Abstract
Redundancy-reliability allocation problems in multi-stage series-parallel systems under uncertain environments are addressed in this study. First, a multi-objective programming model is formulated for simultaneously maximizing system reliability and minimizing system total cost. Due to the nature of uncertainty in the problem, the fuzzy set theory and technique are used to convert the deterministic multi-objective programming model into a fuzzy nonlinear programming problem. A heuristic method is developed to get a set of satisfactory solutions for the fuzzy nonlinear programming problem. Then, a modified data envelopment analysis (DEA) model, is applied for completely ranking those satisfactory solutions considering some criteria of satisfactory, reliability, cost, volume and weight. A case study that is related to the electronic control unit installed on aircraft engine over-speed protection system is used to implement the developed approach. Results suggest that the developed fuzzy programming and DEA approach can effectively resolve the fuzzy and uncertain problem when design goals and constraints are not clearly confirmed at the initial conceptual design phase. Key words: Fuzzy programming, data envelopment analysis, reliability allocation.
Highlights
For a system design with low reliability requirement, the designer can adopt series-parallel-systems techniques to improve system reliability and redundancy allocation
Redundancy-reliability allocation problems in multi-stage series-parallel systems under uncertain environments are addressed in this study
A multi-objective programming model is formulated for simultaneously maximizing system reliability and minimizing system total cost
Summary
Redundancy-reliability allocation problems in multi-stage series-parallel systems under uncertain environments are addressed in this study. A multi-objective programming model is formulated for simultaneously maximizing system reliability and minimizing system total cost. Due to the nature of uncertainty in the problem, the fuzzy set theory and technique are used to convert the deterministic multi-objective programming model into a fuzzy nonlinear programming problem. A heuristic method is developed to get a set of satisfactory solutions for the fuzzy nonlinear programming problem. A modified data envelopment analysis (DEA) model, is applied for completely ranking those satisfactory solutions considering some criteria of satisfactory, reliability, cost, volume and weight. Results suggest that the developed fuzzy programming and DEA approach can effectively resolve the fuzzy and uncertain problem when design goals and constraints are not clearly confirmed at the initial conceptual design phase
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