Effective search space control for large and/or complex driver scheduling problems
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| Publicado en: | Annals of Operations Research vol. 155, no. 1 (Nov 2007), p. 417-435 |
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| Publicado: |
Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| Resumen: | For real life bus and train driver scheduling instances, the number of columns in terms of the set covering/partitioning ILP model could run into billions making the problem very difficult. Column generation approaches have the drawback that the sub-problems for generating the columns would be computationally expensive in such situations. This paper proposes a hybrid solution method, called PowerSolver, of using an iterative heuristic to derive a series of small refined sub-problem instances fed into an existing efficient set covering ILP based solver. In each iteration, the minimum set of relief opportunities that guarantees a solution no worse than the current best is retained. Controlled by a user-defined strategy, a small number of the banned relief opportunities would be reactivated and some soft constraints may be relaxed before the new sub-problem instance is solved. PowerSolver is proving successful by many transport operators who are now routinely using it. Test results from some large scale real-life exercises will be reported. [PUBLICATION ABSTRACT] |
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| ISSN: | 0254-5330 1572-9338 |
| DOI: | 10.1007/s10479-007-0203-3 |
| Fuente: | ABI/INFORM Global |