7,645 research outputs found
A Memtic genetic algorithm for a redundancy allocation problem
In general redundancy allocation problems the redundancy strategy for each subsystem is predetermined. Tavakkoli- Moghaddam presented a series-parallel redundancy allocation problem with mixing components (RAPMC) in which the redundancy strategy can be chosen for individual subsystems. In this paper, we present a bi-objective redundancy allocation when the redundancy strategies for subsystems are considered as a variable of the problem. As the problem belongs to the NP-hard class problems, we will present a new approach for the non-dominated sorting genetic algorithm (NSGAII) and Memtic algorithm (MA) with each one to solve the multi-objective model
Increasing the reliability and the profit in a redundancy allocation problem
This paper proposes a new mathematical model for multi-objective redundancy allocation problem (RAP) without component mixing in each subsystem when the redundancy strategy can be chosen for individual subsystems. Majority of the mathematical model for the multi-objective redundancy allocation problems (MORAP) assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in practice both active and cold-standby redundancies may be used within a particular system design and the choice of the redundancy strategy becomes an additional decision variable. The proposed model for MORAP simultaneously maximizes the reliability and the net profit of the system. And finally, to clarify the proposed mathematical model a numerical example will be solved. Keywords: Redundancy Allocation Problem, Serial-Parallel System, Redundancy Strategies, MORAP
Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment : A fuzzy multi-criteria genetic algorithm approach
Abstract: Optimizing system reliability in a fuzzy environment is complex due to the presence of imprecise multiple decision criteria such as maximizing system reliability and minimizing system cost. This calls for multi-criteria decision making approaches that incorporate fuzzy set theory concepts and heuristic methods. This paper presents a fuzzy multi-criteria nonlinear model, and proposes a fuzzy multi-criteria genetic algorithm (FMGA) for complex bridge system reliability design in a fuzzy environment. The algorithm uses fuzzy multi-criteria evaluation techniques to handle fuzzy goals, preferences, and constraints. The evaluation approach incorporates fuzzy preferences and expert choices of the decision maker in regards to cost and reliability goals. Fuzzy evaluation gives the algorithm flexibility and adaptability, yielding near-optimal solutions within short computation times. Results from computational experiments based on benchmark problems demonstrate that the FMGA approach is a more reliable and effective approach than best known algorithm, especially in a fuzzy multi-criteria environment
An improved ant system algorithm for maximizing system reliability in the compatible module
This paper presents an improved Ant System (AS) algorithm called AS-2Swap for solving one of the reliability optimization problems. The objective is to selection a compatible module in order to maximize the system reliability and subject to budget constraints. This problem is NP-hard and formulated as a binary integer-programming problem with a nonlinear objective function. The proposed algorithm is based on the original AS algorithm and the improvement, focused on choosing the feasible solutions, neighborhood search with Swap technique for each loop of finding the solution. The implementation was tested by the five groups of data sets from the existing meta-heuristic found in the literature. The computational results show that the proposed algorithm can find the global optimal solution and is more accurate for larger problems
Risk-based reliability allocation at component level in non-repairable systems by using evolutionary algorithm
The approach for setting system reliability in the risk-based reliability allocation
(RBRA) method is driven solely by the amount of ‘total losses’ (sum of reliability
investment and risk of failure) associated with a non-repairable system failure. For a
system consisting of many components, reliability allocation by RBRA
method becomes a very complex combinatorial optimisation problem particularly if
large numbers of alternatives, with different levels of reliability and associated cost,
are considered for each component. Furthermore, the complexity of this problem is
magnified when the relationship between cost and reliability assumed to be nonlinear
and non-monotone. An optimisation algorithm (OA) is therefore developed in
this research to demonstrate the solution for such difficult problems.
The core design of the OA originates from the fundamental concepts of
basic Evolutionary Algorithms which are well known for emulating Natural process
of evolution in solving complex optimisation problems through computer simulations
of the key genetic operations such as 'reproduction', ‘crossover’ and ‘mutation’.
However, the OA has been designed with significantly different model of evolution
(for identifying valuable parent solutions and subsequently turning them into even
better child solutions) compared to the classical genetic model for ensuring rapid and
efficient convergence of the search process towards an optimum solution. The vital
features of this OA model are 'generation of all populations (samples) with unique
chromosomes (solutions)', 'working exclusively with the elite chromosomes in each
iteration' and 'application of prudently designed genetic operators on the elite
chromosomes with extra emphasis on mutation operation'. For each possible
combination of alternatives, both system reliability and cost of failure is computed by
means of Monte-Carlo simulation technique.
For validation purposes, the optimisation algorithm is first applied to
solve an already published reliability optimisation problem with constraint on some
target level of system reliability, which is required to be achieved at a minimum
system cost. After successful validation, the viability of the OA is demonstrated by
showing its application in optimising four different non-repairable sample systems in view of the risk based reliability allocation method. Each system is assumed to have
discrete choice of component data set, showing monotonically increasing cost and
reliability relationship among the alternatives, and a fixed amount associated with
cost of failure. While this optimisation process is the main objective of the research
study, two variations are also introduced in this process for the purpose of
undertaking parametric studies. To study the effects of changes in the reliability
investment on system reliability and total loss, the first variation involves using a
different choice of discrete data set exhibiting a non-monotonically increasing
relationship between cost and reliability among the alternatives. To study the effects
of risk of failure, the second variation in the optimisation process is introduced by
means of a different cost of failure amount, associated with a given non-repairable
system failure.
The optimisation processes show very interesting results between system
reliability and total loss. For instance, it is observed that while maximum reliability
can generally be associated with high total loss and low risk of failure, the minimum
observed value of the total loss is not always associated with minimum system
reliability. Therefore, the results exhibit various levels of system reliability and total
loss with both values showing strong sensitivity towards the selected combination of
component alternatives. The first parametric study shows that second data set (nonmonotone)
creates more opportunities for the optimisation process for producing
better values of the loss function since cheaper components with higher reliabilities
can be selected with higher probabilities. In the second parametric study, it can be
seen that the reduction in the cost of failure amount reduces the size of risk of failure
which also increases the chances of using cheaper components with lower levels of
reliability hence producing lower values of the loss functions.
The research study concludes that the risk-based reliability allocation
method together with the optimisation algorithm can be used as a powerful tool for
highlighting various levels of system reliabilities with associated total losses for any
given system in consideration. This notion can be further extended in selecting
optimal system configuration from various competing topologies. With such
information to hand, reliability engineers can streamline complicated system designs
in view of the required level of system reliability with minimum associated total cost of premature failure. In all cases studied, the run time of the optimisation algorithm
increases linearly with the complexity of the algorithm and due to its unique model
of evolution, it appears to conduct very detailed multi-directional search across the
solution space in fewer generations - a very important attribute for solving the kind
of problem studied in this research. Consequently, it converges rapidly towards
optimum solution unlike the classical genetic algorithm which gradually reaches the
optimum, when successful. The research also identifies key areas for future
development with the scope to expand in various other dimensions due to its
interdisciplinary applications
System reliability optimization : a fuzzy genetic algorithm approach
System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective. In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program (FMOOP). A fuzzy multiobjective genetic algorithm approach (FMGA) is proposed for solving the multi-objective decision problem in order to handle the fuzzy goals and constraints. The approach is able flexible and adaptable, allowing for intermediate solutions, leading to high quality solutions. Thus, the approach incorporates the preferences of the decision maker concerning the cost and reliability goals through the use of fuzzy numbers. The utility of the approach is demonstrated on benchmark problems in the literature. Computational results show that the FMGA approach is promising
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