An integrated GA-DEA algorithm for determining the most effective maintenance policy for a k -out-of- n problem

This paper presents a novel hybrid GA-DEA algorithm in order to solve multi-objective \(k\)-out-of-\(n\) problem and determine preferred policy. The proposed algorithm maximizes overall system reliability and availability, while minimizing system cost and queue length, simultaneously. To meet these objectives, an adaptive hybrid GA-DEA algorithm is developed to identify the optimal solutions and improve computation efficiency. In order to improve computation efficiency genetic algorithm (GA) is used to simulate a series production line and find the Pareto-optimal solutions which are different values of \(k\) and \(n\) of \(k\)-out-of-\(n\) problem. Data envelopment analysis is used to find the best \(k\) and \(n\) from Genetic Algorithm’s Pareto solutions. An illustrative example is applied to show the flexibility and effectiveness of the proposed algorithm. The proposed algorithm of this study would help managers to identify the preferred policy considering and investigating various parameters and scenarios in logical time. Also considering different objectives result in Pareto-optimal solutions that would help decision makers to select the preferred solution based on their situation and preference.