A Unified Model for the Two-stage Offline-then-Online Resource Allocation

TL;DR

This paper introduces a unified model for two-stage resource allocation combining offline and online phases, leveraging known arrival distributions and LP-based algorithms to improve robustness and effectiveness in real-world applications.

Contribution

It proposes a novel unified framework for two-stage resource allocation and develops an LP-based algorithm with provable approximation guarantees.

Findings

LP-based approach achieves at most 1/4 of optimal performance

Outperforms heuristics in robustness and effectiveness

Effective on real datasets

Abstract

With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decision-making requirement upon the arrival of each online agent. Both offline and online resource allocation have wide applications in various real-world matching markets ranging from ridesharing to crowdsourcing. There are some emerging applications such as rebalancing in bike sharing and trip-vehicle dispatching in ridesharing, which involve a two-stage resource allocation process. The process consists of an offline phase and another sequential online phase, and both phases compete for the same set of resources. In this paper, we propose a unified model which incorporates both offline and online resource allocation into a single framework. Our model assumes non-uniform and known arrival distributions for online agents in the second online phase, which can be learned from historical data. We propose a parameterized linear programming (LP)-based algorithm, which is shown to be at most a constant factor of $1/4$ from the optimal. Experimental results on the real dataset show that our LP-based approaches outperform the LP-agnostic heuristics in terms of robustness and effectiveness.