A Novel Demand Dispatching Model for Autonomous On-Demand Services

Recent on-demand services such as Uber 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. 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. In this paper, we propose a novel many-to-many demand dispatching 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. We first learn the probability distribution of the response time of a supplier to a given demand. Taking the learned results as input, our model generates an optimal matching between the demands and suppliers to minimize the average response time. Experiments on real-world datasets show our model is better than the start-of-art models in terms of successful acceptance rate and response time.