Sample-Based Online Generalized Assignment Problem with Unknown Poisson Arrivals
Zihao Li, Hao Wang, Zhenzhen Yan

TL;DR
This paper introduces a sample-based online algorithm for the generalized assignment problem with unknown Poisson arrivals, leveraging historical data to improve decision-making and providing performance guarantees.
Contribution
It develops a multi-phase algorithm that combines offline data and online observations, extending to multidimensional demands and analyzing the impact of data size and dimensions.
Findings
Performance depends on historical data size and arrival rates.
The algorithm achieves a provable competitive ratio.
Numerical experiments confirm effectiveness.
Abstract
We study an edge-weighted online stochastic \emph{Generalized Assignment Problem} with \emph{unknown} Poisson arrivals. In this model, we consider a bipartite graph that contains offline bins and online items, where each offline bin is associated with a -dimensional capacity vector and each online item is with a -dimensional demand vector. Online arrivals are sampled from a set of online item types which follow independent but not necessarily identical Poisson processes. The arrival rate for each Poisson process is unknown. Each online item will either be packed into an offline bin which will deduct the allocated bin's capacity vector and generate a reward, or be rejected. The decision should be made immediately and irrevocably upon its arrival. Our goal is to maximize the total reward of the allocation without violating the capacity constraints. We provide a sample-based…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOptimization and Search Problems · Transportation and Mobility Innovations · Auction Theory and Applications
