Meta-heuristics to optimise complex FIFO (Fly-in-Fly-out) workforce roster modelling in the mining sector

Bermingham, Luke, Lee, Kyungmi, and Myers, Trina (2015) Meta-heuristics to optimise complex FIFO (Fly-in-Fly-out) workforce roster modelling in the mining sector. In: Proceedings of 2015 10th International Conference on Intelligent Systems and Knowledge Engineering, 521 -525. From: IEEE ISKE 2015: 10th International Conference on Intelligent Systems and Knowledge Engineering, 24-27 November 2015, Taipei, Taiwan.

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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.

Item ID: 42323
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4673-9322-5
Keywords: FIFO(Fly-In-Fly-Out); heuristics; rostering; scheduling; algorithm design and analysis; genetic algorithms; optimization; transportation
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Funders: Osmotion, Australian Government (AG)
Projects and Grants: AG Researchers in Business Grant
Date Deposited: 02 Mar 2016 02:35
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 100%
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