Solving Sequential Mixed Auctions with Integer Programming

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Article , Conference object , Other literature type 01 Jan 2011Publisher:Springer Berlin Heidelberg

Authors: Boris Mikhaylov; Jesús Cerquides; Juan A. Rodríguez-Aguilar;

doi: 10.1007/978-3-642-25274-7_5

handle: 10261/138277

Solving Sequential Mixed Auctions with Integer Programming

- Summary
- Subjects
- Metrics

Abstract

Mixed multi-unit combinatorial auctions (MMUCAs) offer a high potential to be employed for the automated assembly of supply chains of agents. However, in order for mixed auctions to be effectively applied to supply chain formation, we must ensure computational tractability and reduce bidders¿ uncertainty. With this aim, we introduce Sequential Mixed Auctions (SMAs), a novel auction model conceived to help bidders collaboratively discover supply chain structures. Thus, an SMA allows bidders progressively build a supply chain structure through successive auction rounds.

Peer Reviewed

Related Organizations

Spanish National Research Council
Spain

Keywords

Multi-unit combinatorial auctions, Automated assembly, Computational tractability, Supply chain formation, High potential, Supply chain structures, Auction model

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	2
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average