That's a clarifying response: I've applied (non)convex programming (LP, QP, MIP, etc) to all the above and a few more, all of which i'd classify as classical applications. Less traditional applications -- I'd like to explore these more --- include data envelopment analysis, which provides a framework for assessing the efficiency of processes based on 1 or more input metrics, and several ideas in papers published at NeurIPS and other conferences that integrate LPs into neural networks in various ways, including AUC maximization. I've also worked on first order methods to solve LPs, and while I'd like to continue in that direction, the area is very crowded with very good existing tools, new and emerging tools that are also often good, and very strong teams building on all of the above. One of the biggest challenges that I see in the OR space is that it requires human expertise to leverage the technology.

In a professional context, I use it to help a client (chemicals company) optimize their deliveries to customers. They have > 100 production sites and thousands of customers, so LP is used to allocate customers to production sites based on the product-cost and availability at supply site and trucking costs. We have evaluated multiple solvers (Gurobi/ Llamasoft/ GAMS/ LocalSolver etc) for optimizing deliveries, as well as evaluating the cost impact of changes to the delivery network.