Pairwise comparisons constitute a popular and important tool for multiple-criteria decision making. Consistency is one particular aspect of pairwise comparisons that has been studied thoroughly in recent decades since human judgements are seldom fully consistent. The aim of this paper is to propose a new simple and intuitive iterative Step-by-Step (SBS) algorithm for inconsistency reduction of a (multiplicative) pairwise comparison matrix based on step-by-step changes of the most inconsistent matrix element, where the inconsistency is measured by a modified Koczkodaj’s inconsistency index. A comparison of the proposed SBS algorithm with other algorithms for inconsistency reduction is provided as well.

Present-day technologies used in SHM (Structural Health Monitoring) systems in many implementations are based on wireless sensor networks (WSN). In the context of the continuous development of these systems, the costs of the elements that form the monitoring system are decreasing. In this situation, the challenge is to select the optimal number of sensors and the network architecture, depending on the wireless system’s other parameters and requirements. It is a challenging task for WSN to provide scalability to cover a large area, fault tolerance, transmission reliability, and energy efficiency when no events are detected. In this article, fundamental issues concerning wireless communication in structural health monitoring systems (SHM) in the context of non-destructive testing sensors (NDT) were presented. Wireless technology developments in several crucial areas were also presented, and these include engineering facilities such as aviation and wind turbine systems as well as bridges and associated engineering facilities.

The aim of this paper is to compare selected iterative algorithms for inconsistency reduction in pairwise comparisons by Monte Carlo simulations. We perform simulations for pairwise comparison matrices of the order $n = 4 $ and $n = 8 $ with the initial inconsistency $0.10 < CR < 0.80 $ and entries drawn from Saaty’s fundamental scale. Subsequently, we evaluate the algorithms’ performance with respect to four measures that express the degree of original preference preservation. Our results indicate that no algorithm outperforms all other algorithms with respect to every measure of preference preservation. The Xu and Wei’s algorithm is the best with regard to the preservation of an original priority vector and the ranking of objects, the Step-by-Step algorithm best preserves the original preferences expressed in the form of a pairwise comparison matrix, and the algorithm of Szybowski keeps the most matrix entries unchanged during inconsistency reduction.

One of the alternative ways to obtain low-cost energy is to use biogas generated by the digestion process from sewage sludge. This paper presents an analysis of the processes in four anaerobic digesters (AD) - A, B, C and D. The study analyzed the amount of biogas produced in each digester tank...

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...

Rapidly developing of internet technologies and digitalization of government generate more and more data. Databases from various public institutions and private sectors, e.g. in the fields of economics, transport, environment and public safety are publishing in the global Internet network, so that any user can browse them without additional charges. Most of this data is published on the open data portals. Open data - that is, “open”, public data can allow the processing and analysis of information contained in them completely free of charge. This article is an introduction to a fairly new area of issues such as “open data” or “open government”, presents the main mechanisms of accessing to data in public open data portals and also propose a conceptual open data/government model.