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Accountable and responsible logistics optimisation via distributed AI


   Centre for Accountable, Responsible and Transparent AI

   Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

Logistics is the process of obtaining, transporting, and distributing material and products in proper places and proper quantities. Logistics management is a part of the supply chain management that plans, implements, and controls the forward and reverse flows of storage, services, and information between origin and consumption points to meet customers' requirements. It underpins every aspect of modern life. Better logistics will secure more efficient road freight and economic growth.

Logistics network evaluation is very complex as there are many constraints to consider. From date and time restrictions, vehicle capacities, and drivers’ schedules, to the number and duration of stops, this project will evaluate the existing network structure, and highlight bottlenecks and inefficiencies, for higher capacity utilisation and less time spent on the roads.

This PhD project employs distributed AI, operational research, and reinforcement learning to tackle emerging challenges in the supply chain management and transportation sector. It will study an uncertain logistics network that optimises the total rewards while satisfying uncertain demands, examine how sensitive a model is to some variables by sensitivity analysis, and evaluate the developed solutions under worst-case guarantees and average-case scenarios.

This is an opportunity to be part of a creative research team with an interdisciplinary background in computer science, mathematics, and economics. The project provides collaboration and training opportunities in the industry. It will combine domain knowledge and expertise in machine learning and data science.

The outcome of the PhD project will be developed algorithms, open-source software, and plausible solutions (protocol, platform, evaluation) through research initiatives to guide the logistics community.

The project will be carried out as part of an interdisciplinary integrated PhD in the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI (ART-AI). The ART-AI CDT aims at producing interdisciplinary graduates who can act as leaders and innovators with the knowledge to make the right decisions on what is possible, what is desirable, and how AI can be ethically, safely and effectively deployed. We value people from different life experiences with a passion for research. The CDT's mission is to graduate diverse specialists with perspectives who can go out in the world and make a difference.

Successful applicants will have, or expect to receive, a master's degree or first or upper-second bachelor's degree in computer science, mathematics, management, or other related subjects, and a good level of programming skills. You will need to have taken a mathematics or a quantitative methods unit at university or have at least grade B in A level maths or international equivalent.

Formal applications should include a research proposal and be made via the University of Bath’s online application form. Enquiries about the application process should be sent to . Enquiries about the research should be directed to Dr Zhang.

Start date: 2 October 2023.


Funding Notes

ART-AI CDT studentships are available on a competition basis and applicants are advised to apply early as offers are made from January onwards. Funding will cover tuition fees and maintenance at the UKRI doctoral stipend rate (£17,668 per annum in 2022/23, increased annually in line with the GDP deflator) for up to 4 years.
We also welcome applications from candidates who can source their own funding.

References

[1] W. B.Powell, H. Topaloglu, Stochastic Programming in Transportation and Logistics, Handbooks in Operations Research and Management Science, 2003.
[2] R. Toorajipour, V. Sohrabpour, A. Nazarpour, P. Oghazi, M. Fischl, Artificial intelligence in supply chain management: A systematic literature review, Journal of Business Research, Elsevier, vol. 122(C), 2021.

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