Abstract
This paper investigates an on-demand spatial service broker for suggesting service provider propositions and the corresponding time of service to mobile consumers while meeting the consumer's maximum travel time and wait time constraints. The goal of the broker is to maximize the number of matched requests while also keeping the “eco-system” functioning and robust against variations in the supply–demand ratio by engaging as many service providers as possible and maintaining providers’ participation to ensure that providers do not drop out. This problem is important because of its many related societal applications in the on-demand and sharing economy (e.g., on-demand ride-hailing services, on-demand food delivery, etc.). Challenges of this problem include the need to satisfy many conflicting requirements of the broker, consumers and service providers and the high computational complexity since solving the problem for a number of available consumers at any time instant is NP-hard. Related work in spatial crowdsourcing and ridesharing has mainly focused on maximizing the number of matched requests and minimizing travel cost, but did not consider the importance of maintaining provider engagement and balancing provider utilization, which could become a priority when the available supply exceeds the demand. In this paper, we propose a Utilization-Aware Matching Approach (ULAMA) which employs novel provider-centric heuristics for balancing the utilization of providers, and a consumer-priority-based greedy matching algorithm that prioritizes consumers for maximizing the number of matched requests. Experimental results show that our proposed approach outperforms related work by achieving the lowest variance in provider utilization while matching all available providers even when supply greatly exceeds demand. Our approach also achieved a larger number of matched requests, particularly when supply exceeds demand and also when both supply and demand are balanced.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1030-1048 |
| Number of pages | 19 |
| Journal | Future Generation Computer Systems |
| Volume | 108 |
| DOIs | |
| State | Published - Jul 2020 |
Bibliographical note
Funding Information:This material is based upon work supported by FORD University Research Program (URP), United States, the National Science Foundation, United States under Grant No. IIS-1320580 and 1737633, the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Grant No. DE-AR0000795, the USDA, United States under Grant No. 2017-51181-27222, the OVPR U-Spatial and Minnesota Supercomputing Institute (MSI) (www.msi.umn.edu) at the University of Minnesota, United States. We would like to thank Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their comments.
Funding Information:
This material is based upon work supported by FORD University Research Program (URP), United States , the National Science Foundation, United States under Grant No. IIS-1320580 and 1737633 , the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy , under Grant No. DE-AR0000795 , the USDA, United States under Grant No. 2017-51181-27222 , the OVPR U-Spatial and Minnesota Supercomputing Institute (MSI) ( www.msi.umn.edu ) at the University of Minnesota, United States . We would like to thank Kim Koffolt and the members of the University of Minnesota Spatial Computing Research Group for their comments.
Publisher Copyright:
© 2018 Elsevier B.V.
Keywords
- On-demand services
- On-demand spatial service broker
- Provider-centric matching
- Supply–demand matching
- Utilization-aware matching
