In the presence of increasingly dynamic environments, frequent uncertainties, high customer specifications, strict project deadlines, and stricter requirements on sustainability and scarcity of resources, modern project managers are challenged in their ability to schedule and control projects. Thus, in the context of sustainable project scheduling context, two important elements are to be considered as decision variables: the input elements of a scheduling (e.g. resources: workforce, machine, money) that enable the realisation of a schedule for a project and the output element that are consequences of the realisation of the project (e.g. noise, pollution, waste).

In this context, innovative approaches and concepts available in the era of fourth generation industrial revolution, such as cyber-physical systems and big data, can bring insights to the development of effective and efficient sustainable project scheduling systems. Therefore, this project intends to present novel Cyber Physical System (CPS) and big data-enabled project scheduling approaches to address the above challenge by ensuring project efficiency and sustainability with regards to the environmental and economic dimensions.

Research objectives

a mathematical model of the problem under study will be developed considering both profit-driven objectives (like makespan, profit) and sustainability (like energy consumptions) and temporal constraints

the energy model, which is enabled by CPS, big data analytics and intelligent learning algorithms, consider the dynamic conditions of a project

a novel scheduling, process monitoring (learning), and rescheduling approach will be designed to enhance projects' performances.

practical case studies will testify the effectiveness and great potential of applicability of the system in practice.

Contact:

Dr Ripon Chakrabortty r.chakrabortty@adfa.edu.au or Dr Humyun Fuad Rahman md.rahman@unsw.edu.au for further information. Each potential student needs to write a research proposal highlighting research motivation, research problems, research objectives, a brief review of the most relevant literature, proposed methodology and expected outcome.

School

School of Engineering & IT

Research Area

Technology Decision Making | Optimization & Design | Project Management

Supervisor

PG Project Management, Decision Analytics, and Engineering Science Program Coordinator & Lecturer - Systems Engineering & Decision Analytics Ripon Chakrabortty
PG Project Management, Decision Analytics, and Engineering Science Program Coordinator & Lecturer - Systems Engineering & Decision Analytics