A double-sided bundle auction mechanism for collaborative additive manufacturing

Abstract

Collaborative manufacturing has become an essential strategy for improving resource utilization and profitability in the additive manufacturing (AM) industry. In this study, we propose an additive manufacturing collaboration platform (AMCP) to facilitate bilateral resource exchange in decentralized AM environments. To address the challenges posed by the two-way, many-to-many exchange of heterogeneous AM resources in AMCP, we design a five-phase double-sided bundle auction (DBA) mechanism. This mechanism is proven to satisfy incentive compatibility, individual rationality, budget balance, and allocative efficiency, ensuring fair and efficient collaboration among self-interested participants. To optimize the winner determination problem in DBA, which arises from overlapping transactions and flexible bundle bidding, we reformulate it as a maximum weight independent set problem in graph theory. Based on this reformulation, we develop a graph-based adaptive neighborhood search algorithm that balances computational efficiency and solution quality. The experimental results demonstrate that the proposed mechanism is both feasible and robust. It achieves superior social welfare, cooperation rates, and scalability when compared to traditional approaches. Additionally, sensitivity analysis reveals the robustness and resilience of the proposed mechanism, even in the presence of strategic manufacturers.