Comparative Study of Heuristics in Improved Winner Determination in Combinatorial Auction.

Date of Submission

December 2009

Date of Award

Winter 12-12-2010

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Applied Statistics Unit (ASU-Kolkata)


Roy, Bimal Kumar (ASU-Kolkata; ISI)

Abstract (Summary of the Work)

Combinatorial auction is a type of auction where bidders can bid on combinations of items, tend to lead more efficient allocation of items between agents than traditional auction mechanisms where it might possible that agents’ valuation be not additive. However, determining the winners so as to maximize revenue is NP-complete. In this report first, we will discuss about existing approaches for tackling this problem: exhaustive enumeration,dynamic programming and drawbacks. Second, we will discuss about the possibility of approximate winner determination in the general case with reasonable bound and Inapproximability result. After that we will present the possible existing approaches for optimal winner determination . We will study one of the search technique branch on items and try to improvise the search technique to find optimal winner determination in combinatorial auction with our search algorithm with improved data structure. Experiments are shown on weighted random bid distributions .This algorithm uses value added Bidtree for preferred child generation to make main search faster and item tree for faster heuristic computation. In later section, we will study the effect of different admissible heuristics on the performance of our algorithm by node generation,node expansion,item computation & bidtree node traversal. In last we will present experimental result for both of heuristics that is helpful to understand how heuristics effect performance in Winner determination in Combinatorial auctio


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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