A hybrid approach of linear programming and logic modeling for the market core of sealed bid auctions

Ho Geun Lee, Ronald M. Lee

Research output: Contribution to journalArticle

Abstract

This paper presents a hybrid approach for sealed bid auction that integrates linear programming and logic modeling techniques. A linear programming model for the sealed bid auction considers only prices for transaction arrangements and yields multiple market cores when goods from sellers are equally attractive to many buyers. The hybrid approach takes into account ordinal preferences of traders as well to find the best market core. On the one hand, the hybrid approach uses a linear programming model to maximize total surplus of market participants based on the bid and ask prices. On the other hand, it employs a logical inferencing approach to satisfy traders' ordinal preferences that are not included in a single combination of bid and ask prices. A constraint logic programming scheme, which combines a mathematical program with a logical inferencing technique in an integrated formalism, is introduced to implement the hybrid approach. The hybrid approach is validated through market simulations, where a set of computer-generated trading data is applied to both the hybrid approach and the linear programming model for their market performance comparison.

Original languageEnglish
Pages (from-to)209-232
Number of pages24
JournalAnnals of Operations Research
Volume75
Publication statusPublished - 1997 Dec 1

Fingerprint

Modeling
Linear programming
Logic
Sealed-bid auction
Hybrid approach
Traders
Bid
Surplus
Logic programming
Formalism
Integrated
Seller
Simulation
Market performance
Buyers

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Management Science and Operations Research

Cite this

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A hybrid approach of linear programming and logic modeling for the market core of sealed bid auctions. / Lee, Ho Geun; Lee, Ronald M.

In: Annals of Operations Research, Vol. 75, 01.12.1997, p. 209-232.

Research output: Contribution to journalArticle

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