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General introduction
Published in Adedeji B. Badiru, Handbook of Industrial and Systems Engineering, 2013
The aggregation in both MCDM and ME-MCDM is usually done by an averaging function or by using the maximum or minimum value among the scores assigned to that alternative. Yager (1988) introduced the ordered weighted averaging (OWA) aggregation method, in which linguistic quantifiers are used in the aggregation function. This approach allows decision making on the basis of linguistic requirements such as "choose the best alternative based on most of the criteria," or based on "all of the experts."
How to assess investments in industry 4.0 technologies? A multiple-criteria framework for economic, financial, and sociotechnical factors
Published in Production Planning & Control, 2022
Rodrigo Pessotto Almeida, Néstor Fabián Ayala, Guilherme Brittes Benitez, Francisco José Kliemann Neto, Alejandro Germán Frank
After standardising the scales of the indicators, this step involves determining an overall index. We adopted an aggregation function known as the ordered weighted aggregation (OWA). The OWA is a useful aggregation technique and widely used for multicriteria aggregation that determines a decision-making process (Jin, Mesiar, and Yager 2019). In this sense, to aggregate the three indicators, we adapt the method proposed by Merigó and Casanovas (2011), called induced Euclidean ordered weighted averaging distance (IEOWAD). This method is an extension of OWA, which uses inducing variables to assign different weights to the criteria in constructing the overall index. IEOWAD allows the decision-making to take into consideration more complex reordering processes that can describe the decision problem in a complete way (Merigó and Casanovas 2011). Equation 14 was used to determine the tridimensional overall index, called the integrative investment index (IIIi), where IIIi represents the integrative investment index for the potential technology investment i.
Evaluating optimal sites for combined-cycle power plants using GIS: comparison of two aggregation methods in Iran
Published in International Journal of Sustainable Energy, 2020
Hazhir Karimi, Alireza Soffianian, Sadri Seifi, Saeid Pourmanafi, Hadi Ramin
Suitable sites were achieved by using WLC and Fuzzy logic methods. Both methods used in this study are efficient methods to aggregate data and layers. These methods offer more flexibility than other MCE methods such as Boolean logic and Overlay. These methods allow criteria to be standardised continuously and let the criteria to be differentially weighted. However, using fuzzy logic leads to accurate results. It is recommended to consider additional parameters such as technical and strategic parameters and use a similar approach in future studies. It is also suggested to use other aggregation methods such as Ordered Weighted Averaging (OWA) and Analytical Hierarchy Process (AHP) and compare the results with the methods in this study. Based on the literature reviews, it was found that using OWA method, both criteria weights and order weights are considered that makes more complicated uncertainty and decision strategy. In future analyses, additional data layers and additional parameters could be added based on site-specific problems and requirements.
Sustainable Management of the Supply Chain Based on Fuzzy Logic
Published in Cybernetics and Systems, 2021
Luciano Barcellos de Paula, Anna María Gil-Lafuente, Aline de Castro Rezende
OWA operators provide flexibility in the modeling and simulation process, since it is defined by a vector of weights and not by a single parameter. As advantages for its use, OWA helps in the analysis of complex systems and facilitates the decision-makings in scenarios of uncertainty. As a limitation, the result will depend on the quality of information received. This algorithm was used successfully in several investigations (Alfaro-García et al. 2019; Blanco-Mesa, Gil-Lafuente, and Merigó 2017; Merigó and Gil-Lafuente 2011; Vizuete-Luciano et al. 2015) and for these reasons; we chose it for this study.