Optimization of mining equipment maintenance scheduling based on multiobjective genetic algorithm

Aiming at the optimization problem of piston reciprocating pump maintenance scheduling, this paper proposes an optimization method based on multi-objective genetic algorithm (MOGA), which aims to minimize the total maintenance cost, minimize downtime and maximize system reliability at the same time. By constructing a multi-objective mathematical model and combining the fitness evaluation mechanism of non-dominated sorting and crowding distance, the algorithm can efficiently generate the Pareto optimal solution set. Simulation experiments verify that MOGA is superior to the single-objective genetic algorithm (SGA) and the multi-objective particle swarm optimization algorithm (MOPSO) in terms of coverage and hypervolume indicators. Its uniformly distributed solution set and fast convergence characteristics demonstrate its applicability in complex multi-objective optimization scenarios. Visual analysis further reveals the trade-off relationship between objectives and provides diversified solutions for maintenance scheduling decisions. This study provides theoretical support and practical reference for the intelligent and efficient maintenance management of mining equipment, and has significant engineering application value.