Descripció del projecte
Transportation and logistics (T&L) activities represent a key sector in worldwide economies, being a significant contributor to social and economic progress in modern societies.
Due to its potential applications to real-life operations, one of the most recurrent topics in the T&L literature is that of modeling and optimizing tour assignments of vehicles.
This is known as the vehicle routing problem (Toth and Vigo, 2014), where each customer has a given demand that has to be satisfied without exceeding a maximum vehicle capacity.
Moreover, modern T&L systems include heterogeneous fleets consisting of traditional internal combustion engine vehicles as well as other types of vehicles using green technologies,
e.g., plug-in hybrid electric vehicles, electric vehicles, bicycles, and even unmanned aerial vehicles (drones).
The incorporation of these vehicles in T&L activities also raise some additional challenges from the strategic, planning, and operational perspectives (Juan et al., 2016).
For instance, transportation infrastructures are required to provide recharge stations for electric-based vehicles, meaning that investment decisions need to be made about the
number, location, and capacity of thesestations. Similarly, the limited driving-range capabilities of some vehicles impose non-trivial additional constraints when designing efficient
distribution routes (Juan et al., 2014).
This project aims at studying several open research challenges related to data analytics and optimization of real-life L&T activities. introduction in urban areas of electric vehicles in
T&L activities. Based on the previous ideas, we have settled the following global objectives for our project:
1. Identification: Diagnosis and statement of the main issues related to freight T&L activities in a European-scope enterprise, including aspects such as data analysis,
traffic management, environmental / social impact of T&L activities, etc.
2. Modeling: Development of theoretical models to solve real-life challenges, including: information extraction from available data, use of consolidation centers, shared vehicles
and parking areas, charging stations, heterogeneous fleets, effective truck loading and backhauling, location systems, etc.
3. Solving: Design and implementation of expert systems (based on metaheuristic algorithms), management- and location systems able to provide efficient solutions to the
main issues identified in the previous goals. The integration of these processes in the business dynamics will be also investigated.
4. Transfer: Knowledge transfer to real-life L&T practices in collaboration with the enterprise that supports this proposal.
REFERENCES
Toth, P., & Vigo, D. (2014). Vehicle Routing – Problems, Methods and Applications. (2nd ed.). SIAM – Society for Industrial and Applied Mathematics.
Juan, A. A., Goentzel, J., & Bektas, T. (2014). Routing fleets with multiple driving ranges: Is it possible to use greener fleet configurations? Applied Soft Computing, 21, 84–94
Juan, A. A., Mendez, C., Faulin, J., Armas, J., & Grasman, S. (2016). Electric Vehicles in Logistics and Transportation: a survey on emerging environmental, strategic,
and operational challenges. Energies, 9(86).
