A new publication by the staff of the Department of Complex Systems (Paweł Kuraś, Bartosz Kowal, Dominik Strzałka) has been released. The article titled "REDUCE – A Python Module for Reducing Inconsistency in Pairwise Comparison Matrices" has been published in the "Advances in Science and Technology Research Journal (ASTRJ)" (Volume 17, Issue 4, 2023). (IF=1.1, 100 points on the MEiN list).
The article is available at REDUCE – A Python Module for Reducing Inconsistency in Pairwise Comparison Matrices
The paper introduces REDUCE, a Python module designed to minimize inconsistency in Multi-Criteria Decision Making (MCDM). This module can be applied in various fields, including engineering science and numerical simulation methods. REDUCE implements several algorithms that identify and correct inaccurate data in pairwise comparison matrices (PCM), thereby reducing the inconsistency ratio.
The REDUCE library, written in Python and utilizing auxiliary libraries such as NumPy, SciPy, and SymPy, offers 21 functions categorized into data input helpers, consistency ratio (CR) reduction algorithms, PCM indexes, and support functions. Performance testing indicates that the library can efficiently handle matrices of varying sizes, particularly those ranging from 3x3 to 10x10.
The module is available at: REDUCE on GitHub
