Selective maintenance process optimization based on an improved gravitational search algorithm, from the perspective of energy consumption. Issue 8 (2nd August 2020)

• Record Type: Journal Article
• Authors: Zhang, Lele; Zhang, Libin; Shan, Hongying; Shan, Hongmei
• Is Part Of: Engineering optimization. Volume 52:Issue 8(2020)
• Page Start: 1401
• Page End: 1420
• Publication Date: 2020-08-02
• Subjects: Selective maintenance -- energy consumption -- system reliability -- evolutionary algorithms -- optimization; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2019.1657111
• Languages: English
• ISSNs: 0305-215X

Abstract

Abstract : Maintenance is indispensable for ensuring that multi-component systems successfully complete the next mission. Maintenance activities require an enormous amount of energy consumption in engineering problems. For selective maintenance, many studies focus on the trade-off between system reliability and other resources, such as time and cost. The enormous energy consumption of maintenance processes has been ignored in previous research. In this work, a novel energy consumption model considering the actual maintenance process is proposed. Moreover, system reliability is added to the optimization model as a crucial constraint, which makes the optimization process of energy consumption meaningful. The selective maintenance process is a complex combinatorial optimization problem. Hence, a hybrid differential evolution and gravitational search algorithm (DGSA) is proposed to solve the selective maintenance problem considering energy consumption. In addition, several experiments with DGSA, particle swarm optimization and differential evolution are designed to demonstrate the effectiveness of the proposed algorithm.