Development of an intelligent scheduling system to optimise the sustainability measures in multi-disciplinary engineering projects

Within Galliford Try group, project delivery is supported by a system which provide procedures for tasks at different project phases delivered in line with the Royal Institute of British Architects (RIBA) plan of work. However, within the group there is no robust approach to exploit data for improving project performance. As such, this project will develop a mechanism by which DA and AI capabilities will be acquired to improve data management. This will include capturing both subjective and objective project data and interrogating the data to identify important yet hidden data patterns and trends to enable the company to learn from past and ongoing projects, monitor task outcomes and adapt to changes along different project phases. The intelligent mechanism will be embedded into the scheduling system to manage all critical tasks (decisions) with an aim to optimise the project sustainability.

The project outcomes are:

• Construction of an analytical capability to support dynamic project analysis and reporting
• Generation of decision-making knowledge from past projects and stakeholders; and
• Development of an intelligent scheduling system with self-learning and adapting capabilities to optimise project sustainability.

This project will help the company determine the best investment in time and cost to meet the scope while optimising the project sustainability. This will enable the company and the sector to grow in the most sustainable direction without compromising the society and environment.

Further information

The company will provide office and computing facilities when there is a requirement for the student to visit its sites, gain experience, or collect/analyse data. It will also cover travel and other project expenses to support the student and research progress. Other specific facilities which are suitable and available would be also provided.

Eligibility

We're looking for an outstanding and enthusiastic PhD student with a Masters degree (MEng or BEng & MSc) and appropriate expertise in one or more of the following areas:

• engineering management
• sytems engineering
• project planning and control
• data analytics
• machine learning
• artificial intelligence

The candidate should be able to undertake, complete and disseminate the outcomes of this research in international conferences and high-impact research journals. In particular, the candidate is expected to understand and apply a number of software systems within the project period such as:

• MS PowerBI
• Python (or R)
• MS Excel
• MS Azure
• Primavera P6