Transportation Models in the Era of the Internet of Things


Open call for doctoral grant:

Project overview:

The goal is to provide simulation and optimization models for transportation under new paradigms. Outputs of this

project should contribute to state-of-the-art mathematical models, reflecting new specificities arising under this paradigm, as well as with off- and on-line optimization algorithms to handle these problems.


The ultimate purpose of using IoT devices in vehicles is to allow moving goods and people more efficiently and safely; benefits are expected in terms of cost savings, ecological impact, and improvement in the quality of life.

Appliances in future vehicles will be permanently connected, generating huge amounts of data that can be used both for making them smarter and for improving external planning systems.  This poses many challenges in the development of algorithms: they need to be more responsive, and take into consideration much more data; they must operate in a distributed way, but must take into account global information; etc.

Many well known problems in optimization will have to be reformulated for this reality: from vehicle routing to enterprise resource planning, from facility location to end-user logistics.  Future optimization systems will be operating mostly online, making use of simulation for an enlarged reality, and taking advantage of large quantities of data -- including data not directly related to the problem (e.g., weather forecasts).

Besides, IoT will introduce radically new kinds of problems; optimizing transportation efficiency will require systemic, collaborative, dynamic approaches.  Systemic optimization concerns full consideration of the entire supply chain network and the interaction of its agents, including value co-creation and exploiting information sharing.  This can be fostered by building collaborative networks, where challenges include joint accountability and rewards, through revenue sharing mechanisms, with the aim of optimizing total system value.  A related issue is the dynamical selection of partners, upstream and downstream, which will raise challenges in dynamic rescheduling and other problems.

Holistic methods will be required for tackling these challenges, comprising systems engineering, simulation, optimization, business modeling, and other approaches, in an integrated framework.  Methods must provide a trade-off between robustness, cost/quality, and time to obtain solutions.  This poses challenges in the development of new algorithms, with repercussions in important areas such as traffic monitoring and control, daily transportation, safety, and waste management.

Project team

Ana Viana -

João Pedro Pedroso -