Scheduling Problems with Constrained Rejections

TL;DR

This paper explores bicriteria scheduling problems allowing limited rejections, improving approximation ratios for makespan minimization and establishing NP-hardness for a related Santa Claus problem, introducing a bicriteria Set Packing variant.

Contribution

It presents improved approximation algorithms for makespan scheduling with rejections and establishes NP-hardness results for Santa Claus with rejections, introducing a novel bicriteria Set Packing problem.

Findings

Improved the job scheduling ratio to 0.6533 with a 1.5 times makespan.

Proved NP-hardness for a Santa Claus variant with rejections.

Introduced a bicriteria Set Packing problem with algorithmic and hardness results.

Abstract

We study bicriteria versions of Makespan Minimization on Unrelated Machines and Santa Claus by allowing a constrained number of rejections. Given an instance of Makespan Minimization on Unrelated Machines where the optimal makespan for scheduling $n$ jobs on $m$ unrelated machines is $T$, (Feige and Vondr\'ak, 2006) gave an algorithm that schedules a $(1-1/e+10^{-180})$ fraction of jobs in time $T$. We show the ratio can be improved to $0.6533>1-1/e+0.02$ if we allow makespan $3T/2$. To the best our knowledge, this is the first result examining the tradeoff between makespan and the fraction of scheduled jobs when the makespan is not $T$ or $2T$. For the Santa Claus problem (the Max-Min version of Makespan Minimization), the analogous bicriteria objective was studied by (Golovin, 2005), who gave an algorithm providing an allocation so a $(1-1/k)$ fraction of agents receive value at least $T/k$, for any $k \in \mathbb{Z}^+$ and $T$ being the optimal minimum value every agent can receive. We provide the first hardness result by showing there are constants $\delta,\varepsilon>0$ such that it is NP-hard to find an allocation where a $(1-\delta)$ fraction of agents receive value at least $(1-\varepsilon) T$. To prove this hardness result, we introduce a bicriteria version of Set Packing, which may be of independent interest, and prove some algorithmic and hardness results for it. Overall, we believe these bicriteria scheduling problems warrant further study as they provide an interesting lens to understand how robust the difficulty of the original optimization goal might be.