Name
Abstract | ||
|---|---|---|
This paper shows how Benders decomposition can be used for estimating the parameters of a fatigue model. The objective function
of such model depends on five parameters of different nature. This makes the parameter estimation problem of the fatigue model
suitable for the Benders decomposition, which allows us to use well-behaved and robust parameter estimation methods for the
different subproblems. To build the Benders cuts, explicit formulas for the sensitivities (partial derivatives) are obtained.
This permits building the classical iterative method, in which upper and lower bounds of the optimal value of the objective
function are obtained until convergence. Two alternative objective functions to be optimized are the likelihood and the sum
of squares error functions, which relate to the maximum likelihood and the minimum error principles, respectively. The method
is illustrated by its application to a real-world problem. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10479-011-0891-6 | Annals OR |
Keywords | Field | DocType |
Linear optimization,Least-squares,Maximum likelihood,Sensitivity analysis,Benders’ decomposition,Fatigue | Convergence (routing),Least squares,Mathematical optimization,Upper and lower bounds,Iterative method,Partial derivative,Linear programming,Estimation theory,Explained sum of squares,Mathematics | Journal |
Volume | Issue | ISSN |
210 | 1 | 0254-5330 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique Castillo | 1 | 555 | 59.86 |
| Roberto Mínguez | 2 | 43 | 9.56 |
| Antonio Conejo | 3 | 189 | 24.33 |
| Beatriz Pérez-Sánchez | 4 | 95 | 14.03 |
| Oscar Fontenla-Romero | 5 | 337 | 39.49 |