Recent decades have seen the development of many new multi-criteria decision-making methods, mainly through in-depth research on them. The main ones are techniques such as PRO-METHEE, PAPRIKA, VIKOR, ELECTREE, and especially BWM (Best-Worst Method) and AHP (Analytic Hierarchy Process, see e.g. Mazurek [1], Liang et al. [2], Saaty [3,4], Brans et al. [17,18], Hansen et al. [25], Opricovic et al. [26] and Alkihairi et al. [27].All these methods use pairwise comparison matrices, which are used also in such many other fields, see e.g. Koczkodaj et al. [20,21,22,23], Cavallo et al [24]. Since the authors of this paper, have repeatedly participated in research on methods that use them, they have also confronted the problem of efficiently generating random comparison matrices -necessary, for example, to perform Monte Carlo simulations for such methods that require large amounts of random data, see e.g. Caflisch [5]. This problem was particularly evident in the case of matrices, the so-called "large" (from 6x6 to 10x10) and when one wanted to simultaneously obtain a low CR -consistency range (especially below the value of 0.1). Then the generation times for several thousand random matrices with the desired parameters could reach even several days. This was a big obstacle for performing efficient research, as the need to examine large amounts of data with a specific consistency interval may be needed on such fronts as the study of constructive consistent approximations

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.

The process of mainframe machines managing and administration requires not only specialized expert knowledge based on many years of experience but also on appropriate tools provided by a machine performance management system, e.g. the Resource Measurement Facility (RMF). The aim of this paper is to show some preliminary results of Z-RAYS system construction that is built basing on machine learning (ML) techniques. It allows automatic detection of anomalies and generation of early warnings about some errors that can appear in the mainframe to support mainframe management process. Presented results are based on extensive simulations that were done basing on the IBM emulator. We focus on determining the degree of the metrics variability, the degree of the data repeatability in metrics, some approaches in metrics anomaly detection and solutions for event correlation detection in metrics.