Industrial Systems, Manufacturing and Management - Final Projects 2017
Tarek Maamari
Logic based row and column generation approach for solving integrated airline recovery problem
Airlines face a multitude of disruptions within their day of operations. The options available to them are to prepare for potential disruptions in advance by building in flexibility to their operations, and to respond to disruptions by rapidly finding solutions to bring three key resources together: aircraft, crew, and passengers. This project will investigate the possibility of preparing for potential disruptions in advance, and building integrated methods to search for optimal solutions. The project is sponsored by Boeing.
Supervisor: Alena Puchkova
Adrien Ruche
Predicting supply chain disruptions in Aerospace
Disruptions in supply chains cost the aerospace industry millions of pounds annually. If potential disruptions could be estimated in advance; appropriate mitigation mechanisms could be put in place to reduce their impact. In this project the student will work with the DIAL team and the Boeing Company, to apply a set of machine learning algorithms to goods delivery data for predicting supply chain disruptions.
Supervisors: Philip Woodall, Alexandra Brintrup, Pascal Wichmann
Designing resilient supply chains for disaster relief operations
Supervisor: Tariq Masood
Automated fault classification using machine learning for gas turbine failures
Supervisor: Zhenglin Liang
Anika Mistry
Optimisation of Resource Allocation and Production Sequencing in Off-site Construction
Supervisor: Raj Srinivasan
Barnaby Lloyd
Developing a Model for Automation Maturity: A Universal Method of Assessing the Maturity of Automated Systems
Supervisor: Alan Thorne
Svenja Fischer
Factors influencing the acceptance of self-service data preparation and analytics tools
Supervisors: Mohamed Zaki & Philip Woodall
Vivien Aufort
Optimisation of the production sequence for an off-site construction factory
Supervisor: Raj Srinivasan
Gregory Heidenreich
Optimisation in Farming
The production of agricultural products often involve intervention decisions on the quantity and frequency of watering, use of fertilisers, pesticides and herbicides, which are often determined by weather and disease events. These decisions in turn determine crop yield and have an impact on possible crop rotation. Although traditionally matching demand from wholesalers and these parameters involved tacit knowledge, modelling tools such as optimisation are increasingly considered for structured decision support. This project will consider the feasibility and benefits of optimisation using existing tools on a given range of farming scenarios. The project is sponsored by Gs Growers.
Supervisor: Alexandra Brintrup
Bas aan de Stegge
Quality analytics
Working with a powder metallurgy producer, this project will explore the use of online data from metrology equipment on the factory floor for to detect early warning signs of quality issues so that appropriate action can be taken during production. To achieve this, we will explore data analytics methods that can capture, filter, and analyse metrology data and convert it into adjustments of a quality inspection plan.
Supervisor: Alexandra Brintrup
Paul Jaccarini
A survey of supply network analytics: Opportunities and Challenges
One of the unifying concepts in Industry 4.0 technologies and capabilities, is the explosion of data in manufacturing. Supply network data analytics is the science of studying data to discover hidden patterns that yield useful insights for improving supply chain operations. For example, these insights can be used to forecast deliveries, predict quality of goods, estimate the best price for procurement negotiations etc. In this project the student will survey a variety of manufacturing companies to understand what data is being generated and shared with their supply chain, to what extent it is being used and what potential opportunities and challenges exit within the emerging field of supply network data analytics.
Supervisor: Alexandra Brintrup