Two-stage weighted PROMETHEE II with results' visualization.

Area of Science:

• Operations Research
• Economics
• Regional Science

Background:

• Multicriteria decision-making (MCDA) methods are essential tools for complex problem-solving.
• Existing MCDA methods like Weighted Sum Approach (WSA) and TOPSIS are user-friendly, while others like Analytic Network Process (ANP) offer advanced preference and dependency modeling.
• PROMETHEE methods enhance preference modeling with generalized preferential functions, and PROMETHEE II provides unambiguous rankings.

Purpose of the Study:

• To present a novel two-stage weighted PROMETHEE method.
• To combine the strengths of PROMETHEE's preference functions and PROMETHEE II's unambiguous ranking with the hierarchical structure of the Analytic Hierarchy Process (AHP).
• To apply this integrated method for evaluating and ranking economic performance across Czech Republic regions.

Main Methods:

• Development of a two-stage weighted PROMETHEE methodology.
• Integration of generalized preferential functions, unambiguous ranking (PROMETHEE II), and hierarchical analysis (AHP).
• Application to economic data (unemployment rate, economic activity, wages, etc.) for 14 Czech regions from 2012-2019, utilizing weighted time series analysis.

Main Results:

• The study successfully ranks 14 Czech regions based on multiple economic indicators over an eight-year period.
• The two-stage weighted PROMETHEE method provides a robust framework for analyzing regional economic performance.
• Visualization of results is facilitated using Visual PROMETHEE software.

Conclusions:

• The proposed two-stage weighted PROMETHEE method offers an effective approach for complex regional economic evaluation.
• The method enhances decision-making by incorporating advanced preference modeling and hierarchical structures.
• This research provides valuable insights into the economic dynamics of Czech regions using sophisticated MCDA techniques.