Sea-Horse Optimizer-Based Selective Maintenance for Phased Mission Systems Under Different Shift Schedules With Multiple Maintenance Workers

Phased mission systems (PMSs) are systems composed of several different subsystems used to perform a series of consecutive missions. PMS has a high demand for mission success probability and selective maintenance action optimization is the key to ensure the safe and reliable operation of PMS, therefore it is necessary to define a reasonable selective maintenance action for PMS. This paper proposes a PMS mission success probability evaluation method based on imperfect maintenance, and constructs a selective maintenance action optimization model for PMS under the constraints of maintenance cost, maintenance time and maintenance workers. Based on the optimization model, a selective maintenance action solving method based on sea-horse optimizer (SHO) is designed. Compared with the genetic algorithm (GA), the performance of SHO is verified from the aspects of mission success probability, running time, convergence. Experimental results show that the SHO can effectively analyze the optimal mission interval to determine the optimal production organization form.