and Management Systems

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“…Due to the fuzzy nature of many practical problems, a number of fuzzy clustering methods have been developed following the general fuzzy set theory strategies outlined by Zadeh in his fundamental paper [1]. The main difference between the traditional crisp clustering and fuzzy clustering can be stated as follows.…”

Section: Preliminary Remarksmentioning

confidence: 99%

Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering

Viattchenin¹,

Shyrai²

2015

CAE

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This paper introduces a novel intuitionistic fuzzy set-based heuristic algorithm of possibilistic clustering. For the purpose, some remarks on the fuzzy approach to clustering are discussed and a brief review of intuitionistic fuzzy set-based clustering procedures is given, basic concepts of the intuitionistic fuzzy set theory and the intuitionistic fuzzy generalization of the heuristic approach to possibilistic clustering are considered, a general plan of the proposed clustering procedure is described in detail, two illustrative examples confirm good performance of the proposed algorithm, and some preliminary conclusions are formulated.

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“…In 1933, Kolmogorov founded the theory of probability axiomatic theorem. The theory of fuzzy set was established by Zadeh (1965). Rough set theory was presented by Pawlak (1982) for the first time in 1982.…”

Section: Introductionmentioning

confidence: 99%

Some Properties of Complex Uncertain Process

You¹,

Na²

2016

Industrial Engineering and Management Systems

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Uncertainty appears not only in real quantities but also in complex quantities. Complex uncertain process is essentially a sequence of complex uncertain variables indexed by time. In order to describe complex uncertain process, a formal definition of complex uncertain distribution is given in this paper, as well as the concepts of independence and variance. In addition, some properties of complex uncertain integral are presented.

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Fuzzy Sets and Information Granularity

Cited by 1,067 publications

References 8 publications

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering

Some Properties of Complex Uncertain Process

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