Raeesi, Ramin (2021) Fuelling the zero-emissions road freight of the future: routing of mobile fuellers. In: 31st European Conference on Operational Research, 11 July - 14 July 2021, Athens, Greece. (KAR id:90397)
PDF
Publisher pdf
Language: English
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
|
|
Download this file (PDF/17MB) |
Preview |
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: http://files.convin.gr/various/EURO21-Conference_e... |
Abstract
The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios.
Item Type: | Conference or workshop item (Paper) |
---|---|
Subjects: | H Social Sciences > HA Statistics > HA33 Management Science |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | Ramin Raeesi |
Date Deposited: | 28 Sep 2021 10:06 UTC |
Last Modified: | 04 Oct 2023 16:32 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/90397 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):