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Riedler, M., Ruthmair, M., & Raidl, G. (2019). Strategies for Iteratively Refining Layered Graph Models. In Hybrid Metaheuristics: 11th International Workshop (pp. 46–62). Lecture Notes in Computer Science. http://hdl.handle.net/20.500.12708/57817
E192-01 - Forschungsbereich Algorithms and Complexity
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Erschienen in:
Hybrid Metaheuristics: 11th International Workshop
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Datum (veröffentlicht):
2019
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Veranstaltungsname:
International Workshop on Hybrid Metaheuristics (HM)
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Veranstaltungszeitraum:
13-Okt-2006 - 15-Okt-2006
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Veranstaltungsort:
Gran Canaria, Spanien
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Umfang:
17
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Verlag:
Lecture Notes in Computer Science, 11299
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Peer Reviewed:
Ja
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Abstract:
We consider a framework for obtaining a sequence of converging primal and dual bounds based on mixed integer linear programming formulations on layered graphs. The proposed iterative algorithm avoids the typically rather large size of the full layered graph by approximating it incrementally. We focus in particular on this refinement step that extends the graph in each iteration. Novel path-based a...
We consider a framework for obtaining a sequence of converging primal and dual bounds based on mixed integer linear programming formulations on layered graphs. The proposed iterative algorithm avoids the typically rather large size of the full layered graph by approximating it incrementally. We focus in particular on this refinement step that extends the graph in each iteration. Novel path-based approaches are compared to existing variants from the literature. Experiments on two benchmark problems-the traveling salesman problem with time windows and the rooted distance-constrained minimum spanning tree problem-show the effectiveness of our new strategies. Moreover, we investigate the impact of a strong heuristic component within the algorithm, both for improving convergence speed and for improving the potential of an employed reduced cost fixing step.
