Journal of Engineering, Project, and Production Management, 2026, 16(4), 2025-157
Maintenance Scheduling Optimization Using MILP and Asset LCA
1
Intermediate Accountant, Hospital Affairs Office, The First Affiliated
Hospital of Air Force Medical University (Xijing Hospital), Xi’an,
710032, China, E-mail: zqq199709@126.com
2 Intermediate Accountant, Hospital
Affairs Office, The First Affiliated Hospital of Air Force Medical
University (Xijing Hospital), Xi’an, 710032, China, E-mail:
zxf20250226@163.com (corresponding author).
3 Assistant Economist, Fire
Protection Division, Xi'an CNNC Nuclear Instrument Co., Ltd, Xi'an,
710000, China, E-mail: 18092187581@163.com
Project Management
Received August 11, 2025; revised August 12, 2025; October 19, 2025; accepted October 20, 2025
Available online May 29, 2026
Abstract: In asset lifecycle management, the increasing scale of power equipment and the complexity of the operating environment challenge traditional maintenance and scheduling methods to meet the requirements of high efficiency, economy, and reliability throughout the entire lifecycle of power equipment. This study, therefore, aims to minimize the total lifecycle maintenance cost while maximizing equipment reliability. The proposed approach combines a mixed-integer linear programming model and the Improved Aquila Optimizer (IOA) algorithm, incorporating asset lifecycle analysis for maintenance scheduling optimization. The Improved Aquila Optimizer (IOA) algorithm improves performance through quasi-inverse solution initialization, adaptive weighting, bubble predation, and perturbation strategies. Results demonstrate that the IOA achieves better convergence speed and precision than comparative algorithms, including the Generic Algorithm (GA) and Practical Swarm Optimization (PSO), in single-peak and multi-peak test function optimization. When applied to the case studies, the improved algorithms were 11.983 million yuan (Example 2) and 21.504 million yuan (Example 3), demonstrating short convergence time and high stability. The proposed scheme reduced total cost by 21.2% to 27.2%, fault losses costs by 45.7%, and increased average equipment reliability by 12.6% to 14.6%, and the load balancing rate by 13.8% to 16.8%, compared to the traditional scheme. These improvements effectively reduce the number of faults and power outage duration. This research provides a scientific and efficient decision-making framework for lifecycle maintenance and scheduling of power equipment assets, which can enhance the operational quality and economic benefits of power system asset lifecycle management.
Keywords: MILP, asset lifecycle, asset lifecycle management, maintenance scheduling, IAO, WOA.
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Citation: Zhang, Q., Zhou, X., and Cao, Y. (2026). Maintenance Scheduling Optimization Using MILP and Asset LCA. Journal of Engineering, Project, and Production Management, 16(4), 2025-157.
