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An Ant Colony Approach for the Winner Determination Problem

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10782))

Abstract

Combinatorial auctions are those where bidders can bid on bundles of items. These auctions can lead to more economically efficient allocations but determining the winners is an NP-complete problem. In this paper, we propose an ant colony technique for approximating solutions to hard instances of this problem. Hard instances are those that are unsolvable within reasonable time by CPLEX and have more than 1000 bids on 500 or more unique items. Such instances occur in real world applications such as 4th Party Logistics (4PL) auctions, online resource time sharing auctions and the sale of spectrum licenses by the Federal Communications Commission. We perform experiments on 10 such instances to show and compare the performance of the proposed approach to CPLEX (Branch-and-Bound), stochastic greedy search, random walk and a memetic algorithm. Results indicate that in a given runtime, CPLEX results lie within the third quartile of the values generated using our approach for 3 of 10 of the instances. In addition, CPLEX results are on average \(0.24\%\) worse than best values reported using our approach for 5 of 10 instances. Further, our approach performs statistically significantly better (\(p< 0.01\)) than other heuristics on all instances.

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Authors and Affiliations

  1. Krannert School of Management, Purdue University, West Lafayette, IN, 47906, USA

    Abhishek Ray

  2. School of Industrial Engineering, Purdue University, West Lafayette, IN, 47906, USA

    Mario Ventresca

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  1. Abhishek Ray
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  2. Mario Ventresca
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Correspondence to Mario Ventresca .

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Editors and Affiliations

  1. University of Lille, Lille, France

    Arnaud Liefooghe

  2. University of Manchester, Manchester, United Kingdom

    Manuel López-Ibáñez

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Ray, A., Ventresca, M. (2018). An Ant Colony Approach for the Winner Determination Problem. In: Liefooghe, A., López-Ibáñez, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2018. Lecture Notes in Computer Science(), vol 10782. Springer, Cham. https://doi.org/10.1007/978-3-319-77449-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-77449-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77448-0

  • Online ISBN: 978-3-319-77449-7

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