A Genetic Algorithm Model Improved with Zeckendorf Representations for Preventive Maintenance Scheduling Problem
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Authors: Y. E. GöKTEPE, F. KöKEN AND H. ERGUN
DOI: 10.46793/KgJMat2610.1569G
Abstract:
This study introduces a novel Genetic Algorithm (GA) model enhanced with Zeckendorf representation and Fibonacci number-based encoding to optimize preventive maintenance scheduling (PMS) problems. Conventional maintenance scheduling methods, based on random or linear encoding techniques, often fail to optimize maintenance processes effectively.
Therefore, the proposed model aims to systematically plan maintenance periods and minimize production interruptions by encoding maintenance intervals using Zeckendorf representation.
By optimizing maintenance processes, the proposed model enhances system production continuity. Experimental analyses indicate that the proposed model enhances existing production capacity and facilitate a more balanced management of maintenance operations. The electricity and water production capacities increased by 11% and 10%, respectively, while the reserve capacity improved by 9% for electricity and 17% for water.
These results show that the proposed method is a new optimization strategy for maintenance planning by enhancing the applicability of GA in preventive maintenance scheduling problems. Optimizing maintenance scheduling with Zeckendorf representation enables systematic and balanced execution of maintenance operations, thereby ensuring more efficient planning in industrial facility maintenance processes.
Keywords:
Fibonacci numbers, Zeckendorf representations, genetic algorithm, preventive maintenance.
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