Single Machine Scheduling with Few Deadlines

Abstract We study single-machine scheduling problems with few deadlines. We focus on three classical objectives, namely minimizing the weighted number of tardy jobs, the total weighted completion time, and the total weighted late work. For each of the three problems, we give a pseudo-polynomial-time algorithm for a constant number of different deadlines. These algorithms are complemented with ETH-based, almost tight lower bounds. Furthermore, we study the case in which the number of jobs with nontrivial deadline (i.e. jobs with deadline smaller than the total processing time of all jobs) is taken as a parameter. For this case, the complexities of our three problems differ: Minimizing the weighted number of tardy jobs and minimizing the total weighted late work becomes fixed-parameter tractable, while minimizing the total weighted completion time is W[1]-hard.

Authors

Institutions

Publication Details

Journal
Algorithmica
Published
2026-11-06
DOI
https://doi.org/10.1007/s00453-026-01414-3
Primary Topic
Scheduling and Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Single Machine Scheduling with Few Deadlines

Dvir Shabtay, Danny Hermelin, Klaus Heeger
1 links
Algorithmica
Scheduling and Optimization Algorithms
article

Single Machine Scheduling with Few Deadlines

Dvir Shabtay, Danny Hermelin, Klaus Heeger
article en

Abstract

Abstract We study single-machine scheduling problems with few deadlines. We focus on three classical objectives, namely minimizing the weighted number of tardy jobs, the total weighted completion time, and the total weighted late work. For each of the three problems, we give a pseudo-polynomial-time algorithm for a constant number of different deadlines. These algorithms are complemented with ETH-based, almost tight lower bounds. Furthermore, we study the case in which the number of jobs with nontrivial deadline (i.e. jobs with deadline smaller than the total processing time of all jobs) is taken as a parameter. For this case, the complexities of our three problems differ: Minimizing the weighted number of tardy jobs and minimizing the total weighted late work becomes fixed-parameter tractable, while minimizing the total weighted completion time is W[1]-hard.

AlgorithmicaVol. 89(1)
Ben-Gurion University of the Negev (IL)
Israel Science Foundation, Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz
Openalex Percentile: Top 19%
Scheduling and Optimization Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon