A Hierarchical Approach to Enabling Supplier Choice In On-Demand Platforms

Abstract

In peer-to-peer logistics platforms, capacity cannot be set. Instead, capacity must be enticed from decentralized suppliers (who provide access to their resources). Current peer-to-peer platforms for crowdsourced delivery and ride-sharing do not operationally prioritize suppliers' underutilized resources when making assignments. This paper is interested in evaluating a new hierarchical approach, recasting the platform's role as one providing personalized menus of requests for suppliers to choose from. Supplier choice can increase participation (capacity) and resource utilization. However, coordinating decentralized resources using multiple, simultaneous recommendations is complicated because platform performance is the result of interdependent suppliers' selection outcomes, in which some requests may not be selected and other requests are selected by more than one supplier. Therefore, the objective of this research is to determine when providing suppliers with choices is beneficial to the platform and to quantify this benefit for different environments. A novel bilevel optimization formulation explicitly models the two stage decision process: first, the platform determines which set of requests to recommend to which suppliers, and second, suppliers having a choice to select which request (if any) they would like to serve. By harnessing the problem's structure, the computationally expensive mixed-integer linear bilevel problem is transformed into an equivalent single-level problem that is computationally superior. A computational study based on ride-sharing quantifies the value of providing suppliers with choices. When a platform's knowledge of suppliers' selections is imperfect, our hierarchical approach outperforms existing recommendation methods, namely, centralized, many-to-many stable matching, and decentralized approaches. We show that a platform's lack of knowledge over suppliers' selections can be compensated by providing choices in environments with either inflexible suppliers or when suppliers' utilities have higher variance than the platform's utilities. We find that providing choices and recommending alternatives to more than one supplier can be beneficial to not only the platform, but also freelance suppliers and demand requests.