Updating Lower and Upper Bounds for the Job-Shop Scheduling Problem Test Instances
April 17, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Marc-Emmanuel Coupvent des Graviers, Lotfi Kobrosly, Christophe Guettier, Tristan Cazenave
arXiv ID
2504.16106
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.DM
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The Job-Shop Scheduling Problem (JSSP) and its variant, the Flexible Job-Shop Scheduling Problem (FJSSP), are combinatorial optimization problems studied thoroughly in the literature. Generally, the aim is to reduce the makespan of a scheduling solution corresponding to a problem instance. Thus, finding upper and lower bounds for an optimal makespan enables the assessment of performances for multiple approaches addressed so far. We use OR-Tools, a solver portfolio, to compute new bounds for some open benchmark instances, in order to reduce the gap between upper and lower bounds. We find new numerical lower bounds for multiple benchmark instances, up to closing the Taillard's ta33 instance. We also improve upper bounds for four instances, namely Taillard's ta26 & ta45 and Dauzere's 05a & 06a. Additionally we share an optimal solution for Taillard's ta45 as well as Hurink-edata's car5.
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