Online Allocation Problem with Two-sided Resource Constraints

Online resource allocation is a prominent paradigm for sequential decision making during a finite horizon subject to the resource constraints, increasingly attracting the wide attention of researchers and practitioners in theoretical computer science (Mehta et al., 2007; Devanur and Jain, 2012; Devanur et al., 2019), operations research (Agrawal et al., 2014; Li and Ye, 2021) and machine learning communities (Balseiro et al., 2020; Li et al., 2020). In these settings, the requests arrive online and we need to serve each request via one of the available channels, which consumes a certain amount of resources and generates a corresponding service charge. The objective of the decision maker is to maximize the cumulative revenue subject to the resource capacity constraints. Such problem frequently appears in many applications including online advertising (Mehta et al., 2007; Buchbinder et al., 2007), online combinatorial auctions (Chawla et al., 2010), online linear programming(Agrawal et al., 2014; Buchbinder and Naor, 2009), online routing(Buchbinder and Naor, 2006), online multi-leg flight seats and hotel rooms allocation (Talluri et al., 2004), etc. The aforementioned online resource allocation framework only considers the capacity (upper bound) constraints for resources.