Adaptive Online Resource Allocation Schemes under General Non-Stationary Arrivals

57 Pages Posted: 24 Feb 2025 Last revised: 28 Feb 2025

See all articles by Zhuoru Li

Zhuoru Li

Fudan University

Xiaoyue Zhang

National University of Singapore (NUS) - Institute for Operations Research and Analytics

Hanzhang Qin

National University of Singapore (NUS)

Mabel Chou

National University of Singapore (NUS) - Department of Decision Sciences

Date Written: January 24, 2025

Abstract

We propose a novel first-order method for online resource allocation under a non-stationary arrival process and unknown demands. We assume multiple types of customers arrive in a nonstationary stochastic fashion, with unknown arrival rates in each period. It is also assumed that customers' click-through rates are unknown and can only be learned online. By leveraging results from the stochastic contextual bandit with knapsack and online matching with adversarial arrivals, we develop an online scheme to allocate the resources to nonstationary customers, which is adaptive to the non-stationarity of the customer arrival process. We prove that under mild conditions, our scheme enjoys a "best-of-both-world" guarantee: the scheme has a sublinear regret when the customer arrivals are near-stationary, and has an optimal competitive ratio under general (non-stationary) customer arrival distributions. Finally, we conduct extensive numerical experiments to show that our approach generates near-optimal revenues under general non-stationary arrivals. 

Suggested Citation

Li, Zhuoru and Zhang, Xiaoyue and Qin, Hanzhang and Chou, Mabel, Adaptive Online Resource Allocation Schemes under General Non-Stationary Arrivals (January 24, 2025). Available at SSRN: https://ssrn.com/abstract=5112437 or http://dx.doi.org/10.2139/ssrn.5112437

Zhuoru Li

Fudan University ( email )

Xiaoyue Zhang

National University of Singapore (NUS) - Institute for Operations Research and Analytics ( email )

Singapore

Hanzhang Qin (Contact Author)

National University of Singapore (NUS) ( email )

1E Kent Ridge Road
NUHS Tower Block Level 7
Singapore, 119228
Singapore

Mabel Chou

National University of Singapore (NUS) - Department of Decision Sciences ( email )

NUS Business School
BIZ 1 Building, #02-01, 1 Business Link
117592
Singapore

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