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Online resource allocation under horizon uncertainty
Santiago Balseiro, Christian Kroer, Rachitesh Kumar
Columbia University, United States of America
We study stochastic online resource allocation: a decision maker needs to allocate limited resources to i.i.d. sequential requests generated from an unknown distribution in order to maximize reward. Online resource allocation has been studied extensively in the past, but prior results crucially rely on the assumption that the total number of requests (the horizon) is known to the decision maker in advance. In this work, we develop online algorithms that are robust to horizon uncertainty.
Online advance reservation with multi-class arrivals
Tianming Huo, Wang Chi Cheung
National University of Singapore, Singapore
We consider an online advance reservation problem under customer heterogeneity. The decision maker decides on admitting a customer based on the customer's class and the current system information. Our problem model captures applications in medical appointment and hotel room booking, where a resources are to be scheduled for customers' uses in advance. We derive a novel rejection price based algorithm, and we quantify the theoretical guarantee of our algorithm in terms of its competitive ratio.
Tractable budget allocation strategies for multichannel ad campaigns
