REDUCE – A Python Module for Reducing Inconsistency in Pairwise Comparison Matrices

• Autorzy: Kuraś Paweł , Strzałka Dominik , Kowal Bartosz , Mazurek Jiří

Identyfikatory

DOI

10.12913/22998624/170187

• Języki publikacji: EN

Abstrakty

EN

This paper introduces REDUCE, a Python module designed to minimize inconsistency in multiplicative pairwise comparisons (PC), a fundamental technique in Multi-Criteria Decision Making (MCDM). Pairwise comparisons are extensively used in various fields, including engineering science and numerical simulation methods, to compare different options based on a set of criteria. However, human errors in perception and judgment often lead to inconsistencies in pairwise comparison matrices (PCM). REDUCE addresses this issue by implementing several algorithms that identify and correct inaccurate data in PCMs, thereby reducing the inconsistency ratio. These algorithms do not require expert intervention, making REDUCE a valuable tool for both scientific research and small to medium enterprises that may not have access to costly commercial software or dedicated decision-making experts. The main functionality of the module is incorporating iterative algorithms for inconsistency reduction. 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, and its use significantly accelerates the process compared to spreadsheets, especially when dealing with large quantities of matrices. The library has already been used in several research papers and application tools, and its availability as a free resource opens up opportunities for small and medium-sized enterprises to leverage multi-criteria decision-making methods. Currently, there are no publicly available libraries for this solution. The authors believe that the proposed module may contribute to do better decision-making process in pairwise comparisons, not only for the circle of scientists but also for small and medium enterprises that usually cannot afford expensive commercial software and do not employ full-time experts in decision-making as they rely on the experience of their employees and free online resources. It should also contributes to the transition to Industry 4.0 and advances research in fields such as fuzzy logic, preference programming, and constructive consistent approximations.

Słowa kluczowe

EN

inconsistency; decision making support; pairwise comparisons; inconsistency index; inconsistency reduction

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2023
• Tom: Vol. 17, no 4
• Strony: 227--234
• Opis fizyczny: Bibliogr. 47 poz., fig., tab.

Twórcy

autor

Kuraś Paweł

• Department of Complex Systems, The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, ul. MC Skłodowskiej 8, 35-036 Rzeszów, Poland

• https://orcid.org/0000-0002-8658-0821

autor

Strzałka Dominik

• Department of Complex Systems, The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, ul. MC Skłodowskiej 8, 35-036 Rzeszów, Poland

• https://orcid.org/0000-0002-8887-4321

autor

Kowal Bartosz

• Department of Complex Systems, The Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, ul. MC Skłodowskiej 8, 35-036 Rzeszów, Poland

• https://orcid.org/0000-0002-7909-6484

autor

Mazurek Jiří

• School of Business Administration in Karvina, Silesian University in Opava, Univerzitní nám. 1934, Fryštát, 733 40 Karviná, Czech Republic

• https://orcid.org/0000-0002-7965-0457

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).