Preference selection index (PSI) method is a novel tool to select best alternative from given alternatives without deciding relative importance between attributes.
The proposed methodology is based on Preference selection index (PSI) method.
The MCDM techniques used in this work are Analytical Hierarchal Process (AHP), Preference Selection Index (PSI) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).
However, traditional IP architecture has faced several known challenges, such as mobility, multihoming, privacy, path preference selection, etc., which should be resolved in FWNs.
This paper explores the application of a novel MCDM method i.e. Preference selection index (PSI) method to solve various decision-making problems that are generally encountered in the design stage of production system life cycle.
Where the data matrix B represents 'voting' data, such that (b_{ij}) indicates that object (X^{i}) voted positively for object (Y^{j}), one may consider that the preference selection ('1' or '0') is a forced binary decision on a continuous variable that represents preference.
A validation and consistency test of preference selection index method is performed in present work by comparing results of PSI method with published results of graph theory and matrix approach (GTMA), and technique for order preference by similarity to ideal solution (TOPSIS) method, respectively.
Findings are illustrated as follows (Table 3): Table 3 McNemar Test of Preferential Attribute Randomness McNemar Test Scenario Based Preference Selection Attribute Recognition Response Chose Local Chose International (QAB) Sum Population Attribute Present in Local 2246.56 1017 3263.56 Attribute Present in International (QAB) 2119.44 1153.44 3272.88 Sum 4366 2170.44 6536.44.
As the established sustainability model consists of both quantitative and qualitative factors, the qualitative factors were transformed into numerical values prior to performing the materials selection process, aided by the Preference Selection Index (PSI) and Principal Component Analysis (PCA) decision-making/supporting tools.
