Technology Decision Making

Competitive Advantage

  • Our team applies multidisciplinary theories and methods to diverse technology decision making approaches. This includes supply chain design, life cycle analysis and evidence-based decision making.
  • We focus on data-driven project management and scheduling approaches that allow us to better integrate business rules and principles along the line of conventional project management.
  • When we develop advanced decision-making tools, we consider various criteria with proper implementation of advanced techniques. This includes evolutionary algorithms, machine learning tools, and multi-method approaches.
  • We specialise in integrating business logistics and engineering economic concepts while making capability sustainment decisions. 


We provide effective solutions for managing uncertainties and risks in complex project/program environments while making technology decisions.  

This includes:

  • more agile and accurate decision-making tools for various phases of product life cycles
  • better approaches and software-embedded tools to enhance decision making for data-driven businesses, and
  • improved autonomous systems and decision making tools for adapting both uncertain and dynamic data. 

Successful Applications

Our researchers provide evolutionary approaches with a focus on optimisation. We demonstrate finesse in combining and applying data science and financial modelling concepts while making a technology decision. Our development of robust project control tools considers multiple resource uncertainties within the overall material supply chain. We also have proven success in applying advanced machine learning and the integration of optimisation concepts for decision making.

  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, 

  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, 

  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, 

  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, 

  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, 

  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,

  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, 

  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, 

  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, 

  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,