A Polynomial Time Constant Approximation For Minimizing Total Weighted Flow-time

TL;DR

This paper presents a polynomial-time algorithm that approximates the minimum total weighted flow-time on a single machine within a constant factor, solving a long-standing open problem in scheduling theory.

Contribution

It transforms pseudo-polynomial algorithms into polynomial algorithms, establishing the first polynomial-time constant approximation for min-WPFT.

Findings

Established a polynomial-time constant approximation algorithm for min-WPFT.

Resolved the open conjecture of existence of such an algorithm.

Builds on recent pseudo-polynomial algorithms to achieve polynomial complexity.

Abstract

We consider the classic scheduling problem of minimizing the total weighted flow-time on a single machine (min-WPFT), when preemption is allowed. In this problem, we are given a set of $n$ jobs, each job having a release time $r_j$, a processing time $p_j$, and a weight $w_j$. The flow-time of a job is defined as the amount of time the job spends in the system before it completes; that is, $F_j = C_j - r_j$, where $C_j$ is the completion time of job. The objective is to minimize the total weighted flow-time of jobs. This NP-hard problem has been studied quite extensively for decades. In a recent breakthrough, Batra, Garg, and Kumar presented a {\em pseudo-polynomial} time algorithm that has an $O(1)$ approximation ratio. The design of a truly polynomial time algorithm, however, remained an open problem. In this paper, we show a transformation from pseudo-polynomial time algorithms to polynomial time algorithms in the context of min-WPFT. Our result combined with the result of Batra, Garg, and Kumar settles the long standing conjecture that there is a polynomial time algorithm with $O(1)$-approximation for min-WPFT.