The official title of my PhD is ''Modelling and Solving Dynamic and Stochastic Vehicle Routing and Scheduling Problems using efficiently forecasted link attribute'' which combines the areas of both forecasting and optimisation. My 3 supervisors are Konstantinos Zografos and Nikos Kourentzes from the Management Science department and Matt Nunes from the Mathematics and Statistics department.
The vehicle routing and scheduling problem or VRP as it is often shortened to is a very widely studied area and broad area. VRP optimisation models are linear programmes upon a network. The network is a representation of the road layout with nodes being junctions or customers and edges being the roads in-between. There exist many optimisation models and solutions within even the much smaller area of Hazardous Material, or Hazmat, routing.
There are many risks associated with the transport of hazardous materials. An accident can escalate into something much worse such as a fire or explosion due to the hazardous material being carried. Of most pressing concern is the danger to those nearby should an accident occur. Less people are likely to be injured or even killed on a country lane than if the accident happens in a busy city centre.
Vehicles carrying hazardous materials should travel upon routes where they are least likely to crash and that pose the least danger should an accident occur. The best route is called the optimal route. The selection of the optimal route uses an optimisation model which calculates the risk along each of the edges in the route to create the total risk.
Some of the things that contribute towards the risk such as vehicle speed are unknown beforehand. These values can be predicted using forecasting methods. If a road is congested it is likely that those nearby will also be congested. Therefore the network structure of the road layout must be taken into account when forecasting. My PhD will focus upon using forecasting with an optimisation model to try and find the best routes for hazardous material vehicles to take.