Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2008
Abstract
In this paper, we evaluate whether the robustness of a market mechanism that allocates complementary resources could be improved through the aggregation of time periods in which resources are consumed. In particular, we study a multi-round combinatorial auction that is built on a general equilibrium framework. We adopt the general equilibrium framework and the particular combinatorial auction design from the literature, and we investigate the benefits and the limitation of time-period aggregation when demand-side uncertainties are introduced. By using simulation experiments, we show that under stochastic conditions the performance variation of the process decreases as the time frame length (time frames are obtained by aggregating time periods) increases. This is achieved without causing deterioration in the mean performance.
Keywords
market-based approach, uncertainty, robust resource allocation
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Publication
WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology: 9-12 December, 2008, Sydney
First Page
373
Last Page
379
ISBN
9780769534961
Identifier
10.1109/WIIAT.2008.293
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
CHENG, Shih-Fen; TAJAN, John; and LAU, Hoong Chuin.
Distributing complementary resources across multiple periods with stochastic demand. (2008). WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology: 9-12 December, 2008, Sydney. 373-379.
Available at: https://ink.library.smu.edu.sg/sis_research/283
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1109/WIIAT.2008.293
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons