LAUSR

Abstract This paper proposes and evaluates a new hierarchical approach to peer-to-peer logistics platforms, recasting the platform’s role as one providing personalized menus of requests to freelance suppliers. A 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 have a choice to select which request (if any) to fulfill. 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. In these environments, 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.