Alleviating Users' Pain of Waiting: Effective Task Grouping for Online-to-Offline Food Delivery Services

Ordering take-out food (a.k.a. takeaway food) on online-to-offline (O2O) food ordering and delivery platforms is becoming a new lifestyle for people living in big cities, thanks to its great convenience. Web users and mobile device users can order take-out food (i.e. obtain online food ordering services) on an O2O platform. Then the O2O platform will dispatch food carriers to deliver food from restaurants to users, i.e. providing users with offline food delivery services. For an O2O food ordering and delivery platform, improving food delivery efficiency, given the massive number of food orders each day and the limited number of food carriers, is of paramount importance to reducing the length of time users wait for their food. Thus, in this paper, we study the food delivery task grouping problem so as to improve food delivery efficiency and alleviate the pain of waiting for users, which to the best of our knowledge has not been studied yet. However, the food delivery task grouping problem is challenging, given two reasons. First, the food delivery efficiency is affected by multiple factors, which are non-trivial to formulate and jointly consider. Second, the problem is a typical NP-hard problem and to find near-optimal grouping results is not easy. To address these two issues, we propose an effective task grouping method. On one hand, we provide formal formulations for the factors affecting the food delivery efficiency, and provide an objective to organically combine these factors such that it can better guide the task grouping. On the other hand, we propose heuristic algorithms to efficiently obtain effective task grouping results, consisting of a greedy algorithm and a replacement algorithm. We evaluate our task grouping method using take-out food order data from web users and mobile device users on a real-world O2O food ordering and delivery platform. Experiment results demonstrate that our task grouping method can save ~ 16% (87 seconds) of average waiting time for each user, comparing with many baseline methods. It indicates that our method is able to significantly improve the food delivery efficiency and can provide better food delivery services for users.