Name
Abstract | ||
|---|---|---|
One of the biggest challenges in the design of real-world decision support systems is coming up with a good combinatorial optimization model. Often enough, accurate predictive models (e.g. simulators) can be devised, but they are too complex or too slow to be employed in combinatorial optimization. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.artint.2016.01.005 | Artificial Intelligence |
Keywords | Field | DocType |
Combinatorial optimization,Machine learning,Complex systems,Local search,Constraint programming,Mixed integer non-linear programming,SAT modulo theories,Artificial neural networks,Decision trees | Decision tree,Computer science,Decision support system,Constraint programming,Combinatorial optimization,Decision model,Artificial intelligence,Local search (optimization),Prescriptive analytics,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
244 | 1 | 0004-3702 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michele Lombardi | 1 | 270 | 28.86 |
| Michela Milano | 2 | 1117 | 97.67 |
| Andrea Bartolini | 3 | 457 | 51.90 |