redundancy allocation

This paper presents an innovative model for the simultaneous optimization of redundancy allocation and condition-based maintenance in series-parallel load-sharing systems. The primary objective of the model is to determine the optimal level of redundancy so that costs are minimized while system reliability constraints are met.

In this research, stochastic dependencies between system components are considered using the proportional hazards model and tempered failure rates to assess reliability accurately. Additionally, transition probability matrices are used to determine the optimal maintenance limits for each subsystem, and periodic inspections are performed. The proposed model is solved using MATLAB, and its performance is evaluated under four different scenarios: 1) a baseline model without redundancy or stochastic dependencies, 2) redundancy allocation without stochastic dependencies, 3) stochastic dependencies without redundancy, and 4) the proposed model.

The results show that the proposed model achieves an optimal balance between cost and reliability, reducing both failure and maintenance costs. Compared to the various scenarios, the proposed model demonstrates superior performance in optimizing costs and enhancing reliability. The findings also emphasize the importance of simultaneously considering stochastic dependencies and redundancy allocation to improve system performance.

Originality/Value:

 This research introduces a novel approach by simultaneously considering stochastic dependencies and redundancy allocation in series-parallel load-sharing systems. The proposed model significantly improves system performance and reduces failure and maintenance costs. It underscores the importance of integrating these two factors in optimizing complex engineering systems.

Allocating redundancy components is one of the most efficient and well-known ways to increase the reliability of factories; which plays an important role in responding appropriately to customer demand, timely delivery of products and cost reduction. This leads to the creation of a stable and reliable supply chain. In the present study, the subject of simultaneous optimization of facility location-inventory-redundancy allocation has been investigated. In this regard, a single-period, three-level supply chain including supplier, distributor and retailer is considered. It is assumed that demand for each retailer is stochastic and normally distributed. Also, in order to deal with the fluctuations of demand, the risk pooling strategy has been applied, as a result of which, inventory will be held only in distribution centers. For this purpose, a nonlinear integer programming model is proposed to optimize the total cost of the supply chain as well as its reliability.Due to the complexity and NP-hardness of facility location-inventory and redundancy allocation problems, a multi-objective metaheuristic algorithm based on the simulated annealing algorithm, called AMOSA, was developed to solve the foregoing problem. Finally, to validate and accredit the algorithm, its results are compared with the results of the complete enumeration of all feasible solutions.