Bi-objective redundancy allocation problem for a system with mixed repairable and non-repairable components.

Area of Science:

• Operations Research
• Systems Engineering
• Reliability Engineering

Background:

• Traditional redundancy allocation problems (RAP) focus on either reliability or availability optimization, assuming components are uniformly repairable or non-repairable.
• Availability optimization is less explored than reliability optimization in RAP.
• Real-world systems often comprise a mix of both repairable and non-repairable components, posing a challenge for existing models.

Purpose of the Study:

• To develop a new Mixed Integer Nonlinear Programming (MINLP) model for availability optimization.
• To address systems composed of both repairable and non-repairable components simultaneously.
• To propose an efficient Genetic Algorithm (GA) for solving the developed MINLP model.

Main Methods:

• Formulation of a novel Mixed Integer Nonlinear Programming (MINLP) model for system availability optimization.
• Development of an efficient Genetic Algorithm (GA) to solve the MINLP model.
• Validation through a numerical example and comparison with existing algorithms.

Main Results:

• The proposed MINLP model effectively handles systems with mixed component types (repairable and non-repairable).
• The developed Genetic Algorithm (GA) provides an efficient solution for the complex optimization problem.
• Experimental results show the proposed GA outperforms a leading existing algorithm in the literature.

Conclusions:

• The study successfully presents a new framework for availability optimization in complex systems.
• The developed GA offers a more efficient approach for solving mixed-component redundancy allocation problems.
• This research contributes to improved system design and maintenance strategies by optimizing availability.