A Novel Demand Dispatching Model for Autonomous On-Demand Services
Lei Yang, Xi Yu, Jiannong Cao, Wengen Li, Yuqi Wang, Michal Szczecinski · IEEE Transactions on Services Computing · 2019
Recent on-demand services, such as Uber and DiDi, provide a platform for users to request services on the spot and for suppliers to meet such demand. In such platforms, demands are dispatched to suppliers round by round, and suppliers have autonomy to decide whether to accept demands or not. Existing approaches dispatch a demand to multiple suppliers in each round, while a supplier can only receive one demand. However, by using these approaches, pended demands can not be fully dispatched in a round specially when suppliers are not sufficient, and thus need to wait for many rounds to be dispatched, leading to long response time. In this paper, we propose a novel demand dispatching model, named by many-to-many model. The novelty of the model is that a supplier could receive multiple demands in a round, such that the demand has high chance to be dispatched and answered within short time. More specifically, we first learn the probability distribution function of the response time of a supplier to a given demand, by considering the features of both the demand and the supplier. Taking the learned results as input, our model generates an optimal matching between the demands and suppliers to minimize the overall response time of the demands via solving an optimization problem. Experiments on real-world datasets show that our model is better than the start-of-art models in terms of successful acceptance rate and response time.