Research Article Open Access

Solving the Periodic Maintenance Scheduling Problem via Genetic Algorithm to Balance Workforce Levels and Maintenance Cost

Mohamed Ali Abdel-Fattah Mansour1
  • 1 , Afganistan
American Journal of Engineering and Applied Sciences
Volume 4 No. 2, 2011, 223-234

DOI: https://doi.org/10.3844/ajeassp.2011.223.234

Submitted On: 17 December 2010 Published On: 12 July 2011

How to Cite: Mansour, M. A. A. (2011). Solving the Periodic Maintenance Scheduling Problem via Genetic Algorithm to Balance Workforce Levels and Maintenance Cost. American Journal of Engineering and Applied Sciences, 4(2), 223-234. https://doi.org/10.3844/ajeassp.2011.223.234

Abstract

Problem statement: In this article we address the multi-objective Periodic Maintenance Scheduling Problem (PMSP) of scheduling a set of cyclic maintenance operations for a given set of machines through a specified planning period to minimize the total variance of workforce levels measured in man-hours and maintenance costs with equal weights. Approach: The article proposed a mixed integer non-linear math programming model and a linearised model for the PMSP. Also, we proposed a Genetic Algorithm (GA) for solving the problem using a new genome representation considered as a new addition to the maintenance scheduling literature. The algorithms were compared on a set of representative test problems. Results: The developed GA proves its capability and superiority to find good solutions for the PMSP and outperforms solutions found by the commercial optimization package CPLEX. The results indicated that the developed algorithms were able to identify optimal solutions for small size problems up to 5 machines and 6 planning periods.The GAs defined solutions in 22 seconds consuming less than two kilobytes with a reliability of 0.84 while the nonlinear and linear models consumes on average 705 and 37 kilobytes respectively. Conclusion: The developed GA could define solutions of average performance of 0.34 and 0.8 for the linearized algorithm compared with lower bound defined by the nonlinear math programming model. We hope to expand the developed algorithms for integrating maintenance planning and aggregate production planning problems.

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Keywords

  • Periodic maintenance
  • multi-criteria optimization
  • mixed-integer non-linear math programming
  • linearization
  • genetic algorithms
  • maintenance costs
  • complexity parameter
  • CPU seconds
  • genome's fitness