LAUSR

Abstract The focus of this paper is on matching service seekers and service providers, such as designers and machine owners, in cloud-based design and manufacturing (CBDM). In such decentralized scenarios the objectives and preferences of service seekers are different from those of service providers. Current resource configuration methods are unsuitable because they optimize the objectives of only one type of participants – either service seekers or service providers. Existing marketplaces based on first-come-first-serve (FCFS) approach are inefficient because they may not result in optimal matches. To address these limitations there is a need for mechanisms that result in optimal matching considering the private preferences of all the agents. In this paper, we formulate the resource allocation problem as a bipartite matching problem. Four bipartite matching mechanisms, namely, Deferred Acceptance (DA), Top Trading Cycle (TTC), Munkres, and FCFS are analyzed with respect to desired properties of the mechanisms such as individual rationality, stability, strategy proofness, consistency, monotonicity and Pareto efficiency. Further, the performance of these mechanisms is evaluated under different levels of resource availability through simulation studies. The appropriateness of matching mechanisms for different scenarios in CBDM such as fully decentralized, partially decentralized and totally monopolistic are assessed. Based on the analysis, we conclude that DA is the best mechanism for totally decentralized scenario, TTC is most appropriate when cloud-based resources are used in an organizational scenario, and Munkres is the best mechanism when all resources are owned by a single agent.