Abstract
We consider the problem of finding maximal flows with respect to capacities which are linear functions of a parametert ∈ [0,T]. Since this problem is a special case of a parametric linear program the classichorizontal approach can be applied in which optimal solutions are computed for successive subintervals of [0,T]. We discuss an alternative algorithm which approximates in each iteration the optimal solution for allt ∈ [0,T]. Thisvertical algorithm is a labeling type algorithm where the flow variables are piecewise linear functions. Flow augmentations are done alongconditional flow augmenting paths which can be found by modified path algorithms. The vertical algorithm can be used to solve the parametric flow problem optimally as well as to compute a good approximation for allt if the computation of the optimal solution turns out to be too time consuming.
Zusammenfassung
Wir betrachten maximale Flußprobleme, in denen die Kapazitäten lineare Funktionen eines Parameterst ∈ [0,T] sind. Da dieses Problem ein Spezialfall eines parametrischen linearen Programms ist, kann man den klassischen horizontalen Ansatz anwenden, mit dem optimale Lösungen sukzessive auf Teilintervallen von [0,T] bestimmt werden. Wir stellen einen alternativen Algorithmus vor, der in jeder Iteration die optimale Lösung für allet ∈ [0,T] approximiert. Dieser vertikalen Ansatz ist eine Art Markierungsalgorithmus, wobei die Flußvariablen stückweise lineare Funktionen sind. Flußvergrößerungen werden aufbedingten flußvergrößernden Wegen durchgeführt, die mittels modifizierter kürzester Wege Algorithmen gefunden werden können. Der vertikale Algorithmus kann sowohl zur Berechnung des optimalen parametrischen Flusses als auch zur Berechnung einer guten Approximation für allet benutzt werden, falls sich herausstellt, daß die Berechnung der optimalen Lösung zu zeitaufwendig ist.
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Partially supported by NSF Grants ECS-8412926 and INT-8521433, and NATO Grant RG 85/0240.
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Hamacher, H.W., Foulds, L.R. Algorithms for flows with parametric capacities. ZOR - Methods and Models of Operations Research 33, 21–37 (1989). https://doi.org/10.1007/BF01415514
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DOI: https://doi.org/10.1007/BF01415514