Suchergebnisse

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.

AbstractThis article formulates and solves a stochastic optimization model to investigate the impact of crowdsourced platforms (e.g., ridesharing, on‐demand delivery, volunteer food rescue, and carpooling) offering small, personalized menus of requests and incentive offers for drivers to choose from. To circumvent nonlinear variable relationships, we exploit model structure to formulate the program as a stochastic linear integer program. The proposed solution approach models stochastic responses as a sample of variable and fixed scenarios, and to counterbalance solution overfitting, uses a participation ratio parameter. The problem is also decomposed and iterated among two separate subproblems, one which optimizes menus, and another, which optimizes incentives. Computational experiments, based on a ride sharing application using occasional drivers demonstrate the importance of using multiple scenarios to capture stochastic driver behavior. Our method provides robust performance even when discrepancies between predicted and observed driver behaviors exist. Computational results show that offering menus and personalized incentives can significantly increase match rates and platform profit compared to recommending a single request to each driver. Further, compared to the menu‐only model, the average driver income is increased, and more customer requests are matched. By strategically using personalized incentives to prioritize promising matches and to increase drivers' willingness to accept requests, our approach benefits both drivers and customers. Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver‐request pairs less likely to be accepted.