Abstract
Preliminary normalization is central to the decision process of several popular, recent or completely new multi-attribute decision-making (MADM) methods. However, a number of authors have pointed out serious pitfalls attributed to normalization methods. One major pitfall, which has been identified, is that normalization methods may lead to different final rankings of alternatives when a ranking procedure (RP) based on them is used for solving a MADM problem. The current paper aims to ascertain and illustrate the effectiveness of some RPs based on prominent primary WEighted Self-NORmalizing Distance (WESNORD) metrics and their averages. The effectiveness of the selected RPs is demonstrated by solving a logistics service provider (LSP) selection problem taken from the literature. The results reveal that the RPs considered deliver final rankings of alternatives, which are very similar to the SAW-produced reference ranking.
Highlights
Multi-attribute decision-making (MADM) is a prominent branch of operations research and management science
One major pitfall, which has been identified, is that normalization methods may lead to different final rankings of alternatives when a ranking procedure (RP) based on them is used for solving a multi-attribute decision-making (MADM) problem
The current paper aims to ascertain and illustrate the effectiveness of some RPs based on prominent primary WEighted Self-NORmalizing Distance (WESNORD) metrics and their averages
Summary
Multi-attribute decision-making (MADM) is a prominent branch of operations research and management science. In MADM, preliminary normalization (i.e., mathematical transformation of all initial attribute values to eliminate the effects of different scales of measurement before using a given method) is central to the decision process of various well-established, recent or completely new methods. One major pitfall attributed to normalization methods is that they may lead to different final rankings of alternatives when a ranking procedure (RP) based on them is used for solving a MADM problem. For at least this reason, the current work aims to ascertain and illustrate the effectiveness of some RPs based on prominent primary WEighted Self-NORmalizing Distance (WESNORD) metrics and their averages (see Section 2 and Section 3 for details). The fifth section concludes the article and points out two directions for future research
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