Weekly maintenance scheduling using exact and genetic methods

TL;DR

This paper compares exact and genetic methods for automating weekly maintenance scheduling, demonstrating the efficiency and effectiveness of genetic algorithms over MILP in operational mine environments.

Contribution

It introduces a genetic algorithm approach for maintenance scheduling, outperforming MILP and providing a fast, adaptable solution for operational constraints.

Findings

Genetic algorithms outperform MILP in scheduling efficiency.

Linear fitness function yields better results than inverse.

Genetic approach enables rapid schedule recalculations.

Abstract

The weekly maintenance schedule specifies when maintenance activities should be performed on the equipment, taking into account the availability of workers and maintenance bays, and other operational constraints. The current approach to generating this schedule is labour intensive and requires coordination between the maintenance schedulers and operations staff to minimise its impact on the operation of the mine. This paper presents methods for automatically generating this schedule from the list of maintenance tasks to be performed, the availability roster of the maintenance staff, and time windows in which each piece of equipment is available for maintenance. Both Mixed-Integer Linear Programming (MILP) and genetic algorithms are evaluated, with the genetic algorithm shown to significantly outperform the MILP. Two fitness functions for the genetic algorithm are also examined, with a linear fitness function outperforming an inverse fitness function by up to 5% for the same calculation time. The genetic algorithm approach is computationally fast, allowing the schedule to be rapidly recalculated in response to unexpected delays and breakdowns.