Course Overview

A practical workshop on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Scholars will learn how to evaluate and rank alternatives based on proximity to the ideal solution, applying TOPSIS across research domains such as supply chain, sustainability, healthcare, and engineering.

Learning Outcomes

• Understand the principles and steps of TOPSIS.
• Construct and normalize decision matrices.
• Apply weights to criteria and identify ideal/negative-ideal solutions.
• Calculate closeness coefficients and rank alternatives.
• Compare TOPSIS with other MCDM methods (AHP, VIKOR, Fuzzy).
• Use TOPSIS for real datasets and present results in research-ready tables and visuals.

Tools & Resources

• Software: Excel templates, R (MCDA/TOPSIS packages), Python (scikit-criteria, pyMCDM), MATLAB.
• Visualization: Tableau, Power BI, Python (Matplotlib, Seaborn).
• AI/Assistive Tools: ChatGPT, Elicit for automated calculation support, visualization, and reporting.