PhD - Elevating Food Supply Chain Resilience to Global Mega-Disruptions via analytic Decision-Making Framework of Simulation/Optimisation and Machine Learning

Project Description In recent years, the world has faced three major crises: climate change, the COVID-19 pandemic, and the Russian-Ukrainian war, which have a similar global impact on the market and supply chains, with far-reaching effects across different sectors. One sector that has been particularly affected is food safety, security, and sustainability. The global nature of these crises caused significant disruptions to food supply chains which possess complex network designs and expand globally.

This project aims to create a comprehensive framework that merges operations research and data analytics to simulate food delivery in the face of global mega-disruption scenarios and provide the intermodal transport network for food with the ability to adapt and respond. The project will offer decision-makers and policymakers a systematic interpretation and analysis of global disruptions to assist in developing proactive risk mitigation strategies. Additionally, it aims to create intelligent solutions to optimise food delivery planning. This will be achieved using mathematical and simulation models to enhance the transport networks’ performance during times of disruption, sensitivity analysis techniques to assess the model’s sensitivity to variables, and evaluation of the developed solutions under various system scenarios, such as scenario planning. Towards realising these objectives, there is a need to 1) identify the characteristics of global disruptions and investigate their impact on food delivery, how they emerge, and their propagation nature across the intermodal transport network, 2) develop the integration of methods that allow modelling the transport network response and adaptation strategies and optimising the relevant decisions during disruption, 3) propose the communication mechanisms that help to engage experts, decision-makers, policymakers, and citizens in analysing and interpreting the effect of disruption risks.

The project involves various phases, including; A. Identify disruption risks that cause significant food transportation risks and map their development pathway and implications. B. Design an analytic framework that integrates a methodological toolkit for the decision-making process of anticipation, response, and adaptation pillars. C. Employ the framework to plan disruption risk scenarios and investigate the suggested mitigation and response strategies.

Student Requirements for this Project 1. The PhD Scholarship is open to the EU and International Students 2. Applicants should show outstanding academic qualities by obtaining a 1st class honour degree (or equivalent Grade Point Average (GPA)) or MSc in computer science, management science, mathematics, or other related subjects. 3. For applicants for whom English is not their first language, An IELTS (Academic) score of 6.5 minimum (with a minimum of 6.0 in each component) should be provided. 4. Applicant should have strong mathematical and programming skills, particularly Python, Java, C or R. 5. Familiar with Machine Learning and Operations Research Algorithms

Student Stipend per annum 20,000 Materials & Travel Budget per annum €2,000 Fees covered by the funding per annum €5000 Duration of Funding 42months

Deadline to submit an application 31/08/2023

If you are interested in submitting an application for this project, please complete an Expression of Interest.

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