Optimization Heuristics for the Combinatorial Auction Problem


Schwind, Michael ; Stockheim, Tim ; Rothlauf, Franz


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URL: https://ub-madoc.bib.uni-mannheim.de/90
URN: urn:nbn:de:bsz:180-madoc-906
Dokumenttyp: Arbeitspapier
Erscheinungsjahr: 2003
Titel einer Zeitschrift oder einer Reihe: Working papers
Band: 13
Ort der Veröffentlichung: Mannheim
Sprache der Veröffentlichung: Englisch
Einrichtung: Fakultät für Betriebswirtschaftslehre > Sonstige - Fakultät für Betriebswirtschaftslehre
MADOC-Schriftenreihe: Area Information Systems and Institute for Enterprise Systems > Working Papers Lehrstuhl für ABWL und Wirtschaftsinformatik (Heinzl) (bis 2011)
Fachgebiet: 004 Informatik
Normierte Schlagwörter (SWD): Algorithmus
Abstract: This paper presents and compares three heuristics for the combinatorial auction problem. Besides a simple greedy (SG) mechanism, two metaheuristics, a simulated annealing (SA), and a genetic algorithm (GA) approach are developed which use the combinatorial auction process to find an allocation with maximal revenue for the auctioneer. The performance of these three heuristics is evaluated in the context of a price controlled resource allocation process designed for the control and provision of distributed information services. Comparing the SG and SA method shows that depending on the problem structure the performance of the SA is up to 20% higher than the performance of the simple greedy allocation method. The proposed GA approach, using a random key encoding, results in a further improvement of the solution quality. Although the metaheuristic approaches result in higher search performance, the computational effort in terms of used CPU time is higher in comparison to the simple greedy mechanism. However, the absolute overall computation time is low enough to enable real-time execution in the considered IS application domain.
Übersetzung des Abstracts: This paper presents and compares three heuristics for the combinatorial auction problem. Besides a simple greedy (SG) mechanism, two metaheuristics, a simulated annealing (SA), and a genetic algorithm (GA) approach are developed which use the combinatorial auction process to find an allocation with maximal revenue for the auctioneer. The performance of these three heuristics is evaluated in the context of a price controlled resource allocation process designed for the control and provision of distributed information services. Comparing the SG and SA method shows that depending on the problem structure the performance of the SA is up to 20% higher than the performance of the simple greedy allocation method. The proposed GA approach, using a random key encoding, results in a further improvement of the solution quality. Although the metaheuristic approaches result in higher search performance, the computational effort in terms of used CPU time is higher in comparison to the simple greedy mechanism. However, the absolute overall computation time is low enough to enable real-time execution in the considered IS application domain. (Englisch)
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Schwind, Michael und Stockheim, Tim und Rothlauf, Franz (2003) Optimization Heuristics for the Combinatorial Auction Problem. Mannheim [Arbeitspapier]
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