Quick Minimization of Tardy Processing Time on a Single Machine

TL;DR

This paper presents an improved algorithm for minimizing total tardy processing time on a single machine, reducing computational complexity by introducing a job bundling technique and leveraging skewed-convolutions.

Contribution

The authors develop a faster algorithm for tardy processing time minimization by applying job bundling and advanced convolution techniques, improving previous time complexity bounds.

Findings

Achieved a $ ilde{O}(P^{7/5})$ time algorithm for the problem.

Introduced a job bundling technique to optimize computation.

Improved upon the previous $ ilde{O}(P^{5/3})$ algorithm.

Abstract

We consider the problem of minimizing the total processing time of tardy jobs on a single machine. This is a classical scheduling problem, first considered by [Lawler and Moore 1969], that also generalizes the Subset Sum problem. Recently, it was shown that this problem can be solved efficiently by computing $(\max,\min)$-skewed-convolutions. The running time of the resulting algorithm is equivalent, up to logarithmic factors, to the time it takes to compute a $(\max,\min)$-skewed-convolution of two vectors of integers whose sum is $O(P)$, where $P$ is the sum of the jobs' processing times. We further improve the running time of the minimum tardy processing time computation by introducing a job ``bundling'' technique and achieve a $\tilde{O}\left(P^{2-1/\alpha}\right)$ running time, where $\tilde{O}\left(P^\alpha\right)$ is the running time of a $(\max,\min)$-skewed-convolution of vectors of size $P$. This results in a $\tilde{O}\left(P^{7/5}\right)$ time algorithm for tardy processing time minimization, an improvement over the previously known $\tilde{O}\left(P^{5/3}\right)$ time algorithm.