Online Resource Allocation Under Partially Predictable Demand

66 Pages Posted: 12 Oct 2018

See all articles by Dawsen Hwang

Dawsen Hwang

Google Inc.

Patrick Jaillet

Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science

Vahideh Manshadi

Yale School of Management

Date Written: September 20, 2018


For online resource allocation problems, we propose a new demand arrival model where the sequence of arrivals contains both an adversarial component and a stochastic one. Our model requires no demand forecasting; however, due to the presence of the stochastic component, we can partially predict future demand as the sequence of arrivals unfolds. Under the proposed model, we study the problem of the online allocation of a single resource to two types of customers, and design online algorithms that outperform existing ones. Our algorithms are adjustable to the relative size of the stochastic component, and our analysis reveals that as the portion of the stochastic component grows, the loss due to making online decisions decreases. This highlights the value of (even partial) predictability in online resource allocation. We impose no conditions on how the resource capacity scales with the maximum number of customers. However, we show that using an adaptive algorithm — which makes online decisions based on observed data — is particularly beneficial when capacity scales linearly with the number of customers. Our work serves as a first step in bridging the long-standing gap between the two well-studied approaches to the design and analysis of online algorithms based on (1) adversarial models and (2) stochastic ones. Using novel algorithm design, we demonstrate that even if the arrival sequence contains an adversarial component, we can take advantage of the limited information that the data reveals to improve allocation decisions. We also study the classical secretary problem under our proposed arrival model, and we show that randomizing over multiple stopping rules may increase the probability of success.

Keywords: online resource allocation, competitive analysis, analysis of algorithms

Suggested Citation

Hwang, Dawsen and Jaillet, Patrick and Manshadi, Vahideh, Online Resource Allocation Under Partially Predictable Demand (September 20, 2018). Available at SSRN: or

Dawsen Hwang

Google Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

Patrick Jaillet

Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science ( email )

77 Massachusetts Avenue
Cambridge, MA 02139-4307
United States

Vahideh Manshadi (Contact Author)

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

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