"Dynamic scheduling" or "integrated project management and control" are two terms used in academic literature to describe data-driven project portfolio management. It is a project management approach for planning, monitoring, and controlling ongoing projects so that they may be delivered to the client on time and on budget. Data-driven project management comprises of three crucial aspects, i.e., baseline schedule, schedule risk analysis and project control. Baseline Scheduling is the process of preparing project activities in order to generate a project schedule that adheres to time and financial constraints. The risk of the baseline schedule and its influence on the project's time and budget are examined in Schedule Risk Analysis. Meanwhile, Project Control duties include collecting and analysing project performance data and taking measures to get the project back on track.
Most existing research in the project management domain assumes a deterministic environment along with the availability of complete information at any given time that works quite well to provide a workable baseline schedule. While data dynamicity and uncertainties are a concern, most existing prediction models fail to predict long-term, uncertain values. Notably, advancements in computing power and the requisite for real-time prediction and decision-making by analysing large-scale data have caused artificial intelligence (AI) proliferation in the last few decades, which, regrettably, are very few in the project management domain to date. Our research focuses on placing Australian manufacturing firms and construction industries at the forefront of developing reliable and integrated decision support tools by following data-driven project portfolio management strategies. Our research focuses on considering data ambiguities, uncertainties and dynamicity in a single framework.
Our research is aimed at delivering:
- Resilient and proactive project scheduling frameworks
- More effective uncertainty management
- Reliable and integrated decision support tool for maximum project success
- Improved utilisation of artificial intelligence and evolutionary algorithms while solving complex project scheduling problems.
Our research group bring together experts with remarkable track records in Cross-disciplinary research areas (such as supply chain management, network science, evolutionary algorithms, artificial intelligence business modelling, and simulation & modelling). We have long-standing experience in working with different project scheduling problems and then, consecutively, solving them by different advanced evolutionary algorithms. We can help organisations to better understand their capacity system and how it may lead to enhanced outputs and results, starting with the assumption that the focus should be on achieving high performance. Our emphasis on collaborating with organisations and doing applied research helps practitioners get the information and skills they need to put our research results into practice. The research team has extensive experience designing and implementing funded research from the ARC, industry partners, and other sources. We ensure that for any potential project design, the composition of the research team and the budget required for that project will reflect a concerted effort to calibrate a project that accounts for common project risks. In addition, we endeavour to communicate our findings to the public using local and national online news and communication platforms such as LinkedIn and other social media. Through UNSW, this research group has access to the supercomputer at National Computational Infrastructure (NCI), which are used to develop and test the developed algorithms. We have the necessary office space, access to information and library resources necessary to carry out any possible research matters.