Scheduling with Gaps: New Models and Algorithms

TL;DR

This paper introduces new models and algorithms for scheduling unit jobs with gaps, considering various objectives and constraints, and provides polynomial-time solutions for these complex scheduling problems.

Contribution

It initiates the study of scheduling problems with gap-based performance metrics and offers polynomial algorithms for multiple variants.

Findings

Polynomial algorithms for gap scheduling problems

Connections to other algorithmic areas like Monge arrays

Tradeoffs between gaps and flow time analyzed

Abstract

We consider scheduling problems for unit jobs with release times, where the number or size of the gaps in the schedule is taken into consideration, either in the objective function or as a constraint. Except for a few papers on energy minimization, there is no work in the scheduling literature that uses performance metrics depending on the gap structure of a schedule. One of our objectives is to initiate the study of such scheduling problems with gaps. We show that such problems often lead to interesting algorithmic problems, with connections to other areas of algorithmics. We focus on the model with unit jobs. First we examine scheduling problems with deadlines, where we consider variants of minimum-gap scheduling, including maximizing throughput with a budget for gaps or minimizing the number of gaps with a throughput requirement. We then turn to other objective functions. For example, in some scenarios, gaps in a schedule may be actually desirable, leading to the problem of maximizing the number of gaps. Other versions we study include minimizing maximum gap or maximizing minimum gap. The second part of the paper examines the model without deadlines, where we focus on the tradeoff between the number of gaps and the total or maximum flow time. For all these problems we provide polynomial time algorithms, with running times ranging from $O(n \log n)$ for some problems, to $O(n^7)$ for other. The solutions involve a spectrum of algorithmic techniques, including different dynamic programming formulations, speed-up techniques based on searching Monge arrays, searching X + Y matrices, or implicit binary search.