Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis.

Dynamic resource allocation problems arise under a variety of settings. In "Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis," Balseiro, Besbes, and Pizarro introduce a unifying model for a large class of dynamic optimization problems dubbed dynamic resource-constrained reward collection (DRC²) problems. Surveying the literature, they show that this class encompasses a variety of disparate and classical problems typically studied separately, such as dynamic pricing with capacity constraints, dynamic bidding with budgets, network revenue management, online matching, or order fulfillment. Furthermore, they establish that the DRC² class is amenable to analysis by characterizing the performance of a central, certainty-equivalent heuristic. Notably, they provide a novel unifying analysis that isolates the drivers of performance, recovers as corollaries some existing specialized results, generalizes other existing results by weakening the assumptions required, and yields new results in specialized settings for which no such characterization was available. Dynamic resource allocation problems arise under a variety of settings and have been studied across disciplines such as operations research and computer science. The present paper introduces a unifying model for a very large class of dynamic optimization problems that we call dynamic resource-constrained reward collection (DRC²) problems. We show that this class encompasses a variety of disparate and classical dynamic optimization problems such as dynamic pricing with capacity constraints, dynamic bidding with budgets, network revenue management, online matching, and order fulfillment, to name a few. Furthermore, we establish that the class of DRC² problems, although highly general, is amenable to analysis. In particular, we characterize the performance of the fluid certainty-equivalent control heuristic for this class. Notably, this very general result recovers as corollaries some existing specialized results, generalizes other existing results by weakening the assumptions required, and also yields new results in specialized settings for which no such characterization was available. As such, the DRC² class isolates some common features of a broad class of problems and offers a new object of analysis. Funding: The work of D. Pizarro was supported by the Artificial and Natural Intelligence Toulouse Institute, which is funded by the French "Investing for the Future—PIA3" program [Grant ANR-19-P3IA-0004]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2441. [ABSTRACT FROM AUTHOR]