A Hybrid Approach for Solving the Collaborative Batching Scheduling P...

Additive Manufacturing (AM) is a technology with the potential to disrupt entire supply chains - one reason why AM attracted attention from both academia and practitioners in the last decade. In recent years, researchers have focused on increasing the efficiencies of AM operations as the technology has reached new levels of maturity. Collaborative production (CP) is a proven approach for decreasing the costs of operations in conventional fields of production. However, CP has not been sufficiently studied in the context of AM. Our study aims to close this research gap by demonstrating the impact of collaborative planning in the field of AM. We introduce the collaborative multi-site batching scheduling problem in the context of AM, wherein we assume that production orders have to be batched and scheduled at several geographically dispersed manufacturing sites by a central authority. We formulate the problem within a quadratic model. As AM production planning combines bin packing and scheduling problems, both of them which are strongly NP-hard, we devise an efficient hybrid solving method. In our approach, the model is solved by combining mixed integer programming with Genetic Algorithms, wherein batching and scheduling problems are solved sequentially. An extensive computational study reveals that the proposed approach yields very good solution quality within short computational times. Managerial insights emphasize that cross-site collaborative production planning can significantly decrease the overall costs of AM operations.