Abstract
To improve system performance, redundancy is widely used in different kinds of industrial applications such as power systems, aerospace, electronic, telecommunications and manufacturing systems. Designing high performant systems which meet customer requirements with a minimum cost is a challenging task in these industries. This paper develops an efficient approach for the redundancy optimization problem of series-parallel structures modeled as multi-state systems. To reach the target system availability, redundancies are used for components among a list of products available in the market. Each component is characterized by its own availability, cost and performance. The goal is to minimize the total cost under a system availability constraint. Discrete levels of performance are considered for the system and its components. The extreme values of such performance levels correspond to perfect functioning and complete failure. A piecewise cumulative load curve represents consumer demand. System availability corresponds to the aptitude to fulfill this demand. The multi-state system availability evaluation uses the universal moment generating function technique. The proposed optimization algorithm is based on the non-linear threshold accepting metaheuristic, while using a self-adjusting penalty guided strategy. The obtained results demonstrate the approach efficiency for solving the redundancy optimization problem of multi-state systems. Its effectiveness is also tested using the classical redundancy optimization problem of binary-state systems. The algorithm is evaluated by comparison to the best known methods. For multi-state systems, it is compared to genetic algorithm and tabu search. For binary-state systems, it is compared to genetic algorithm, tabu search, ant colony optimization and harmony search. The obtained results demonstrate that the proposed approach outperforms these state-of-the-art benchmark methods in finding, for all considered instances, a high-quality solution in a minimum computational time.
Highlights
To improve system reliability, redundancy is widely used in different kinds of industrial applications including aerospace, power systems, electronic, telecommunications and manufacturing systems
6.1.3 Comparison Results The algorithm is evaluated by comparison to the existing benchmark methods: genetic algorithms (GA) (Levitin et al, 1998; Levitin et al, 1997; Lisnianski et al, 1996) and tabu search (Ouzineb et al, 2008)
The total cost for the best solutions found in 10 trials and the average cost, as well as the standard deviations
Summary
Redundancy is widely used in different kinds of industrial applications including aerospace, power systems, electronic, telecommunications and manufacturing systems. The redundancy optimization problem (ROP) of a series-parallel system considers many sub-systems in series and for each sub-system, different component versions are connected in parallel. The first objective is to find the number of redundant components and their location to ensure the required performance subject to resource constraints. The second objective is to minimize the cost under performance constraints. The current reliability optimization literature distinguishes between the ROP of multistate systems (MSS), and the ROP of binary-state systems (BSS)
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