A fuzzy multi-objective genetic algorithm for system reliability optimisation. (2016)

• Record Type: Journal Article
• Authors: Mutingi, Michael
• Is Part Of: International journal of industrial and systems engineering. Volume 22:Number 1(2016)
• Page Start: 1
• Page End: 16
• Publication Date: 2016
• Subjects: system reliability -- multi-objective optimisation -- genetic algorithms -- fuzzy optimisation -- fuzzy set theory -- fuzzy evaluation -- nonlinear programming -- fuzzy logic; Systems engineering -- Periodicals; Industrial engineering -- Periodicals; 620.001171
• Languages: English
• ISSNs: 1748-5037

Abstract

The problem of optimising system reliability is often confronted with imprecise goals concerned with reduction of system costs and improvement of system reliability. Due to the presence of imprecise parameters, the impact of the decision is fuzzy and multi-objective. The present paper models the problem as a fuzzy multi-objective nonlinear program. To effectively handle the fuzzy goals and constraints of the multi-objective decision problem, a fuzzy multi-objective genetic algorithm approach (FMGA) is proposed. The proposed approach is flexible; it allows for generation of intermediate solutions, which eventually lead to high quality solutions. By using fuzzy membership functions, FMGA incorporates the decision maker's preferences and choices that influence system costs and reliability goals. Computations based on benchmark problems demonstrate the utility of the approach.