Intelligent Optimization Systems for Maintenance Scheduling of Power Plant Generators

Firas Basim Ismail*, G. S. Randhawa, Ammar Al-Bazi, Ammar Ahmed Alkahtani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper presents a Genetic Algorithm (GA) and Ant-Colony (AC) optimization model for power plant generators’ maintenance scheduling. Maintenance scheduling of power plant generators is essential for ensuring the reliability and economic operation of a power system. Proper maintenance scheduling prolongs the shelf life of the generators and prevents unexpected failures. To reduce the cost and duration of generator maintenance, these models are built with various constants, fitness functions, and objective functions. The Analytical Hierarchy Process (AHP), a decision-making tool, is implemented to aid the researcher in prioritizing and re-ranking the maintenance activities from the most important to the least. The intelligent optimization models are developed using MATLAB and the developed intelligent algorithms are tested on a case study in a coal power plant located at minjung, Perak, Malaysia. The power plant is owned and operated by Tenaga Nasional Berhad (TNB), the electric utility company in peninsular Malaysia. The results show that GA outperforms ACO since it reduces maintenance costs by 39.78% and maintenance duration by 60%. The study demonstrates that the proposed optimization method is effective in reducing maintenance time and cost while also optimizing power plant operation.
Original languageEnglish
Pages (from-to)1319-1332
Number of pages14
JournalInformation Sciences Letters
Issue number3
Publication statusPublished - 1 Mar 2023


  • Genetic algorithm
  • Ant-colony optimization
  • Optimization modeling
  • Generator
  • Maintenance scheduling


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