Data science in battle: Applying graph theory to the Ukraine war
I) Introduction
In warfare, having the biggest, most well equipped military means nothing if you can’t bring those forces to bear where you need them. Further, they need to be supported by a logistics network that provides a constant flow of things like fuel, food, spare parts for the equipment etc¹. The science of establishing these supply chains is called logistics and the phrase ‘amateurs talk tactics, professionals talk logistics’ has been repeated to the point of cliche in the context of the war in Ukraine. The field of logistics is full of interesting algorithms from optimization to graph theory (ex: the transportation problem, shortest paths in graphs, etc.). Superposing the layer of combat on top adds many interesting complexities and this birthed the entire field of Operations Research during the Second World war.
While this article will delve into some toy problems that arise out of the logistics of warfare, there are many other ways machine learning, data science, etc. can play a role (and probably are).
- Leveraging game theory on the negotiating table.
- Using computer vision on satellite images to determine:
- When is a tank un-accompanied by air support? This could mean it’s time to send in a drone.
