Using Data to Allocate Resources Efficiently In city logistics systems, a fleet of vehicles is divided between service regions that function autonomously. Each region finds optimal routes for its own fleet and incurs costs accordingly. More vehicles lead to lower costs, but the trade-off is that fewer vehicles are left for other regions. Costs are difficult to quantify precisely because of demand uncertainty but can be estimated using data. The paper “Data-driven robust resource allocation with monotonic cost functions” by Chen, Marković, Ryzhov, and Schonfeld develops a principled risk-averse approach for two-stage resource allocation. The authors propose a new uncertainty model for decreasing cost functions and show how it can be leveraged to efficiently find resource allocations that demonstrably reduce the frequency of high-cost scenarios. This framework combines statistics and optimization in a novel way and is applicable to a general class of resource allocation problems, encompassing facility location, vehicle routing, and discrete-event simulation.
A faster algorithm for the resource allocation problem with convex cost functions
