Multi-Objective optimization of selective maintenance process considering profitability and personnel energy consumption

Abstract

Mechanical equipment naturally deteriorates and may malfunction during regular use, resulting in substantial financial losses and downtime. Regular maintenance can effectively address these issues. However, poor maintenance planning for products with numerous components often leads to inefficiencies for maintenance personnel, higher maintenance costs, and unnecessary resource consumption. Selective maintenance helps create effective maintenance programs under resource constraints, scientifically allocate maintenance resources, promptly repair faulty equipment, and sustain production activities. This study develops a multi-objective optimization model to enhance the efficiency of maintenance activities, avoid resource wastage, and increase maintenance revenue. This model optimizes the serial maintenance sequence by considering factors such as maintenance benefits, costs, personnel energy consumption, and resource constraints. Additionally, an improved metaheuristic algorithm, combining brainstorming optimization and large neighborhood search, is proposed to optimize the maintenance scheme for a specific type of carrier booster device system. Finally, an analysis of maintenance practices validates the applicability of the proposed model and algorithm, demonstrating their effectiveness in real-world scenarios.