Resource Sharing Through Multi-Round Matchings

TL;DR

This paper studies multi-round matching problems in resource sharing scenarios, providing efficient algorithms for certain cases, and addressing NP-hard extensions with heuristics, validated through experiments on real-world applications.

Contribution

It introduces a framework for multi-round matchings with benefit maximization and advice generation, including efficient solutions and heuristics for NP-hard cases.

Findings

Efficient algorithms for multi-round matchings with certain benefit functions.

NP-hardness results for general benefit maximization and advice generation.

Successful application to real-world resource sharing scenarios.

Abstract

Applications such as employees sharing office spaces over a workweek can be modeled as problems where agents are matched to resources over multiple rounds. Agents' requirements limit the set of compatible resources and the rounds in which they want to be matched. Viewing such an application as a multi-round matching problem on a bipartite compatibility graph between agents and resources, we show that a solution (i.e., a set of matchings, with one matching per round) can be found efficiently if one exists. To cope with situations where a solution does not exist, we consider two extensions. In the first extension, a benefit function is defined for each agent and the objective is to find a multi-round matching to maximize the total benefit. For a general class of benefit functions satisfying certain properties (including diminishing returns), we show that this multi-round matching problem is efficiently solvable. This class includes utilitarian and Rawlsian welfare functions. For another benefit function, we show that the maximization problem is NP-hard. In the second extension, the objective is to generate advice to each agent (i.e., a subset of requirements to be relaxed) subject to a budget constraint so that the agent can be matched. We show that this budget-constrained advice generation problem is NP-hard. For this problem, we develop an integer linear programming formulation as well as a heuristic based on local search. We experimentally evaluate our algorithms on synthetic networks and apply them to two real-world situations: shared office spaces and matching courses to classrooms.