Meta-heuristics to Optimise Complex FIFO (Fly-in-Fly-out) Workforce Roster Modelling in the Mining Sector

Abstract

Staff scheduling and rostering problem has become increasingly important as business becomes more service oriented and cost conscious in a global environment. Fly-In-Fly-Out (FIFO) operation is one of a specialised shiftwork solution which is required for many Australian mining workforce environments. The development of an optimised travel, accommodation and roster model for FIFO has not been easily achieved due to the complexity of rostering a specialised workforce and the difficulty of configuring these resources to achieve both the cost saving and employees satisfaction. This paper describes the implementation of an automatic roster system framework to optimise utilisation of FIFO mining site resources. To build an optimised roster model we explored the use of two different optimisation algorithms: Genetic Algorithm (GA) and Tabu Search (TS). The system implemented provides an artificially intelligent solution to optimisation-modelling of workforce logistics.