Greedy Algorithm for Multiway Matching with Bounded Regret

Abstract

A Unified View of Online Matching and Resource Allocation Problems Whereas small regret online algorithms for applications as diverse as network revenue management (NRM), assemble-to-order (ATO) systems, and online stochastic bin packing (SBP) are known in the literature, the design of the existing algorithms are tailored to the specific application and often use the strategy of resolving a planning linear program. In the paper “Greedy Algorithm for Multiway Matching with Bounded Regret,” Gupta proposes a unified model for studying such online matching/allocation problems. In the unified model, resources of three types—off-line (e.g., inventory in NRM), online-queueable (e.g., orders or resources in ATO systems), or online-nonqueueable (e.g., requests in NRM, items in SBP)—must be combined to create feasible configurations. Leveraging the unified framework, the author gives one simple greedy algorithm that gives small regret (bounded or logarithmic in time horizon) for these diverse applications under a mild nondegeneracy condition on the off-line planning problem.