The Usage of Web Services for Consistency Improvement in Pairwise Comparison Matrixes

Paired comparisons of criteria and alternatives are widely used in a large number of technical and scientific problems of the present, in which it is necessary to rank a finite set of objects or to evaluate an object. Paired comparisons are understandable and simple for an expert, they are a high-quality and reliable way of rating, however, it is known that the complexity and dimension of the criteria space in many problems leads to a high load on the expert, as a result of which incorrect or erroneous situations may arise when compiling matrixes of paired comparisons leading to a decrease in the coherence of judgments, and, as a consequence, to the adoption of irrational decisions. Algorithmic software to increase the consistency of judgments is in demand among experts and researchers, which, together with a large number of diverse tasks, creates requirements for the development of appropriate software: the ability to access a large number of users and independence from the subject area, which are highly satisfied by the web interface. In this paper, the authors describe an effective method for increasing the consistency of judgments in matrixes of pairwise comparisons. The main objective of the method is to maximize the consistency of judgments with a minimum of changes made to the matrix proposed by the expert as initial estimates. As a quantitative measure of consistency of judgments, a classic indicator is used – the index of consistency. Based on the created algorithm, the authors developed software that is available to researchers in distributed web services to support decision-making ws-dss.com.

1. T. Saaty, P. Pierfrancesco Rethinking Design and Urban Planning for the Cities of the Future // Buildings, vol. 7, 76, pp. 1–22, 2017.
2. K.F. Yuen The fuzzy cognitive pairwise comparisons for ranking and grade clustering to build a recommender system: An application of smartphone recommendation // Engineering Applications of Artifi cial Intelligence, Vol. 61, 2017, pp. 136–151.
3. M. Groves, J. Branke Top-κ selection with pairwise comparisons // European Journal of Operational Research, Vol. 274, 2, 2019, pp. 615–626.
4. Huin S.F. Managing deployment of ERP systems in SMEs using multiagents // International Journal of Project Management. Vol.22. N.6. pp.511–517. 2004.
5. Batkovskiy A.M., Kurennykh A.E., Semenova E.G., Sudakov, V. A., Fomina, A. V., Balashov, V.M. Sustainable project management for multi-agent development of enterprise information systems // Entrepreneurship and Sustainability Issues 7(1). pp.278–290. 2019.
6. M. Condorcet, “Essai sur l’application de l’analyse à la probabilité des décisionsrendues á la pluralité des voix”, Paris, 1785.
7. J.C. de Borda, “Mémoire sur les électiones au scrutin”, Histoire de l’Académie Royale des Sciences, Paris, 1781.
8. T.L.Saaty,The Analytic Hierarchy Process: Planning, Priority Setting, Resources Allocation. Mcgraw-Hill, New York, 1980.
9. T. Kakiashvili, M. Woodbury-Smith Improving the medical scale predictability by the pairwise comparisons method: evidence from a clinical data study. Computer Methods and Programs in Biomedicine, 105, 2012, pp. 210–216.
10. K. Kułakowski, J. Szybowski, R. Tadeusiewicz, Tender with Success – The Pairwise Comparisons Approach, Procedia Computer Science, Vol. 35, 2014, pp. 1122–1131.
11. A. Kalinowska, T. Trzaskalik Bonus Distribution for Employees of a Telephone Customer Service Department: A Case Study based on Pairwise Comparisons, Procedia Computer Science, Vol. 35, 2014, pp. 1145–1154.
12. T. Runarsson, X. Yao, “Stochastic ranking for constrained evolutionary optimization”, Evolutionary Computation, IEEE Transactions, on. 4, 2000, pp. 284–294.
13. T. Runarsson, X. Yao, “Search Biases in Constrained Evolutionary Optimization”, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 35, 2005, pp. 233–243.