Two-Stage ML-Guided Decision Rules for Sequential Decision Making under Uncertainty

arXiv:2405.14973v1 Announce Type: new
Abstract: Sequential Decision Making under Uncertainty (SDMU) is ubiquitous in many domains such as energy, finance, and supply chains. Some SDMU applications are naturally modeled as Multistage Stochastic Optimization Problems (MSPs), but the resulting optimizations are notoriously challenging from a computational standpoint. Under assumptions of convexity and stage-wise independence of the uncertainty, the resulting optimization can be solved efficiently using Stochastic Dual Dynamic Programming (SDDP). Two-stage Linear Decision Rules (TS-LDRs) have been proposed to solve MSPs …

abstract applications arxiv assumptions computational cs.lg decision decision making domains energy finance making math.oc optimization rules stage stochastic supply chains type uncertainty