A Hierarchical Approach to Enabling Supplier Choice in On-Demand Distribution Systems



On-demand platforms, exemplified by companies like Uber and Lyft, are a disruptive business model.   Distribution requests (e.g., a delivery) are fulfilled by matching independent suppliers (e.g., freelance logistics providers) with demand requests. Current centralized approaches to platform design excel at meeting demand commitments, but limit supplier autonomy. Decentralized approaches provide supplier autonomy, but sacrifice systematic performance and are time consuming. This research proposes a new hierarchical approach, recasting the platform's role as one providing personalized recommendations (i.e., a menu of multiple requests) to suppliers. Supplier choice can increase participation (capacity) and resource utilization when request fulfillment is combined with suppliers' original planned tasks. Prioritizing a quick time to match and efficient systematic resource coordination, the platform first decides how multiple, simultaneous recommendations are made. Then, suppliers have autonomy to select requests (if any) from the personalized recommendations.

To guide design questions and to quantify the impact of supplier choice on platform efficiency, effectiveness, and equity, we create a bilevel optimization framework. These models are novel as they capture the interdependent outcomes of supplier selections.  By harnessing the problem’s structure, we transform the computationally expensive mixed integer linear bilevel problem into a single level problem by proposing logical expressions.  Able to solve large problems reasonably quickly, the proposed model provides a platform with flexibility in recommending different number of requests to different suppliers. We investigate how personalized recommendation sets can be used as a coordination mechanism able to balance desirable platform, suppliers, and demand request outcomes. We define the platform’s price of choice as the ratio between the platform's maximum benefit (i.e., optimal centralized solution, assuming full compliance by all suppliers) and the platform's objective from the hierarchical solution when suppliers have to be given k choices.  For a platform that only partially estimates suppliers' utilities, we measure how the size of the recommendation set impacts objectives. As the number of choices increases, suppliers have a higher chance to be recommended a request they are willing to select. This benefits the platform, up to a point. However, due to misalignment between the suppliers and the platform’s utilities, larger values of k lead to suppliers selecting a request with lower platform benefit. Also, as the number of choices increases, less systematic coordination occurs, and the chance for rejected requests increases, negatively impacting Price of Choice values. We quantify the impact of our hierarchical approach compared to a centralized, decentralized, and stable-matching approaches for a variety of scenarios. We provide insights on what influences the optimal number of choices and performance based on a crowd-sourced delivery application. 

This is joint work with Ph.D. candidate Shahab Mofidi and is partially funded by the National Science Foundation award 1751801.