A structural lower bound of online task allocation for sharing platforms

Abstract

In the process of task allocation on sharing platforms, the following situations are often encountered: The decision-makers need to decide how to reasonably allocate the current demand to the existing server to maximize the profit of the platform, when the future demand task sequence information (arrival time, start time, duration, etc.) is unknown. There is a limit on the number of servers on the platform, and at the same time, it cannot be re-allocated once a demand is allocated. The models and algorithms established previously mainly used for static task allocation environment, but here we need a dynamic task allocation model with above constraints. This paper establishes an online sharing platform task allocation model with a maximizing platform’s profit objective, where the profit include not only variable proportion for the shares but also the fixed income. Applying the Yao principle, we give a lower bound of competitive ratio for this problem. This lower bound does not need any complexity assumption, so it is a structural lower bound.