Stochastic integer programming

One of the main areas of research at the Center for Shipping and Logistics in general is stochastic integer programming, with an emphasis on modelling and model understanding for logistics related problems, in addition to research on modeling and representing the uncertainty in optimization models (often referred to as scenario generation).

Presently we have activities related to network design, facility location, fish farming, evacuation modeling, and vehicle routing.

Collaborative logistics

Collaborative logistics is becoming more relevant nowadays, because of its positive impact in economic and environmental efficiency. Our work in this area primarily focuses on collaborative transportation planning, with emphasis on natural resources.

Game theory

Linked to our work in collaborative logistics, we use concepts from game theory to address questions in coalition formation, stability and cost sharing.

Scheduling

Our interest in scheduling covers a broad range of techniques and applications. This interest is primarily motivated from activities related to the shipping industry and activities related to natural resources. In a more general scope, our work in scheduling also covers activities in other contexts, such as in sports competitions.

Statistical modelling and data analysis

Statistical modelling and data analysis has always been relevant in logistics and shipping. Nowadays, specially due to improved technology, using big data bases and statistical tools help to explain and predict phenomena and also to support decision making. Our work in this area includes topics related to speed optimization, bunker prices, energy costs and freight rates.