Application of Fuzzy Assessing for Reliability Decision Making

TL;DR

This paper introduces a fuzzy assessing method for reliability decision making in repairable systems, providing more accurate system characteristic estimates under uncertainty for better management and design decisions.

Contribution

It develops a novel fuzzy approach to evaluate system reliability, incorporating membership functions and nonlinear programming, enhancing decision-making in repairable and redundant systems.

Findings

Provides a fuzzy assessment framework for repairable systems.

Offers a comparison between fuzzy and conventional reliability methods.

Analyzes parallel and series systems within fuzzy environment.

Abstract

This paper proposes a new fuzzy assessing procedure with application in management decision making. The proposed fuzzy approach build the membership functions for system characteristics of a standby repairable system. This method is used to extract a family of conventional crisp intervals from the fuzzy repairable system for the desired system characteristics. This can be determined with a set of nonlinear parametric programing using the membership functions. When system characteristics are governed by the membership functions, more information is provided for use by management, and because the redundant system is extended to the fuzzy environment, general repairable systems are represented more accurately and the analytic results are more useful for designers and practitioners. Also beside standby, active redundancy systems are used in many cases so this article has many practical instances. Different from other studies, our model provides, a good estimated value based on uncertain environments, a comparison discussion of using fuzzy theory and conventional method and also a comparison between parallel (active redundancy) and series system in fuzzy world when we have standby redundancy. When the membership function intervals cannot be inverted explicitly, system management or designers can specify the system characteristics of interest, perform numerical calculations, examine the corresponding {\alpha}-cuts, and use this information to develop or improve system processes.