Generator Maintenance Scheduling using Exchange Market Algorithm

Area of Science:

• Electrical Engineering
• Operations Research

Background:

• Generator maintenance scheduling is a complex, constrained combinatorial optimization problem.
• Traditional mathematical methods face limitations with complex, non-linear problems.
• Metaheuristic techniques offer efficient approximate solutions for such problems.

Purpose of the Study:

• To apply the Exchange Market Algorithm (EMA) for optimizing generator maintenance schedules.
• To develop a customized EMA for discrete, constrained optimization problems.
• To ensure power system reliability through effective maintenance planning.

Main Methods:

• Formulating the generator maintenance schedule as a combinatorial optimization problem.
• Applying the Exchange Market Algorithm (EMA), a metaheuristic approach.
• Customizing EMA with specific strategies for faster convergence and discrete solutions, including penalty functions for constraint handling.

Main Results:

• The customized EMA effectively addresses the constraints of the generator maintenance scheduling problem.
• The algorithm demonstrates capability in finding optimal or near-optimal maintenance schedules.
• Faster convergence achieved through tailored initial population selection.

Conclusions:

• The Exchange Market Algorithm (EMA) is a viable and efficient tool for generator maintenance scheduling.
• Metaheuristic approaches like EMA provide practical solutions for complex power system optimization.
• The customized EMA enhances feasibility and speed for real-world applications.