Online and semi-online scheduling on two hierarchical machines with a common due date to maximize the total early work
September 19, 2022 Β· Declared Dead Β· π International Journal of Applied Mathematics and Computer Sciences
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Man Xiao, Xiaoqiao Liu, Weidong Li, Xin Chen, Malgorzata Sterna, Jacek Blazewicz
arXiv ID
2209.08704
Category
cs.DS: Data Structures & Algorithms
Cross-listed
math.OC
Citations
6
Venue
International Journal of Applied Mathematics and Computer Sciences
Last Checked
4 months ago
Abstract
In this study, we investigated several online and semi-online scheduling problems on two hierarchical machines with a common due date to maximize the total early work. For the pure online case, we designed an optimal online algorithm with a competitive ratio of $\sqrt 2$. For the case when the total processing time is known, we proposed an optimal semi-online algorithm with a competitive ratio of $\frac{4}{3}$. Additionally, for the cases when the largest processing time is known, we gave optimal algorithms with a competitive ratio of $\frac{6}{5}$ if the largest job is a lower hierarchy one, and of $\sqrt 5-1$ if the largest job is a higher hierarchy one, respectively.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted