Defuzzification Functionals of Ordered Fuzzy Numbers

Kosiński, Witold; Prokopowicz, Piotr; Rosa, Agnieszka

doi:10.1109/tfuzz.2013.2243456

Cited by 42 publications

(27 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the regularity of grid transportation networks provide a modelling framework enabling significant simplification of the evaluation of the overall network performance the ordered fuzzy numbers concept (Kosiński et al 2013) and hybrid techniques (Sitek and Wikarek 2015) joining MLP and CP may dramatically decrease computation time requirements. Coupling both the grid-like structure based methodology of transportation networks and a new problem modelling concepts may result in a new generation of DSS providing online solutions to real-life-sized communication transportation problems.…”

Section: Discussionmentioning

confidence: 99%

Multimodal processes prototyping subject to grid-like network and fuzzy operation time constraints

2017

View full text Add to dashboard Cite

This paper describes a Grid-like Material Transportation Network (GMTN) in which several heterogeneous means of transportation (Automated Guided Vehicles (AGVs), hoists, lifts, etc.) interact with each other via common shared workstations to provide a variety of demand-responsive material handling operations. Different material handling transport modes provide movement of workpieces between workstations along their manufacturing routes in the GMTN and they can be seen as processes realized with synergic utilization of various local periodically acting unimodal processes. The main contribution of this research is the solution of a constraint satisfaction problem addressing AGVs fleet match-up scheduling subject to GMTN and fuzzy operation time constraints. In the presented case both production rate (production takt) and operations execution time are described by imprecise (fuzzy) data. In other words, the research's objective concerns assessing grid-like networks of periodically acting local transportation modes from the perspective of possible mass-customized oriented requirements imposed on scheduling of multimodal flows of jobs assigned to certain technological routes passing through common shared workstations.

show abstract

Section: Discussionmentioning

confidence: 99%

Multimodal processes prototyping subject to grid-like network and fuzzy operation time constraints

2017

View full text Add to dashboard Cite

show abstract

“…• Point forecast: Given by the value of a defuzzification operator (for defuzzification operators see [2,6] …”

Section: Forecasting Using the Ofar(p) Modelmentioning

confidence: 99%

Ordered Fuzzy Candlesticks

Marszałek

Burczyński

2017

Theory and Applications of Ordered Fuzzy Numbers

View full text Add to dashboard Cite

The purpose of this chapter is to present how Ordered Fuzzy Numbers (OFNs) can be used with financial high-frequency time series. Considering this approach the financial data are modeled using OFNs called further ordered fuzzy candlesticks. Their use allows modeling uncertainty associated with financial data and maintaining more information about price movement at an assumed time interval than compared to commonly used price charts (e.g., Japanese Candlestick chart). Furthermore, in a simple way, it is possible to include the information about volume and the bid-ask spread. Thanks to the well-defined arithmetic of OFNs, one can be used in technical analysis or to construct models of fuzzy time series in the form of classical equations. Examples of an ordered fuzzy moving average indicator and ordered fuzzy autoregressive process are presented. IntroductionHigh-frequency financial data are observations on financial variables such as quotations of shares, futures, or currency pairs, quoted daily or at a finer timescale. Data containing the most complete knowledge about quotations of the financial instrument are prices corresponding to each single transaction made on this instrument.

show abstract

“…The model of constructing defuzzification functionals presented in [19] allows us to obtain a number of defuzzification functionals, whether linear or nonlinear. In this chapter we applied the nonlinear center of gravity defuzzification functional, defined by the following equations.…”

Section: Definition 2 Letmentioning

confidence: 99%

OFN Capital Budgeting Under Uncertainty and Risk

Chwastyk¹,

Pisz²

2017

Theory and Applications of Ordered Fuzzy Numbers

View full text Add to dashboard Cite

The aim of this chapter is to propose a new approach to incorporating uncertainty into capital budgeting. The chapter presents methods that can be used by an investor when the decision maker wants to be able to make an investment decision where there are alternative investment projects. This kind of problem is undertaken under the conditions of uncertainty and risk using Ordered Fuzzy Numbers (OFN). The starting point is the concept of Ordered Fuzzy Numbers. The chapter illustrates the implementation of the proposed approach with an example where two alternative investment projects are analyzed. The authors present the capital budgeting problem using a numerical example. The described methods dedicated to investment project selection lay the foundations for a fuzzy decision-making system. We utilize computer software such as MATLAB to demonstrate how the proposed methods can be applied to assessing the profitability of alternative investment projects.

show abstract

Defuzzification Functionals of Ordered Fuzzy Numbers

Cited by 42 publications

References 19 publications

Multimodal processes prototyping subject to grid-like network and fuzzy operation time constraints

Multimodal processes prototyping subject to grid-like network and fuzzy operation time constraints

Ordered Fuzzy Candlesticks

OFN Capital Budgeting Under Uncertainty and Risk

Contact Info

Product

Resources

About