Lazy Local Search Meets Machine Scheduling

TL;DR

This paper advances approximation algorithms for the restricted unrelated parallel machine scheduling problem, achieving a guarantee close to 1.866 in specific cases, improving upon prior bounds and leveraging insights from Configuration LPs.

Contribution

The authors develop a new approximation algorithm with a guarantee approaching 1.866 for a special case of the scheduling problem, improving previous bounds.

Findings

Achieves approximation ratio near 1.866 as   0

Improves upon previous 2- guarantees for certain job processing times

Utilizes Configuration LP insights to enhance approximation bounds

Abstract

We study the restricted case of Scheduling on Unrelated Parallel Machines. In this problem, we are given a set of jobs $J$ with processing times $p_j$ and each job may be scheduled only on some subset of machines $S_j \subseteq M$. The goal is to find an assignment of jobs to machines to minimize the time by which all jobs can be processed. In a seminal paper, Lenstra, Shmoys, and Tardos designed an elegant $2$-approximation for the problem in 1987. The question of whether approximation algorithms with better guarantees exist for this classic scheduling problem has since remained a source of mystery. In recent years, with the improvement of our understanding of Configuration LPs, it now appears an attainable goal to design such an algorithm. Our main contribution is to make progress towards this goal. When the processing times of jobs are either $1$ or $\epsilon \in (0,1)$, we design an approximation algorithm whose guarantee tends to $1 + \sqrt{3}/2 \approx 1.8660254,$ for the interesting cases when $\epsilon \to 0$. This improves on the $2-\epsilon_0$ guarantee recently obtained by Chakrabarty, Khanna, and Li for some constant $\epsilon_0 > 0$.