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Reducing Greenhouse Gases in Freight Transportation and Vehicle Routing Using Artificial Intelligence Methods (Advert Reference: SF19/BL/MOS/QU1)

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Transportation is a significant contributor to greenhouse gases such as carbon dioxide and other harmful emissions such as nitrogen oxides. The rate of production of these gases is not only linked to the total distances travelled by vehicles but also other factors such as vehicle speeds, road gradients, congestion, acceleration and deceleration, empty kilometres, fleet vehicle mixes and many more [1].

Artificial intelligence algorithms are increasingly used to design routes, select fleet mixes and model networks but they traditionally focus on maximising efficiencies and minimising costs. An emerging direction is to also consider environmental impact, greener solutions and their benefits to organisations and society [2,3,4,5].

This PhD project is an exciting opportunity to develop solutions, which will not only have a direct beneficial impact on the environment and society but also offer significant commercial advantages for transportation, logistics and marketing operations.

This PhD project will focus on two emerging challenges. Firstly, congestion data and road gradients are now readily available, but it is not being fully utilised in vehicle fleet routing [6]. New optimisation models and algorithms which incorporate all details of the road networks and the types of vehicles are needed. Secondly, as fleets transform to electric and other alternatively powered vehicles, new research questions have appeared. The project will research and develop solutions to 1) Identify optimal fleet mixes to fulfil an organisation’s operations whilst minimising excess capacity and costs. 2) Optimise fleet route plans and schedules for electric and next generation vehicles which have limited range, fewer re-charging locations and longer re-charging times. 3) Design more efficient networks which locate hubs, depots and re-charging points to maximise the efficacy of electric fleets.

This project is supervised by Dr Yi Qu and Professor Charles Cui.

Eligibility and How to Apply:

Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.

For further details of how to apply, entry requirements and the application form, see: https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF19/…) will not be considered.

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.

Funding Notes

Please note this is a self-funded project and does not include tuition fees or stipend.

References

Qu, Yi, Bektas, Tolga and Bennell, Julia (2016) Sustainability SI: multimode multicommodity network design model for intermodal freight transportation with transfer and emission costs. Networks and Spatial Economics, 16 (1). pp. 303-329. ISSN 1566-113X

Qu, Yi and Curtois, Timothy (2017) Job Insertion for the Pickup and Delivery Problem with Time Windows. Lecture Notes in Management Science, 9. pp. 26-32. ISSN 1927-0097

Curtois, Timothy, Laesanklang, Wasakorn, Landa-Silva, Dario, Mesgarpour, Mohammad and Qu, Yi (2017) Towards Collaborative Optimisation in a Shared-logistics Environment for Pickup and Delivery Operations. In: Proceedings of the 6th International Conference on Operations Research and Enterprise Systems. SCITEPRESS, pp. 477-482. ISBN 9789897582189

Curtois, Timothy, Landa-Silva, Dario, Qu, Yi and Laesanklang, Wasakorn (2018) Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows.EURO Journal on Transportation and Logistics, 7 (2). pp. 151-192. ISSN 2192-4376

References

1. Demir, E., Bektaş, T., & Laporte, G. (2014). A review of recent research on green road freight transportation. European Journal of Operational Research, 237(3), 775-793.

2. Dekker, R., Bloemhof, J., & Mallidis, I. (2012). Operations Research for green logistics–An overview of aspects, issues, contributions and challenges. European Journal of Operational Research, 219(3), 671-679.

3. Qu, Y., & Curtois, T. (2017). Job Insertion for the Pickup and Delivery Problem with Time Windows. Lecture Notes in Management Science, 9, 26-32.

4. Koç, Ç., & Karaoglan, I. (2016). The green vehicle routing problem: A heuristic based exact solution approach. Applied Soft Computing, 39, 154-164.

5. Curtois, T., Landa-Silva, D., Qu, Y., & Laesanklang, W. (2018). Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows. EURO Journal on Transportation and Logistics, 1-42.

6. Lin, C., Choy, K. L., Ho, G. T., Chung, S. H., & Lam, H. Y. (2014). Survey of green vehicle routing problem: past and future trends. Expert Systems with Applications, 41(4), 1118-1138.

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