DISPATCH: An Optimally-Competitive Algorithm for Maximum Online Perfect Bipartite Matching with i.i.d. Arrivals

TL;DR

This paper introduces DISPATCH, an algorithm for online bipartite matching with i.i.d. arrivals, achieving the best possible 0.5 competitiveness, and maintains a uniform worker distribution despite non-uniform job type distributions.

Contribution

The paper presents DISPATCH, a 0.5-competitive, randomized algorithm for maximum online perfect bipartite matching with i.i.d. arrivals, proving optimality of this competitiveness.

Findings

DISPATCH is 0.5-competitive and optimal.

DISPATCH maintains a uniform worker distribution.

The algorithm adapts to non-uniform job type distributions.

Abstract

This work presents an optimally-competitive algorithm for the problem of maximum weighted online perfect bipartite matching with i.i.d. arrivals. In this problem, we are given a known set of workers, a distribution over job types, and non-negative utility weights for each pair of worker and job types. At each time step, a job is drawn i.i.d. from the distribution over job types. Upon arrival, the job must be irrevocably assigned to a worker and cannot be dropped. The goal is to maximize the expected sum of utilities after all jobs are assigned. We introduce DISPATCH, a 0.5-competitive, randomized algorithm. We also prove that 0.5-competitive is the best possible. DISPATCH first selects a "preferred worker" and assigns the job to this worker if it is available. The preferred worker is determined based on an optimal solution to a fractional transportation problem. If the preferred worker is not available, DISPATCH randomly selects a worker from the available workers. We show that DISPATCH maintains a uniform distribution over the workers even when the distribution over the job types is non-uniform.