Title: Fuzzy inference system for a bilevel quality assessment optimisation model

Authors: Georgii Pipiay; Liudmila Chernenkaya; Vladimir Mager

Addresses: Higher School of Cyberphysical Systems and Control, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia ' Higher School of Cyberphysical Systems and Control, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia ' Higher School of Cyberphysical Systems and Control, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

Abstract: In the context of the industry digital development, producers are required to improve all elements of the product life cycle, and in particular, the monitoring and assessment of product quality, since the degree of all stakeholders' satisfaction depends on this. In order to select right models for product quality monitoring and assessment, it is necessary to identify sources of the measured or evaluated information. This problem requires the development of flexible systems for processing and analysing primary information that can take into account heterogeneous information in the production process. In this paper, a fuzzy inference system is proposed for solving the task of bilevel product quality optimisation at the production stage, and fuzzy partial indicators of product quality are obtained, including the possibility of using these product quality indicators to solve the task of bilevel product quality optimisation. In the presented work, methods and approaches will be proposed for solving the problem of assessing product quality.

Keywords: quality assessment methodology; bilevel optimisation; partial criteria; objective functions; fuzzy inference system.

DOI: 10.1504/IJPQM.2023.134266

International Journal of Productivity and Quality Management, 2023 Vol.40 No.2, pp.171 - 196

Received: 10 Aug 2021
Accepted: 05 Nov 2021
Published online: 17 Oct 2023 *

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