Completed projects

  • ADF workforce recruitment and promotion optimisation (@ATHENA Lite Optimiser)
  • Development of Dynamically Reconfigurable Agent-Based Discrete Event Simulator for Aircrew Training (@Aircrew ATHENA)
  • Development of Dynamically Reconfigurable Agent-Based Discrete Event Simulator for Navy Submarine Warfare Officer Career Pipelines (@MWO-SM ATHENA)
  • Timetabling for Australian Navy Training Authority Aviation (TA-Avn)
  • Tender Evaluation using Discrete Event Simulation
  • System Dynamics to support strategic decision making

Development of Dynamically Reconfigurable Agent-Based Discrete Event Simulator for Aircrew Training (@Airecrew ATHENA)

The Australian Defence continually seeks to improve efficiencies and robustness in the aviation training continuum.  For example, services may be consolidated into a single location, schools merged or platforms phased in or out of operation. These changes affect both infrastructure and associated governing policies. Further exacerbated by volatile school failure rates, these changes and uncertainties can severely impact the predictability of the graduates to the operational squadrons.

To model such transient system behaviour, while preserving common simulation architecture requires an easily reconfigurable simulation framework capable of explicitly representing the changing infrastructure, domain policies, constraints and flows.  Flexibility is required to re-build models and provide a repository of historical changes and finally cost benefits.  

The Workforce Analysis team has designed a new approach that combines human intelligence and technology to extract more from human talent, employing state of the art Operations Research techniques that provide guaranteed best solutions - not just any solution that works. That is, stable and robust solutions (avoiding shortage or surplus of personnel numbers), providing significant cost savings. Furthermore, the team has employed high-end cognitive visualisation techniques, thus empowering decision makers to make the right decisions at the right time. 

  1. V. Nguyen, A. Novak, M. Shokr, K. Pash (2017) “Aircrew manpower supply modeling under change: an agent-based discrete event simulation approach”, Proceedings of WinterSim 2017, Las Vegas, US 3-6 December 2017
  2. V. Nguyen, Mina Shokr, A. Novak, T. Caelli, A Dynamically Reconfigurable Agent Based Discrete Event Simulator for Helicopter Aircrew Training, Proceedings of the 2016 ISMOR Conference

Development of Dynamically Reconfigurable Agent-Based Discrete Event Simulator for Navy Submarine Warfare Officer Career Pipelines (@MWO-SM ATHENA)

While Aircrew ATHENA demonstrated the benefits of the hybrid agent-based and discrete event simulation approach for modelling Defence training pipelines, this project extends the work and applies the similar modelling paradigm to simulate Defence career progression through complex posting and on-the-job training pipelines. A prototype model was built for the Navy Maritime Warfare Officer Submarine workforce.

The model was able to detect and visualise critical system health issues, such as high-risk positions, bottleneck courses, and delays and queues in the personnel movements.

Timetabling for Australian Navy Training Authority Aviation (TA-AVN)

The Training Authority Aviation (TA-Avn), an organisation within the Royal Australian Navy (RAN), is responsible for managing aviation-specific training for all RAN personnel, from their recruitment until posting to an operational squadron. Aircrew training consists of an ordered sequence of major courses complemented by mandatory short courses that are limited in prerequisite structure. Most courses are run by other military or external organisations, leaving TA-Avn with little influence over course dates and capacities. Furthermore, high fluctuations in failure rates associated with aviation training expose Defence to potentially large costs.

Scheduling and planning of an individual’s specific training continuum is currently a manual and time consuming process, reliant on corporate memory. The Workforce Analysis team has developed an automated timetabling tool that addresses the above challenges. It generates an optimal solution with respect to training length, compared to the current approach which inherently focuses on finding a single feasible solution.

Recent development of a methodology using Knuth’s Dancing Links ideas to efficiently implement backtracking along with Integer Linear Programming provides an exciting, incipient, alternative to the more conventional approach of the timetabling tool with potential wider applicability.  Currently, this approach requires solution of very high dimensional integer linear programming problems. Application specific methods are being developed to handle the computational feasibility of such large systems. 

  1. Mak-Hau, V., Hill, B., Kirszenblat, D., Moran, B, Nguyen, V. and Novak A. (2021), “A simultaneous sequencing and allocation problem for military pilot training: Integer programming approaches”, Computers and Industrial Engineering, vol. 154, pp. 1-11, Amsterdam, The Netherlands.
  2. Nguyen, V., Moran, B., Novak, A., Mak-Hau, V., Caelli, T., Hill, B., Kirszenblat, D. and Shokr, M. (2018) “Dancing Links for optimal timetabling”, Journal of Military Operations Research, 23(2), pp 61-77.
  3. K. Talbot, D. Kirszenblat, B. Moran, V. Nguyen, A. Novak, Sampled timetabling solution for the Australian Defence Force aircrew training, MODSIM 2017, 23srd International Congress on Modelling and Simulation, December 2017
  4. P. Lalbakhsh, T. Caelli, A. Novak, An optimal recruitment algorithm based on an efficient tree search policy, MODSIM 2017, 23srd International Congress on Modelling and Simulation, December 2017
  5. D. Kirszenblat, V. Nguyen, A. Novak, B. Moran, Using column generation to solve Aircrew Training timetabling problem,  MODSIM 2017, 23srd International Congress on Modelling and Simulation, December 2017
  6. T. Caelli, A. Novak, V. Nguyen, K. Pash, M.Shokr, Tabu Search constraint satisfaction dynamics using Markov models, SimTecT 2017
  7. C.Bayliss, A. Novak, V. Nguyen, B. Moran, T. Caelli, L. Tracey, S. Harrison, OptimizOptimising AirCrew Training Schedules using Tabu, Proceedings of the 2016 SimTechT

Tender Evaluation using Discrete Event Simulation

A novel discrete event simulation (DES) methodology in which performance is measured against “best performance” criteria, has been developed for evaluation of aviation training tenders, specifically for planned training consolidation and helicopter rationalisation of the Australian Defence Aviation Training School.  With an objective to assess and compare multiple aviation training schedules and their resource allocation plans against predetermined training objectives, DES emerged as an ideal platform allowing evaluation of resource plans and schedules, metric selection to objectively encapsulate performance, and unbiased comparison of tenders.  The software tool permits feasibility studies for each tender proposal to assure system and policy constraint satisfaction and strategic examination of what-if scenarios to determine improvements to the proposed solutions.

  1. Novak, L. Tracey, V. Nguyen, T. Caelli, “Evaluation of tendered solutions for scheduling problems with specific reference to a helicopter aviation training system”, Journal of Applied Operational Research (2017), Vol. 9, No.1, 54-66
  2. Novak, L. Tracey, V. Nguyen, M. Johnstone, V. Le, D. Creighton, Evaluation of Tender Solutions for Aviation Training using Discrete Event Simulation and best Performance Criteria, Proceedings of the 2015 Winter Simulation Conference, December 2015

System Dynamics for Strategic Decision Making

A System dynamics (SD) model has been developed to support strategic decision making for an aviation training continuum under major infrastructure and policy transformation such as consolidation of training schools and training platforms across multiple services. A novel SD-based Helicopter Training Continuum simulation architecture facilitated  discovery of relationships between student and instructor development and flow dynamics. The  architecture employed hybrid push-pull flow control to quantify transience and estimate recovery time after a policy change or disturbance, and to permit analysis of complex interactions between pooling, latency and the amplification of effects across the system. The ability to identify resultant causal relationships allowed stakeholders to develop a deeper understanding of the underlying systemic problems, such as delayed transitions between schools and instructor shortages.

  1. M. Johnstone, V. Le, D. Creighton, B. Khan, A. Novak, V. Nguyen, L. Tracey, Modelling a helicopter training continuum to support system transformation, Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), November 2015
  2. M. Johnstone, V. Le, A. Novak, D. Creighton, B. Khan, L. Tracey, V. Nguyen, A Multi-Level Approach to Planning and Scheduling Resources for Aviation Training, MODSIM 2015, 21st International Congress on Modelling and Simulation, December 2015