Please use this identifier to cite or link to this item:
Title: A multi-unit combinatorial auction based approach for decentralized multi-project scheduling
Authors: Song, Wen
Kang, Donghun
Zhang, Jie
Xi, Hui
Keywords: Engineering::Computer science and engineering
Multi-project Scheduling
Multi-unit Combinatorial Auction
Issue Date: 2017
Source: Song, W., Kang, D., Zhang, J., & Xi, H. (2017). A multi-unit combinatorial auction based approach for decentralized multi-project scheduling. Autonomous Agents and Multi-Agent Systems, 31(6), 1548-1577. doi:10.1007/s10458-017-9370-z
Series/Report no.: Autonomous Agents and Multi-Agent Systems
Abstract: In industry, many problems are considered as the decentralized resource-constrained multi-project scheduling problem (DRCMPSP). Existing approaches encounter difficulties in dealing with large DRCMPSP cases while respecting the information privacy requirements of the project agents. In this paper, we tackle DRCMPSP by formulating it as a multi-unit combinatorial auction (Wellman et al. in Games Econ Behav 35(1):271–303, 2001), which does not require sensitive private project information. To handle the hardness of bidder valuation, we introduce the capacity query which uses different item capacity profiles to efficiently elicit valuation information from bidders. Based on the capacity query, we adopt two existing strategies (Gonen and Lehmann in Proceedings of the 2nd ACM conference on electronic commerce, pp 13–20, 2000) for solving multi-unit winner determination problems to find good allocations of the DRCMPSP auctions. The first strategy employs a greedy allocation process, which can rapidly find good allocations by allocating the bidder with the best answer after each query. The second strategy is based on a branch-and-bound process to improve the results of the first strategy, by searching for a better sequence of granting the bids from the bidders. Empirical results indicate that the two strategies can find good solutions with higher quality than state-of-the-art decentralized approaches, and scale well to large-scale problems with thousands of activities from tens of projects.
ISSN: 1387-2532
DOI: 10.1007/s10458-017-9370-z
Schools: School of Computer Science and Engineering 
Research Centres: Rolls-Royce@NTU Corporate Lab 
Rights: © 2017 The Author(s). All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Citations 20

Updated on Jun 7, 2024

Web of ScienceTM
Citations 20

Updated on Oct 27, 2023

Page view(s)

Updated on Jun 9, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.