Supply-Demand Ratio and On-Demand Spatial Service Brokers
This paper investigates an on-demand spatial service broker for suggesting service provider propositions and the corresponding estimated waiting times to mobile consumers while meeting the consumer’s maximum travel distance and waiting time constraints. The goal of the broker is to maximize the number of matched requests. In addition, the broker has to keep the “eco-system” functioning not only by meeting consumer requirements, but also by engaging many service providers and balancing their assigned requests to provide them with incentives to stay in the system. 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 for the broker, consumers and service providers in addition to the problem’s computational complexity which is shown to be NP-hard. Related work has mainly focused on maximizing the number of matched requests (or tasks) and minimizing travel cost, but did not consider the importance of engaging more service providers and balancing their assignments, which could become a priority when the available supply highly exceeds the demand. In this work, we propose several matching heuristics for meeting these conflicting requirements, including a new category of service provider centric heuristics. We employed a discrete-event simulation framework and evaluated our algorithms using synthetic datasets with real-world characteristics. Experimental results show that the proposed heuristics can help engage more service providers and balance their assignments while achieving a similar or better number of matched requests. We also show that the matching heuristics have different dominance zones that vary with the supply-demand ratio and that a supply-demand ratio aware broker is needed to select the best matching policy.