The selection problem of repairable components for a system is a kind of reliability optimization problem and is often treated as a single objective problem with the goal of maximizing the system reliability (or minimizing either time or cost spent on repairing the component). In the present paper, we formulated the selection problem of repairable components for a parallelseries system as a multi-objective optimization problem and have discussed two different models. In the first model, the reliability of subsystems are considered as different objectives. In second model the cost and time spent on repairing the components are considered as two different objectives. Selective maintenance operation is used to select the repairable components and a multi-objective goal programming algorithm is proposed to obtain compromise selection of repairable components for the two models under some given constraints. A numerical example is given to illustrate the procedure.
In reliability optimization problems diverse situation occurs due to which it is not always possible to get relevant precision in system reliability. The imprecision in data can often be represented by triangular fuzzy numbers. In this manuscript, we have considered different fuzzy environment for reliability optimization problem of redundancy. We formulate a redundancy allocation problem for a hypothetical series-parallel system in which the parameters of the system are fuzzy. Two different cases are then formulated as non-linear programming problem and the fuzzy nature is defuzzified into crisp problems using three different defuzzification methods viz. ranking function, graded mean integration value and a-cut. The result of the methods is compared at the end of the manuscript using a numerical example.
In this paper, we obtain optimum allocation of replaceable and repairable components in a system design. When repair and replace time are considered as random in the constraints. We convert probabilistic constraint into an equivalent deterministic constraint by using chance constrained programming. We have used the selective maintenance policy to determine how many components to be replaced & repaired within the limited maintenance time interval and cost. A Numerical example is presented to illustrate the computational procedure and problem is solved by using LINGO Software.
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