Using a Lagrangian Heuristic for a Combinatorial Auction Problem

Combinatorial auctions allow bidders to bid for items leading to more efficient allocations, but determining winners in auctions is $\mathcal{NP}$-complete. In this work, a simple yet effective Lagrangian relaxation based heuristic algorithm is presented. Extensive computational experiments using st...

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المؤلفون الرئيسيون: GUO, Yunsong, LIM, Andrew, RODRIGUES, Brian, TANG, Jiqing
التنسيق: text
اللغة:English
منشور في: Institutional Knowledge at Singapore Management University 2006
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الوصول للمادة أونلاين:https://ink.library.smu.edu.sg/lkcsb_research/558
https://doi.org/10.1142/S0218213006002771
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المؤسسة: Singapore Management University
اللغة: English
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spelling sg-smu-ink.lkcsb_research-15572016-03-12T07:10:29Z Using a Lagrangian Heuristic for a Combinatorial Auction Problem GUO, Yunsong LIM, Andrew RODRIGUES, Brian TANG, Jiqing Combinatorial auctions allow bidders to bid for items leading to more efficient allocations, but determining winners in auctions is $\mathcal{NP}$-complete. In this work, a simple yet effective Lagrangian relaxation based heuristic algorithm is presented. Extensive computational experiments using standard benchmark data (CATS) as well as newly generated more realistic test sets were conducted which showed the heuristic was able to provide optimal solutions for most test cases and is within 1% from the optimums for the rest within very short times. Experiements comparing CPLEX 8.0, the fastest current algorithm, showed the heuristic was able to provide equally godd or better solutions often requring less than 1% of the time required by CPLEX 8.0. 2006-07-01T07:00:00Z text https://ink.library.smu.edu.sg/lkcsb_research/558 info:doi/10.1142/S0218213006002771 https://doi.org/10.1142/S0218213006002771 Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Combinatorial auction Langrange relaxation heuristic Operations and Supply Chain Management
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Combinatorial auction
Langrange relaxation
heuristic
Operations and Supply Chain Management
spellingShingle Combinatorial auction
Langrange relaxation
heuristic
Operations and Supply Chain Management
GUO, Yunsong
LIM, Andrew
RODRIGUES, Brian
TANG, Jiqing
Using a Lagrangian Heuristic for a Combinatorial Auction Problem
description Combinatorial auctions allow bidders to bid for items leading to more efficient allocations, but determining winners in auctions is $\mathcal{NP}$-complete. In this work, a simple yet effective Lagrangian relaxation based heuristic algorithm is presented. Extensive computational experiments using standard benchmark data (CATS) as well as newly generated more realistic test sets were conducted which showed the heuristic was able to provide optimal solutions for most test cases and is within 1% from the optimums for the rest within very short times. Experiements comparing CPLEX 8.0, the fastest current algorithm, showed the heuristic was able to provide equally godd or better solutions often requring less than 1% of the time required by CPLEX 8.0.
format text
author GUO, Yunsong
LIM, Andrew
RODRIGUES, Brian
TANG, Jiqing
author_facet GUO, Yunsong
LIM, Andrew
RODRIGUES, Brian
TANG, Jiqing
author_sort GUO, Yunsong
title Using a Lagrangian Heuristic for a Combinatorial Auction Problem
title_short Using a Lagrangian Heuristic for a Combinatorial Auction Problem
title_full Using a Lagrangian Heuristic for a Combinatorial Auction Problem
title_fullStr Using a Lagrangian Heuristic for a Combinatorial Auction Problem
title_full_unstemmed Using a Lagrangian Heuristic for a Combinatorial Auction Problem
title_sort using a lagrangian heuristic for a combinatorial auction problem
publisher Institutional Knowledge at Singapore Management University
publishDate 2006
url https://ink.library.smu.edu.sg/lkcsb_research/558
https://doi.org/10.1142/S0218213006002771
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