UCL Energy Institute


New PhD studentship on last mile logistics and the shared economy

21 February 2023

Applications are now open for a proposed studentship 'Last mile logistics and the shared economy: Developing operational solutions for novel last mile concepts', as part of UCL EPSRC DTP.

Freight trucks parked up

About the project

Project title: Last mile logistics and the shared economy: Developing operational solutions for novel last mile concepts
Project supervisors:     
Dr Manos ChaniotakisProfessor Andreas Schafer

Responsible for a significant share of global Carbon Dioxide emissions and annual Greenhouse Gas Emissions, the global transport sector must decarbonise rapidly if global climate change targets are to be met. Within freight transportation, the last mile problem, the last leg of the delivery service, is consistently the most expensive and one of the highest polluting segments of the supply chain. Last-mile delivery has increasingly received attention due to the unprecedented growth in e-commerce; placing considerable stress on city planners and businesses to deliver rapid, low-cost and sustainable last-mile services.

Currently, practices that attempt to circumvent the perceived trade-off between urban LMD efficiency and environmental sustainability prioritise vehicle electrification, drone technology and off-peak delivery. However, frequently neglected are schemes that favour the integration of freight and passenger transportation. “Crowdshipping”, “Freight-Sharing”, “Cargo Hitching” or “Crowd logistics” are all concepts that aim to exploit the underutilised capacity in various passenger transportation modes to additionally deliver goods. Combinations of these different concepts have the potential to significantly increase efficiency and robustness of last mile logistics. 

Studentship aims

This PhD topic will target the use of unsupervised learning, optimisation and agent-based modelling for solving operation-research-related problems that would devise solutions for optimised last mile logistics. The PhD is expected to establish a methodological framework for statistical learning metamodeling approaches and examine optimality properties. The student will apply the methodological framework to real-world delivery data to enable a practical application.

Person specification

The project is well-suited to a highly quantitative individual with strong mathematical, operations, research or Machine Learning background. Students should have a bachelor's or master’s degree in engineering, computer science, data science, mathematics, physics or a closely-related discipline, awarded with first-class or upper second-class (2:1) honours, or an overseas qualification of an equivalent standard from a recognised higher education institute. Candidates without a master's degree may be admitted in exceptional cases where suitable research or professional, experience can be demonstrated.

  • Excellent analytical and computing skills. Passionate about modelling, programming, data analysis, and conducting research. 
  • A MSc degree on Operations Research Computer Science, Mathematics, physics, transport or a closely-related discipline.
  • Knowledge of relevant programming languages or statistical software (such as Python, C++, R, MATLab etc.) 
  • Ability use own initiative, prioritise workload, and be a fair team player
  • Good interpersonal and communication skills (oral and written) 
  • A high level of attention to detail in working methods
  • Interest in the challenges of the Transport sector of the 21st century

How to apply

The post must be taken up before 1 June 2023.

Please apply via the UCL Prospectus clearly selecting the year of entry 2022/23 full time studies.  
Clearly state in the application section the title of the studentship: Last Mile Logistics and the Shared Economy

Application deadline: 23:59 PM (UK time) Thursday 9 March 2023.

For further information on the application process please contact bseer-phd-admin@ucl.ac.uk.
For  information regarding the project  please contact Manos Chaniotakis.