TOPSIS: Technique for Order Preference by Similarity to Ideal Solution

The function is provided for TOPSIS methodology with Information Entropy Weighting Methodology.
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Aggiornato 20 mag 2016

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Of the numerous criteria decision-making (MCDM) methods, TOPSIS is a practical and useful technique for ranking and selecting a number of possible alternatives by measuring Euclidean distances. TOPSIS, is a simple ranking method in conception and application. The TOPSIS method based on information entropy is proposed as a decision support tool in many fields. The purpose of this methodology is to first arrive at an ideal solution and a negative ideal solution, and then find a scenario which is nearest to the ideal solution and farthest from the negative ideal solution.
Upon submission of an article to any Journal and conference, an author is required to transfer copyright in the article by citing below reference:
Sianaki, O. A. (2015). Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid. (PhD), Curtin University, Curtin University Library. Retrieved from http://espace.library.curtin.edu.au/R?func=dbin-jump-full&local_base=gen01-era02&object_id=240088 (240088)
I have already provided the ELECTRE function code that you may want to download it,
The link of the video for explaining the implementation of function and methodology is :
https://youtu.be/0_imbSU7mH4

Cita come

Omid Ameri Sianaki (2025). TOPSIS: Technique for Order Preference by Similarity to Ideal Solution (https://it.mathworks.com/matlabcentral/fileexchange/57143-topsis-technique-for-order-preference-by-similarity-to-ideal-solution), MATLAB Central File Exchange. Recuperato .

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Versione Pubblicato Note della release
1.1.0.0

In update version I just added an example and a video link to show how to implement the function code.
You need to make your own decision making matrix and change the data range for variables (input and output).
Update is about adding the training video link and presenting an example

1.0.0.0