Name
Abstract | ||
|---|---|---|
•A widely applicable approach to accelerate the branch-and-price algorithm.•Utilizing the knowledge gained from previous executions of the pricing problem.•A machine learning method predicting a tight upper bound for the pricing problem.•The exactness of the branch-and-price algorithm is preserved.•The runtime reduction of the branch-and-price algorithm by dozens of percentage points. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ejor.2018.05.046 | European Journal of Operational Research |
Keywords | Field | DocType |
Scheduling,Branch-and-price,Pricing problem,Machine learning,Wpper bound | Online machine learning,Mathematical optimization,Central processing unit,Feature selection,Upper and lower bounds,Scheduling (computing),CPU time,Branch and price,Algorithm,Mathematics,Computation | Journal |
Volume | Issue | ISSN |
271 | 3 | 0377-2217 |
Citations | PageRank | References |
1 | 0.34 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roman Václavík | 1 | 1 | 0.34 |
| Antonin Novak | 2 | 2 | 1.71 |
| sůcha přemysl | 3 | 74 | 13.96 |
| hanzalek zdeněk | 4 | 101 | 22.42 |