Application of the Jaya algorithm to solve the optimal reliability allocation for reduction oxygen supply system of a spacecraft

In this paper the reliability of reduction oxygen supply system (ROSS) of a spacecraft which was calculated as a complex system using minimal cut method. The reliability of each component of system was calculated as well as the reliability importance of the system. The cost of each component of the system was possible approaches of the allocation values of reliability based the minimization of the overall cost in this system. The advantage of this algorithm can be used to allocate the optimization of reliability for simple or complex system. This optimization is achieved using the Jaya algorithm. The proposed technique is based on the notion that a conclusion reached on a particular problem should pass near the best results and avoid the worst outcomes. The original findings of this paper are: i) the system used in this paper is a spacecraft’s reduced oxygen supply system with the logarithmic cost function; and ii) the results obtained were by using the Jaya algorithm to solve specific system reliability optimization problems.

System reliability-redundancy allocation by the multiobjective plant propagation algorithm

PurposeIncreasing the system reliability is one of the most important concerns in an industrial plant to become competitive. However, focusing on the overall system reliability increases the overall design cost. The problem is investigated as a multiobjective optimization problem.Design/methodology/approachIt implements the Multiobjective Plant Propagation Algorithm (PPA), also known as the Strawberry Algorithm for the system reliability-redundancy allocation problem.FindingsThe Pareto set of a pharmaceutical plant involving ten subsystems connected in series is generated in order to highlight the applicability of the algorithm.Research limitations/implicationsLimitations include the study of two objective functions.Practical implicationsIt allows the decision-maker to select the best solution according to his target.Originality/valueThis work represents the first implementation of the multiobjective PPA for solving the multiobjective system reliability optimization in the literature.

Redundancy allocation of heterogeneous components by simulation-based optimization

The present study proposes a novel simulation-based optimization model for a series-parallel redundancy allocation problem (RAP) under heterogeneous components for reliability maximization subjected to system-level constraints through the identification of the optimal redundancy strategy, component type, and subsystem component count. To obtain higher practicality, active, cold-standby, mixed, and K-mixed redundancy strategies are incorporated as the decision variables. In general, as it is difficult to determine a closed-form (excluding active redundancy strategies), it is not possible to analytically assess system reliability. Earlier studies on system reliability optimization applied convenient lower bound approximation. This limits higher reliability levels. In order to tackle this problem, this study adopted simulation sampling to make unbiased efficient reliability estimates. To this end, 4Dscript interpreting programming was utilized to develop a simulation model. As RAP has a combinatorial nature and is a random and NP-hard problem, this study adopted the genetic algorithm (GA) optimization. To validate the model and assess GA efficiency, the numerical findings of some benchmark tests were employed. The proposed approach outperformed earlier approaches in reliability and confidence.

System Reliability Optimization of Multi-Source Multi-Sink Stochastic Flow Networks With Budget Constraint

In this paper, we investigate system reliability optimization of multi-source multi-sink flow networks subject to transmission budget constraints. More specifically, we present a mathematical model of the optimization problem and a genetic algorithm (GA) to solve it. The GA is based on determining the optimal set of lower boundary points that maximize system reliability such that transmission cost does not exceed a specified upper bound. Finally, to ensure the efficiency of our approach, we apply our proposed algorithm to various network examples.

Neutrosophic fuzzy goal programming approach in selective maintenance allocation of system reliability

Selective maintenance problem plays an essential role in reliability optimization decision-making problems. Systems are a configuration of several components, and there are situations the system needs small intervals or break for maintenance actions, during the intervals expert carried out the maintenance actions to replace or repair the deteriorated components of the systems. Because of the uncertainty associated with the component’s operational time, failure, and next mission duration create a new challenge in determining optimal components allocation and evaluating future missions successfully. In this paper, a multi-objective selective maintenance allocation problem is formulated with fuzzy parameters under neutrosophic environment. A new defuzzification technique is introduced based on beta distribution to convert fuzzy parameters into crisp values. The neutrosophic goal programming technique is used to determine the compromise allocation of replaceable and repairable components based on the system reliability optimization. A numerical illustration is used to validate the model and ascertain its effectiveness. The result is compared with two other approaches and found to be better. The method is flexible and straightforward and can be solved using any available commercial packages. The extension of the concept can be useful to other complex system reliability optimization.