Artificial Intelligence Based Approaches for
Supply Chain Embedded Project Scheduling Problems

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Project scheduling has been considered one of the critical tasks in project management, which predominantly assumes scheduling project activities by satisfying precedence and resource constraints. By virtue of proper project scheduling, a project manager can determine the timeline, allocate resources, plan for budget, and most importantly, get a sense of the reality of delivering the project.

Nevertheless, blaming the COVID-19 epidemic and its subsequent impacts, traditional project management and scheduling are now characterised by volatility, uncertainty, complexity, and ambiguity. Thus, to sustain Australia’s economic growth and remain competitive in the new reality, it is vital not to cancel but to optimise affected or vulnerable project portfolios. To do so, embedding business reforms, speeding the digital transformation of classical projects, and implementing advanced technologies and automation programs (e.g. artificial intelligence-based approaches) in supply chain and project management problems can encourage better business agility, which is potentially the shortcomings in the current literature.

Moreover, in practice, while scheduling the activities of multiple projects in dynamic environments, project managers face challenges that are typically due to the lack of timely, accurate, and consistent information; finite resource transfer times, and interdependencies among activities of different projects; and uncertain activity interruptions. Therefore, to avoid such shortcomings, an integrated framework considers both project management features and supply chain drivers; advanced solution approaches and better ways of dealing with uncertainties (e.g. artificial intelligence-based) can lead to an optimal decision support system for the whole business.


With the above considerations, this webinar will explain the role of AI in Complex Project Scheduling and will explore the following points:

  • How to define an efficient supply chain embedded project scheduling problem (SCEPSP) along with its parametric analysis,
  • The role of advanced evolutionary algorithm for solving the deterministic SCEPSP,
  • And how to tackle data dynamicity, ambiguity and uncertainty in executing a supply chain embedded project.


Dr Ripon Chakrabortty

Dr Ripon Kumar Chakrabortty specialises in Data-Driven Decision Support & Analytics for Operations Management Problems. He currently lectures at the University of New South Wales (UNSW), Canberra about the topic of Systems Engineering & Decision Analytics. His research interests cover a wide range of topics in decision analytics, evolutionary computation, operations research, and applied optimization in “Project Management and Supply Chain Management”. He has written two book chapters and has published over 160 technical journal and conference papers in multiple prestigious venues, along with more than 2.4 million dollars worth of research grants from different organisations.