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Traditional MADM and New Hybrid MADM for Problem Solving
Published in Gwo-Hshiung Tzeng, Kao-Yi Shen, New Concepts and Trends of Hybrid Multiple Criteria Decision Making, 2017
The supermatrix is further used to calculate the weighted supermatrix by transforming the sums all of columns to unity; this idea is very similar to the concept of a Markov chain. Then, as z approaches infinity in Equation 5.4, a stable weighted supermatrix WSANP can be obtained.
An assessment of the policy gap in port selection of liner shipping companies
Published in Transportation Letters, 2021
Wen-Kai Hsu, Show-Hui Sheree Huang, Wen-Jui Tseng, Dong-Feng Li
In the traditional ANP approach, a supermatrix is created to assess the criteria’s weights. The supermatrix consists of three sub-matrixes: positive reciprocal matrix, network cluster matrix, and alternative matrix, in which the network cluster matrix is used to assess the interrelations among criteria. However, to measure the matrix, respondents need to score the pair-wise comparisons of relative importance among criteria from the perspective of a certain criterion. In practice, such comparisons could be difficult for respondents to make, thus reducing the validity of data collection, and limiting the ANP’s practical applications. Instead of the network cluster matrix, this paper proposes an influential matrix to measure the interrelations among criteria. Specifically, this study proposes an influential graph to assist respondents in scoring the degrees of influence of criteria. The influential graph is easy to use, leading to an improvement of the proposed IANP approach in practical applications.
Evolving a comprehensive geomatics multi-criteria evaluation index model for optimal pipeline route selection
Published in Structure and Infrastructure Engineering, 2020
Isa Adekunle Hamid-Mosaku, Olatoye Fatai Oguntade, Victoria Ifeoma Ifeanyi, Abdul-Lateef Balogun, Olalekan Abeeb Jimoh
The supermatrix represents the influence of elements within the network on each other. This data is required to calculate the weights of all the factors, sub-factors, sub-sub factors, parameters, and alternatives in the model (Saaty & Vargas, 2006). Furthermore, the primary spatial data was collected through field observations (route and bathymetry survey-referenced to established datum) and GPS was used to obtain the coordinates of features in the study area, which was integrated into the GIS environment. Secondary data, including Google Earth images, geological maps, drainage pattern maps, Land cover and road maps were also used for the route selection process. A 2017 30 m resolution LANDSAT 8 satellite imagery of the study area was mosaicked and processed using ENVI 5.0 software in order to extract other relevant geospatial data.
A Lean Implementation Success Model for the Construction Industry
Published in Engineering Management Journal, 2020
Sevilay Demirkesen, Hasan Gokberk Bayhan
In this research, the relative weights are determined by interviewing the experts in the industry after setting the relationships among the nodes or clusters, which is a common practice in ANP methodology (Aydogan & Koksal, 2013; Chemweno et al., 2015; Cheng & Li, 2005; X. K. Li et al., 2019). Moreover, Superdecisions, a software developed by Saaty, were used to implement ANP (T. L. Saaty, 2001; R. W. Saaty, 2003). In this software, the matrices are input and the software generates a “Supermatrix,” “Weighted Supermatrix” and “Limiting Supermatrix” as the outputs revealing the importance weight of the nodes. However, the matrices generated through Superdecisions are not presented in this paper due to space limitations. The model is used to weight and compare the influences of 27 factors on the Lean implementation success model for the construction industry aiming to reveal the power of influence for each factor.