Stable matching of customers and manufacturers for sharing economy of additive manufacturing

With rapid advances in internet and computing technologies, sharing economy paves a new way for people to “share” assets and services with others that disrupts traditional business models across the world. Specifically, rapid growth of additive manufacturing (AM) enables individuals and small manufacturers to own machines and share under-utilized resources with others. Such a decentralized market calls upon the development of new analytical methods and tools to help customers and manufacturers find each other and further shorten the AM supply chain. This paper presents a bipartite matching framework to model the resource allocation among customers and manufacturers and leverage the stable matching algorithm to optimize matches between customers and AM providers. We perform a comparison study with Mix Integer Linear Programming (MILP) optimization as well as the first-come-first-serve (FCFS) allocation strategy for different scenarios of demand-supply configurations (i.e., from 50% to 500%) and system complexities (i.e., uniform parts and manufacturers, heterogeneous parts and uniform manufacturers, heterogeneous parts and manufacturers). Experimental results show that the proposed framework has strong potentials to optimize resource allocation in the AM sharing economy.