Toward a Unified Graph-Based Representation of Medical Data for Precision Oncology Medicine

We present a new unified graph-based representation of medical data, combining genetic information and medical records of patients with medical knowledge via a unique knowledge graph. This approach allows us to infer meaningful information and explanations that would be unavailable by looking at each data set separately. The systematic use of different databases, managed throughout the built knowledge graph, gives new insights toward a better understanding of oncology medicine. Indeed, we reduce some useful medical tasks to well-known problems in theoretical computer science for which efficient algorithms exist.

Multi-Depot Multi-Trip Vehicle Routing with Total Completion Time Minimization

Unmanned aerial vehicles (UAVs) are aircraft whose flights can be fully autonomous without any provision for human intervention. One of the most useful and promising domains where UAVs can be employed is natural disaster management. In this paper, we focus on an emergency scenario and propose the use of a fleet of UAVs that help rescue teams to individuate people needing help inside an affected area. We model this situation as an original graph theoretical problem called Multi-Depot Multi-Trip Vehicle Routing Problem with Total Completion Times minimization (MDMT-VRP-TCT); we go through some problems already studied in the literature that appear somehow similar to it and highlight the differences, propose a mathematical formulation for our problem as a MILP, design a matheuristic framework to quickly solve large instances, and experimentally test its performance. Beyond the proposed application, our solution works in any case in which a multi-depot multi-trip vehicle routing problem must be solved.