Online spatio-Temporal matching in stochastic and dynamic domains
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Jaillet_Online spatio-temporal.pdf
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428.71 KB
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Author(s)
Lowalekar, Meghna
Varakantham, Pradeep
Jaillet, Patrick
Date Issued
March 2016
Journal
Thirtieth AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence
Citation
Lowalekar, Meghna, Pradeep Varakantham, and Patrick Jaillet. "Online Spatio-Temporal Matching in Stochastic and Dynamic Domains." Thirtieth AAAI Conference on Artificial Intelligence, 12-17 February 2016, Phoenix, Arizona, Association for the Advancement of Artificial Intelligence © 2016 Association for the Advancement of Artificial Intelligence
Version
Author's final manuscript
Abstract
Spatio-temporal matching of services to customers online is a problem that arises on a large scale in many domains associated with shared transportation (ex: taxis, ride sharing, super shuttles, etc.) and delivery services (ex: food, equipment, clothing, home fuel, etc.). A key characteristic of these problems is that matching of services to customers in one round has a direct impact on the matching of services to customers in the next round. For instance, in the case of taxis, in the second round taxis can only pick up customers closer to the drop off point of the customer from the first round of matching. Traditionally, greedy myopic approaches have been adopted to address such large scale online matching problems. While they provide solutions in a scalable manner, due to their myopic nature the quality of matching obtained can be improved significantly (demonstrated in our experimental results). In this paper, we present a two stage stochastic optimization formulation to consider expected future demand. We then provide multiple enhancements to solve large scale problems more effectively and efficiently. Finally, we demonstrate the significant improvement provided by our techniques over myopic approaches on two real world taxi data sets.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12385
