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Intelligent multi-objective optimisation for sustainable servitisation PhD


   School of Aerospace, Transport and Manufacturing (SATM)

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  Dr M Farsi, Prof John Ahmet Erkoyuncu  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

This is a fully-funded PhD by EPSRC and Siemens Energy under the Doctoral Training Partnership Funding. The studentship will provide a bursary of up to £20,000 (tax-free) plus tuition fees for three years. This PhD project will focus on machine learning and autonomous multi-objective optimisation to find solution for sustainable service contracts (servitisation) for complex engineering assets (e.g. gas turbine).

This project aims to develop a hybrid multi-objective optimisation model using efficient evolutionary algorithms and multi-agent network robustness optimisation. The potential outcome of this research will tackle the existing challenges in service contract design in high-value manufacturing. 

Servitisation refers to the service transformation from supplying only products to providing integrated products and services. Over the past decades, servitisation aims to create ‘economic value’ for manufacturers and their customers. Industry 4.0 enables manufacturers to develop innovative technologies to increase their competitiveness continuously. Emerging technologies must support sustainable development goals to make a world technologically advanced and create a healthier, safer, and happier place to live.

This necessitates companies to deploy digital infrastructure to quantify the integrated sustainability impacts of their services and manufacturing processes effectively and efficiently. To achieve this, it is required to design integrated product-service solutions that enable the manufacturers to create early value for money and reduce the environmental/social impacts of servitisation. Several challenges exist to move towards such so-called ‘sustainable servitisation’; (i) the highly interactive processes over a product lifecycle cause complications for integrated sustainability assessment; (ii) major assets comprise hundreds of components with highly interactive functions that increase the operational risk and uncertainty; (iii) manufacturers require to provide sustainable and affordable products and services just-in-time with minimum operational disruptions for customers

Aim

This project investigates novel optimisation approaches for autonomous sustainability impact quantification while designing optimal product-service solutions to address these challenges. Autonomous means are intelligent with minimum human intervention. The autonomous multi-objective optimisation approach will accommodate the quantification of stochastic (dynamic and non-linear) uncertainties, the robustness of the optimisation function, and computational efficiency in the optimisation algorithm. This project aims to develop a hybrid multi-objective optimisation model using efficient evolutionary algorithms and multi-agent network robustness optimisation. The key objectives are:

1. Develop a novel mathematical model of multi-objective optimisation for sustainable servitisation.

2. Develop a system design architecture of the required digital infrastructure to quantify the integrated sustainability impacts.

3. Integrate machine learning techniques with the optimisation model to quantify early-stage value for money in servitisation.

4. Demonstrate the application of the intelligent optimiser toolset using an industrial case study.This is a fully-funded PhD by EPSRC and Siemens Energy under the Doctoral Training Partnership Funding. The studentship will provide a bursary of up to £20,000 (tax-free) plus tuition fees for three years. This PhD project will focus on machine learning and autonomous multi-objective optimisation to find a solution for sustainable service contracts (servitisation) for complex engineering assets (e.g. gas turbine). This project aims to develop a hybrid multi-objective optimisation model using efficient evolutionary algorithms and multi-agent network robustness optimisation. The potential outcome of this research will tackle the existing challenges in service contract design in high-value manufacturing. 

This project collaborates with Siemens Energy Industrial Turbomachinery (SEIT). SEIT is committed to manufacture and support Gas Turbines to provide sustainable, reliable, affordable energy to the world. As part of this study, Siemens would like to provide a case study for testing and deploying the research solution on smart sensors and emissions monitoring to further improve the sustainable value of our gas turbines.

The potential outcome of this research will tackle the existing challenges in service contract design in high-value manufacturing. This PhD project is expected to design a sustainable product-service solution for high-value equipment to enable the manufacturers to create early-value for money and reduce the environmental/social impacts through servitisation.

This PhD project provides the opportunity to collaborate with Siemens Energy Industrial Turbomachinery as one of the leading companies in the energy sector in the UK. Cranfield University and Siemens Energy will provide training on several transferable skills such as enterprise modelling, complex system modelling, simulation techniques, gas turbine, sensor technology and IT platforms.

The successful candidate has this opportunity to disseminate the outcome of the research in relevant conferences. In addition, Cranfield University and Siemens Energy will provide several training sessions on relevant academic and research skills, software and technical skills.

Entry requirements

Applicants should have a first- or second-class UK honours degree or equivalent in a related discipline. This project would suit applicants with a background in engineering or other related manufacturing or data science area. Moreover, it is expected that the applicants demonstrate good problem-solving and time-management skills. The successful applicant is expected to conduct high-quality research and literature review on the relevant research area. The candidate is encouraged to publish high-quality articles in the related leading journals, work closely with the academic and industrial supervisors, and collaborate in the research activities within the Centre. It is expected that s/he presents their work to academic and professional audiences and follows the PhD courses and the training designed for PhDs at Cranfield University.

About the sponsor

Sponsored by EPSRC and Siemens Energy under the Doctoral Training Partnership Funding 2020/21. The studentship will provide a bursary of up to £20,000 (tax-free) plus tuition fees for three years.         

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

If you are eligible to apply for this research studentship please complete the online application form.


Funding Notes

This studentship is sponsored by EPSRC and Siemens Energy under the Doctoral Training Partnership Funding 2020/21. The studentship will provide a bursary of up to £20,000 (tax-free) plus tuition fees for three years.
To be eligible for this funding, applicants must the following criteria:
Be a UK National (meeting residency requirements), or
Have settled status, or
Have pre-settled status (meeting residency requirements), or
Have indefinite leave to remain or enter.

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