Fuzzy probabilities for web planning

Buckley, James J.; Reilly, Kevin D.; Zheng, Xidong

doi:10.1007/s00500-003-0305-z

Cited by 21 publications

(8 citation statements)

References 16 publications

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“…(See [3], [4]) Since our model is described by normal distribution, we only discuss fuzzy calculations related to this distribution, while other calculations are mentioned more generally in [8].…”

Section: Fuzzy Calculationsmentioning

confidence: 99%

A fuzzy approach on simulating and optimizing the performance of a discrete-event production line

Rahnama

2010

The 40th International Conference on Computers &Amp; Indutrial Engineering

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Applications of fuzzy set theory are spreading widely into different research areas. The main differences between fuzzy and non-fuzzy simulation approaches are related to the most important attribute of fuzzy approaches and that is modeling uncertain key system parameters (in this study, specifically by fuzzy triangular numbers) with vague data, and therefore obtaining more realistic systematic outputs, whilst utilizing overused non-fuzzy methodologies with the assumption on certainty of gathered data results in enormous computational errors in some cases. In this study, after a short introduction on related efforts in this area, we examine a fuzzy approach for simulating a production line system, which includes three phases: production and manufacturing system analysis, constraint and cost analysis and fuzzy modeling, implementation and simulating with GPSS/H. Since a general view on fuzzy set theory was required, such general concepts in simulating our fuzzy production line are being deliberated after the system analysis phase in an additional section and some references are included for the topics related to our approach. Moreover, by using this fuzzy methodology, optimum solutions in the stable production lines (especially in managerial situations) can be reached as well.

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Section: Fuzzy Calculationsmentioning

confidence: 99%

A fuzzy approach on simulating and optimizing the performance of a discrete-event production line

Rahnama

2010

The 40th International Conference on Computers &Amp; Indutrial Engineering

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“…Note that the proposed method is suitable for fuzzy, interval, crisp, and mixed numbers. More detailed discussions of the theory and applications of constrained fuzzy arithmetic operations can be found in [19,25,26]. Next, we give two numerical examples to illustrate the proposed method.…”

Section: The Fuzzy Analytic Network Processmentioning

confidence: 99%

A Constrained Fuzzy Arithmetic Method for the Fuzzy Analytic Network Process

Huang

Tzeng

2007

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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In this paper, we extend the analytic network process (ANP) by using fuzzy judgments to resolve the problem of uncertainty. Instead of providing crisp global weights, the proposed method, called the fuzzy analytic network process (FANP), provides both fuzzy local weights and fuzzy global weights. The method can help decisionmakers understand the uncertainty degree of a problem before making the final decision.

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“…This produces fuzzy probability distributions. This procedure has been discussed before in [2][3][4][5][6][7][8] but for completeness we include some of these details below. With fuzzy probability distributions we will then compute fuzzy numbers for system performance and we have a fuzzy system.…”

Section: Fuzzy Systemmentioning

confidence: 99%

Fuzzy systems

Buckley

2005

Soft Comput

Self Cite

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In this paper we consider systems whose performance depends on probability distributions and some of the parameters in these probability distributions are to be estimated from data. We argue that all such systems become fuzzy systems whose performance will depend on fuzzy probability distributions and whose measures of performance can be described by fuzzy numbers. Many crisp systems will become fuzzy systems under this procedure.

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Fuzzy probabilities for web planning

Cited by 21 publications

References 16 publications

A fuzzy approach on simulating and optimizing the performance of a discrete-event production line

A fuzzy approach on simulating and optimizing the performance of a discrete-event production line

A Constrained Fuzzy Arithmetic Method for the Fuzzy Analytic Network Process

Fuzzy systems

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