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Binary Pareto Multi-Objective Whale Optimization Algorithm for Power Plant Maintenance Scheduling | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Volume 24, Issue 2, June 2024, Pages 65-78 PDF (759.39 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33103/uot.ijccce.24.2.6 | ||
| Authors | ||
| Aseel J. Haleel* ; Lamyaa M. Dawood | ||
| Production Engineering and Metallurgy, University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| Maintenance scheduling is a critical challenge in all major industrial sectors, such as aeronautics, automotive manufacturing, and power generation plants. The goal of power plant maintenance scheduling is to establish a schedule plan for carrying out preventive maintenance shutdowns for each unit within a defined planning horizon. In this paper, a mathematical model for optimizing the maintenance schedule was established to maximize the supply, minimize fuel costs, and CO2 emissions from generating units. A developed Whale Optimization Algorithm called Binary Pareto Multi-Objective Whale Optimization Algorithm BPMOWOA is proposed and implemented to find optimal maintenance scheduling for a power plant. The proposed algorithm uses two different approaches. The first approach includes binary encoding, in which each generating unit and each time interval are represented in binary form. Then, in the second approach, a fixed-sized repository is integrated into the WOA for saving and retrieving the Pareto optimal solutions, and a grid mechanism is integrated into the WOA to maintain diversity in the population of non-dominated solutions. A case study was conducted on fourteen generating units adapted from a real-world power plant to validate the algorithm's efficiency. The results illustrate that the proposed algorithm was effective in optimizing the maintenance schedule in terms of coverage and non-dominated solution and improved the power plant performance by increasing electricity generation by 11.48% and decreasing fuel expenses by 6.56%, which are the main goals of the considered power plant. | ||
| Keywords | ||
| maintenance scheduling; multi-objective; whale optimization algorithm; power plant | ||
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