Systems Engineering

Competitive Advantage

Our modelling program develops integrated decision analysis methods for asset management and planning. We draw our expertise from a mix of researchers who have varied yet complementary disciplinary backgrounds. Together, we offer covers areas such as: 

  • predictive modelling
  • simulation-optimisation frameworks
  • evidence based decision making
  • embedded machine learning with evolutionary algorithms, and
  • multi-criteria decision analysis. 

Our theories and methods are applied to domains such as classical and construction project management, supply chain design, life cycle analysis, risk analysis, workforce planning, flexible manufacturing design, and smart scheduling.  

Impact

Our in-house Hierarchical Based Modelling (HBM) methodology and the software-embedded decision support schemes serve many modelling requirements to numerous key stakeholders—including the Australian Department of Defence. 

The advancement we’ve made in technology decision making and the breadth of the research portfolio we offer through our Capability Systems Centre entices several external collaborators—both Australian and international. Having such a wide range of collaborators drives the centre to spread its wings across multi-dimensional capability perspectives throughout the entire capability life cycle. 

Successful Applications

The Capability Systems Centre’s in-house implementation tool for our Hierarchical Based Modelling (HBM) methodology is a flexible web-based, database backed, multi-user tool for creating hierarchical models. The HBM uses a layered model approach that includes constraints and system dynamics. It allows for the creation of standalone models that run either in AnyLogic or natively in Java. The computational platform allows our software team to rapidly transform modelling requirements into working models. 

We also offer a bespoke, in-house multi-criteria decision-making tool that allows multiple stakeholders to contribute to shared, accountable decisions. The tool is unique in that it allows users to remotely share large numbers of quantitative and qualitative criteria.

 

  1. Turan HH; Elsawah S; Ryan MJ, 2020, 'A long-term fleet renewal problem under uncertainty: A simulation-based optimization approach', Expert Systems with Applications, vol. 145, pp. 113158 - 113158, http://dx.doi.org/10.1016/j.eswa.2019.113158 

  1. Sallam KM; Chakrabortty RK; Ryan MJ, 2020, 'A two-stage multi-operator differential evolution algorithm for solving Resource Constrained Project Scheduling problems', Future Generation Computer Systems, vol. 108, pp. 432 - 444, http://dx.doi.org/10.1016/j.future.2020.02.074 

  1. Moallemi EA; Elsawah S; Ryan MJ, 2020, 'Robust decision making and Epoch–Era analysis: A comparison of two robustness frameworks for decision-making under uncertainty', Technological Forecasting and Social Change, vol. 151, http://dx.doi.org/10.1016/j.techfore.2019.119797 

  1. Chakrabortty RK; Rahman MHF; Ryan M, 2020, 'Efficient Priority Rules for Project Scheduling under dynamic environments: A Heuristic approach', Computers and Industrial Engineering, vol. 140, pp. 1 - 18, http://dx.doi.org/10.1016/j.cie.2020.106287 

  1. Chakrabortty RK; Abbasi A; Ryan MJ, 2019, 'A Risk Assessment Framework for Scheduling Projects With Resource and Duration Uncertainties', IEEE Transactions on Engineering Management, pp. 1 - 15, http://dx.doi.org/10.1109/TEM.2019.2943161 

  1. Singh HK; Islam MM; Ray T; Ryan M, 2019, 'Nested evolutionary algorithms for computationally expensive bilevel optimization problems: Variants and their systematic analysis', Swarm and Evolutionary Computation, vol. 48, pp. 329 - 344, http://dx.doi.org/10.1016/j.swevo.2019.05.002

  1. Moallemi EA; Elsawah S; Ryan MJ, 2019, 'Strengthening ‘good’ modelling practices in robust decision support: A reporting guideline for combining multiple model-based methods', Mathematics and Computers in Simulation, vol. 175, pp. 3 - 24, http://dx.doi.org/10.1016/j.matcom.2019.05.002 

  1. Moallemi EA; Elsawah S; Ryan MJ, 2018, 'An agent-monitored framework for the output-oriented design of experiments in exploratory modelling', Simulation Modelling Practice and Theory, vol. 89, pp. 48 - 63, http://dx.doi.org/10.1016/j.simpat.2018.09.008 

  1. Moallemi E; Elsawah S; Ryan MJ, 2018, 'Model-based multi-objective decision making under deep uncertainty from a multi-method design lens', Simulation Modelling Practice and Theory, vol. 84, pp. 232 - 250, http://dx.doi.org/10.1016/j.simpat.2018.02.009 

  1. Danesh D; Ryan MJ; Abbasi A, 2017, 'A systematic comparison of multi-criteria decision making methods for the improvement of project portfolio management in complex organisations', International Journal of Management and Decision Making, vol. 16, pp. 280 - 320, http://dx.doi.org/10.1504/IJMDM.2017.085638