Current PhD Projects
Disruption management for off-site construction
Off-site construction operations can be subject to downstream construction site disruptions. These disruptions - such as high wind conditions limiting on-site crane movements for example - delay on-site construction and impact on the effectiveness of the off-site production of construction modules. Brian researches how different disruption management strategies can be used to improve system performance in the face of disruptions.
Supply chains are increasingly global, complex and multi-tiered. Consequently, companies often struggle to maintain complete visibility of their upstream supply network. This poses a problem as visibility of the network is required in order to effectively manage supply chain risk. Pascal is working on automated methods to generate supply chain maps from openly available text sources for the purpose of improving resilience-related decision-making.
Probabilistic dependencies in supply networks
My research examines the use of machine learning and statistical techniques on large industrial datasets in order to deduce complex, probabilistic dependencies between parameters in dynamic, non-deterministic and partially-known environments. There are many possible applications of this research but my initial aim is to apply this knowledge to the multi-goal optimisation of large and deeply connected supply networks.
Gishan Don Ranasinghe
Social Network of Machines
Gishan’s research is targeting to enable industrial agents to generate their thinking criteria to make relationships with other agents in order to collaboratively achieve objectives (e.g. prognostics and workload sharing) in industrial environments. Main research questions are: 1) how can industrial agents represent knowledge? (definition: knowledge in here refers to the contextual awareness of each asset. That is, assets perceiving the world in a much broader sense by understanding their own environment (i.e. other assets and problem space). The aim of addressing this research question is to design industrial agents who translate the real-world of assets into a mathematical representation, which can then be used by the algorithms to address research question 2. 2). How can industrial agents incorporate cognition? The aim of addressing this research question is to develop algorithms to facilitate industrial agents with reasoning and rationalising capabilities to generate their thinking criteria to make relationships and form social networks. 3) How can these social networks of industrial agents be applied to the real-world manufacturing? The aim of addressing this research question is to extend the project outcome to be evaluated using real-world industrial use cases (using a mini-scale physical simulation of a production line which will be developed in parallel to this research project). Relevant concepts are deep reinforcement learning, multi-agent systems, holonic manufacturing systems, recommender systems, social network analysis and asset management
Distributed workload planning and predictive maintenance
As condition-based maintenance, system-wide optimisation, and value-oriented production become the three major directions of the evolution of asset management, maintenance can no longer be considered as isolated from other production activities. Studies have shown that the degradation process of machines are dependent on the operation being performed (e.g., higher workload results in faster degradation). However, the intrinsic connection between operation and condition-based maintenance has not been sufficiently addressed in system-wide asset management optimisation problems. Hao’s research is set in a scenario where parallel assets cooperate to achieve production goals. The aim of her research is to develop a model to assist with integrated optimisation of both load allocation and condition-based maintenance in order to maximise system-level profit in the long run.
Adria Salvador Palau
Distributed collaborative learning for prognosis and predictive maintenance
Adrià Salvador Palau's main field of research is distributed collaborative learning for asset prognostics. In other words, Adrià’s focus is on distributed intelligence in large fleets of machines. He has developed an architecture that allows for each individual machine develop its own predictive models, while updating it with information from the rest of the fleet. In this system, machines talk with each other in order to update neural network models that then are used to improve their predictions. Adrià’s approach is being tested in a fleet of hundreds of Gas Turbines located all around the world.
Information management processes within asset management
The importance of information management is gaining momentum within the Engineering Asset Management domain, both in academic literature and industry applications. Being guided by an array of industry standards that
solely focuses on information management processes within the life-cycle of engineering assets. Most noticeably PAS 1192-3 focusing on Building Information Modelling (BIM) and the associated information management processes within the operational phase of an asset. James’ research aims to address the challenges provided in PAS 1192-3 that says organisation should develop asset information requirements by providing a top-down practical methodology for defining asset information requirements.
Portfolio asset management
My current research focuses on how an organisation deals with a portfolio of diverse assets that have impacts on different stakeholders. Both monetary impact (e.g. asset maintenance cost) and non-monetary impact (e.g. customer disruption) are taken into consideration. With limited budget and time, we delve into how the management deals with multiple objectives arisen from the preferences of different stakeholders. To address this multi-objective problem, I develop a framework to help asset managers make decisions that yield the maximum benefit for an organisation. It is expected that the outcomes of the project will enable decision makers to devise the most efficient and effective intervention plan, leading to the optimal budget allocation strategy.
In the existing manufacturing environment, the unexpected events are causing disruptions that are “negative” (machine breakdown or delayed/defective supply of parts to manufacturers) but more increasingly “positive” (a rush order from customer, or a late customisation request). This is resulting in both the loss of customers as well as production losses. Owing to the inherent capabilities of the so-called “factory-in-the-box”, a 3D printer can address “negative” but more importantly “positive” disruptions that are primarily driven by customers (‘responsiveness’).
Mudassar attempts to develop a model that will quantify and represent how the rapid manufacturing capabilities offered by AM influence the responsiveness (flexibility, efficiency, effectiveness etc) of a production system, which will help find a place (in favour or against) this so-called disruptive technology.
Completed PhD Projects
Complex industrial assets such as power transformers are subject to accelerated deterioration when one of its constituent component malfunctions, affecting the condition of other components – a phenomenon called ‘fault propagation’. Zhenglin’s research is to design a novel approach for optimizing condition-based maintenance policies for such assets by modelling their deterioration as a multiple dependent deterioration path process. The aim of the policy is to replace the malfunctioned component and mitigate accelerated deterioration at minimal impact to the business. The maintenance model provides guidance on determining inspection and maintenance strategies to optimize asset availability and operational cost.
Today’s warehouses are faced with new challenges that require strategies that can offer more flexibility than conventional strategies are able to do. In this context, Wenrong’s PhD project aims to address the flexibility challenge by investigating how order-picking, which is a key factor affecting warehouse performance, can be dynamically managed. The order-picking operation is typically constrained by inventory management in the warehouse and transportation management of placed orders. More specifically, the order-picking operation is managed based on three key decisions:
a) When should the orders be picked from the warehouse?
b) Which storage location should the order-picker visit?
c) How should the orders be batched together to form a pick-list?
Having proposed an interventionist routing algorithm to enable the dynamic re-routing of an order-picker during the picking operation, Wenrong’s project now investigates the dynamism from making the three decisions in different sequences. By formulating the problem as a Markov Decision Process (MDP), Wenrong aims to develop a method of making the decisions in appropriate sequence based on the status of the operation so as to improve the flexibility as well as the efficiency of the order-picking operation.
Torben's Phd project is addressing problems of data overload and the Value of Information. Currently most information systems specifically allocate data to a specific user. However, the amount of data and the number of tasks each user has to do are constantly increasing at a very large rate. Therefore, companies are having a massive data overload problem and ensuring the right data is getting to the right users can be difficult for them. Torben tries to address this problem by using market-based techniques, which use the principle of markets in economics. The user has a certain utility (or value) for specific datasets or dataset combinations. At the same time datasets have costs. Markets like supermarkets combine these two in an efficient manner. Consumers in a supermarket have a specific value for certain products and the supermarket has costs associated in offering these. The market approach is working quite efficiently in various applications and has been shown to work well for similar resource allocation problems. By applying this approach towards data management, Torben is hoping to improve the user decision-making by providing him with the right information and to identify a value for companies large amounts of datasets.
The engineering asset performances such as reliability and maintainability directly impact the ultimate overall business performance. Therefore asset intensive manufacturing companies heavily rely on their engineering asset management systems to gain core competitive advantages. However, developing effective performance measures for valuable and complicated engineering asset management (EAM) has always been a challenge for asset intensive manufacturing organisations. Additionally having effective performance measures in place is required by a number of international standards on engineering asset management such as ISO55000 and PAS55. There is a very limited number of existing studies on the proposed topic, and the mainstream approach in generic performance measurement literature is not the most suitable in the context of organisations’ EAM. A crucial reason is that risk control is not included as independently essential perspective. However EAM heavily relies on the successful management of various risks such as asset safety, reliability and many other potential hazards. Furthermore, the complexity and scope of EAM is quite difficult to be modelled in the performance measurement, therefore leading literature and practical experience of EAM should be adapted in the design process to understand the full picture of EAM. JQ Wang has proposed frameworks by refining existing approach of designing performance measures for asset-intensive organisations’ EAM. Risk control elements and leading EAM knowledge will be factor in the design process to assist organisations to select their performance measures holistically. JQ will apply three phase case studies including a facilitator case study for validating the research. His pilot case study has proved that the frameworks are usable and feasible for partner organisations to review and improve existing performance measures for their EAM.
Joel’s research seeks to address the dynamic nature of assets’ criticality. So far criticality analysis, which is a tool for deciding what assets should have priority within a maintenance management program, has been treated as a static concept both in literature and in practice. The myth is: “…we have just concluded our criticality analysis; we can now check that box...” But insufficient understanding of the changing nature of criticality has led to misalignment between asset maintenance strategies and the business goals of the organisation over time. Joel attempts to develop a model that will combine several multi criteria decision making techniques to identify factors that influence changes in criticality. These factors/criteria will depend upon asset operating condition, business environment, maintenance objectives and key performance indicators of the organisation. The algorithm should detect changes in criticality, connect to the company’s enterprise asset management system to automatically reproduce the analysis and update criticality accordingly. From this research, Joel is hoping to automatically adjust maintenance program to business needs by exploiting the dynamic nature of criticality to generate dynamic CBM strategies.