• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.338-341
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• 2005

In this paper, we present analytic forms of the ordered weighted averaging (OWA) operator weighting functions, each of which has properties of rank-based weights and a constant level of orness, irrespective of the number of objectives considered. These analytic forms provide significant advantages for generating OWA weights over previously reported methods. First, OWA weights can be efficiently generated by use of proposed weighting functions without solving a complicated mathematical program. Moreover, convex combinations of these specific OWA operators can be used to generate OWA operators with any predefined values of orness once specific values of orness are α priori stated by decision maker. Those weights have a property of constant level of orness as well. Finally, OWA weights generated at a predefined value of orness make almost no numerical difference with maximum entropy OWA weights in terms of dispersion.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.273-278
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• 2009

The determination of the associated weights in the theory of ordered weighted averaging (OWA) operators is one of the important issue. Recently, Wang and Parkan [Information Sciences 175 (2005) 20-29] proposed a minimax disparity approach for obtaining OWA operator weights and the approach is based on the solution of a linear program (LP) model for a given degree of orness. Recently, Liu [International Journal of Approximate Reasoning, accepted] showed that the minimum variance OWA problem of Fuller and Majlender [Fuzzy Sets and Systems 136 (2003) 203-215] and the minimax disparity OWA problem of Wang and Parkan always produce the same weight vector using the dual theory of linear programming. In this paper, we give an improved proof of the minimax disparity problem of Wang and Parkan while Liu's method is rather complicated. Our method gives the exact optimum solution of OWA operator weights for all levels of orness, $0\leq\alpha\leq1$, whose values are piecewise linear and continuous functions of $\alpha$.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.499-505
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• 2006

In this note, we give an elementary simple new proof of the main result of $Full{\acute{e}}r$ and Majlender [Fuzzy Sets and systems 136 (2003) 203-215] concerning obtaining minimal variability OWA operator weights.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.537-541
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• 2006

In this note, we give an elementary simple proof of the main result of $Full{\acute{e}}rand$ Majlender [Fuzzy Sets and systems 124(2001) 53-57] concerning obtaining maximal entropy OWA operator weights.

• Journal of applied mathematics & informatics
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• v.37 no.3_4
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• pp.201-205
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• 2019

This note provides the solution equivalence of general minimum variance and minimax disparity problems for OWA operator. This result generalize a main theorem of Liu [International Journal of Approximate Reasoning, 48 (2008) 598-627.]

• East Asian Journal of Business Economics (EAJBE)
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• v.3 no.2
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• pp.21-25
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• 2015

Investment process on startup companies faces several difficulties based on the characteristics of this type of companies, such as lack of historical data, current operating losses and absence of comparable companies. In this paper we focus in a new methodology based on ordered weighted averaging (OWA) operators. OWA operators are useful instruments that enable the aggregation of information; in other words, from a data set we are able to obtain a single representative value of that set. The investment methodology presented consists on the application of OWA operators to the targeted startup companies based on the capacity of cash-flow generation and also on the planned scenario of future growth for each company. This paper shows that the methodology proposed can serve as a valuable tool, complementing the qualitative criteria (which, obviously, should not be ignored) for assessing and selecting a start-up investment.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1788-1792
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• 2006

The ordered weighted averaging (OWA) operator by Yager has received more and more attention since its appearance. One key point in the OWA operator is to determine its associated weights. Among numerous methods that have appeared in the literature, we notice the maximum entropy OWA (MEOWA) weights that are determined by taking into account two appealing measures characterizing the OWA weights. Instead of maximizing the entropy in the formulation for determining the MEOWA weights, the new method in the article tries to obtain the OWA weights which are evenly spread out around equal weights as much as possible while strictly satisfying the orness value provided in the program. This consideration leads to the least squared OWA (LSOWA) weighting method in which the program tries to obtain the weights that minimize the sum of deviations from the equal weights since entropy is maximized when the weights are equal. Above all, the LSOWA weights display symmetric allocations of weights on the basis of equal weights. The positive or negative allocations of weights from the median as a basis depend on the magnitude of orness specified. Further interval LSOWA weights are constructed when a decision-maker specifies his or her value of orness in uncertain numerical bounds.

• Asia pacific journal of information systems
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• v.16 no.1
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• pp.85-101
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• 2006

Actual type of aggregation performed by an ordered weighted averaging (OWA) operator heavily depends upon the weighting vector. A number of approaches have been suggested for obtaining the associated weights. In this paper, we present analytic forms of OWA operator weighting functions, each of which has such properties as rank-based weights and constant value of orness, irrespective of number of objectives aggregated. Specifically, we propose four analytic forms of OWA weighting functions that can be positioned at 0.25, 0.334, 0.667, and 0.75 on the orness scale. The merits for using these weights over other weighting schemes can be mentioned in a couple of ways. Firstiy, we can efficiently utilize the analytic forms of weighting functions without solving complicated mathematical programs once the degree of orness is specified a priori by decision maker. Secondly, combined with well-known OWA operator weights such as max, min, and average, any weighting vectors, having a desired value of orness and being independent of the number of objectives, can be generated. This can be accomplished by convex combinations of predetermined weighting functions having constant values of orness. Finally, in terms of a measure of dispersion, newly generated weighting vectors show just a few discrepancies with weights generated by maximum entropy OWA.

• Journal of Wind Energy
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• v.15 no.1
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• pp.50-59
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• 2024

Floating LiDAR systems provide significant savings in cost and time compared to the fixed meteorological mast measurement type, and have the advantage of being able to be deployed in various locations due to less restriction on the depth of the installation site. However, to use the wind data collected by a floating LiDAR system commercially, verification procedure is required to ensure that the collected data have sufficient availability. The Carbon Trust OWA roadmap presents guidelines in three stages for the reliability of the wind data collected using a floating LiDAR system. Companies developing wind farms are requesting at least Stage 2 (pre-commercial stage) presented by OWA, and many overseas companies are leading the domestic and overseas markets. In this paper, we introduce the case of OWA Stage 2 certification for the commercial operation of floating LiDAR systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.265-268
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• 2007

다기준 의사결정 문제에서 요인간의 가중치 계산과 계산된 요인의 평가값 종합화는 매우 중요하다. 본 연구는 다기준 의사결정 문제에 있어서 의사결정자의 의사전략 결합기법을 도출하고 다기준의사결정 문제로 적용하였다. 복잡한 환경에서 의사결정을 할 때 발생되는 모호함을 해결하기 위해 주관적 의견을 결합한 퍼지지합 이론을, 다기준 문제의 요인을 퍼지값으로 계층화하기 위해 계층분석법을 적용하였다. 또한, 의사결정자의 의사전략을 결합하기 위해 순위 가중치평균법을 이용하였다. 순위가 있는 가중치 평균방법은 퍼지집합의 orness 특성을 이용하여 의사결정자의 주관적 의지를 반영할 수 있는 기법으로, 순위가중치평균(OWA) 연산자에 따른 낙관적 혹은 비관적인 정도에 따라 주관적인 의도를 반영할 수 있는 방법이다. 다기준의사결정 문제의 적용사례로서 해상교통안전을 위한 대기정박지의 위치분석 문제를 본 연구에서 제시한 방법에 따라 적용하였다.