Improved approximation schemes for early work scheduling on identical parallel machines with common due date
July 24, 2020 Β· Declared Dead Β· π Journal of the Operations Research Society of China
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Weidong Li
arXiv ID
2007.12388
Category
cs.DS: Data Structures & Algorithms
Citations
12
Venue
Journal of the Operations Research Society of China
Last Checked
3 months ago
Abstract
We study the early work scheduling problem on identical parallel machines in order to maximize the total early work, i.e., the parts of non-preemptive jobs executed before a common due date. By preprocessing and constructing an auxiliary instance which has several good properties, we propose an efficient polynomial time approximation scheme with running time $O(n)$, which improves the result in [GyΓΆrgyi, P., Kis, T. (2020). A common approximation framework for early work, late work, and resource leveling problems. {\it European Journal of Operational Research}, 286(1), 129-137], and a fully polynomial time approximation scheme with running time $O(n)$ when the number of machines is a fixed number, which improves the result in [Chen, X., Liang, Y., Sterna, M., Wang, W., BΕaΕΌewicz, J. (2020b). Fully polynomial time approximation scheme to maximize early work on parallel machines with common due date. {\it European Journal of Operational Research}, 284(1), 67-74], where $n$ is the number of jobs, and the hidden constant depends on the desired accuracy.
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