Dynamic Optimization in Peer-To-Peer Transportation with Acceptance Probability Approximation

Abstract

Crowdsourced transportation by independent suppliers (or drivers) is central to urban delivery and mobility platforms. While utilizing crowdsourced resources has several advantages, it comes with the challenge that suppliers are not bound to assignments made by the platforms. In practice, suppliers often decline offered service requests, e.g., due to the required travel detour, the expected tip, or the area a request is located. This leads to inconveniences for the platform (ineffective assignments), the corresponding customer (delayed service), and also the suppliers themselves (non-fitting assignment, less revenue). In this work, we show how approximating suppliers’ acceptance behavior by analyzing their past decision making can alleviate these inconveniences. To this end, we propose a dynamic matching problem where suppliers’ acceptances or rejections of offers are uncertain and depend on a variety of request attributes. Suppliers who accept an offered request from the platform are assigned and reenter the system after service looking for another offer. Suppliers declining an offer stay idle to wait for another offer, but leave after a limited time if no acceptable offer is made. Every supplier decision reveals partial information about the suppliers’ acceptance behavior, and in this paper, we present a corresponding mathematical model and a solution approach that translates supplier responses into the probability of a specific supplier to accept a specific future offer and uses this information to optimize subsequent offering decisions. We show that our approach leads to overall more successful assignments, more revenue for the platform and most of the suppliers, and less waiting for the customers to be served. We also show that considering individual supplier behavior can lead to unfair treatment of more agreeable suppliers.