This project presents an Algorithmic Layout Planning (ALP) framework developed in collaboration with Schaeffler AG. The solution uses a Mixed-Integer Programming (MIP) model to optimize factory layouts based on real-world data—machine dimensions, routing sequences, and spatial constraints. The model, consisting of 764 variables, 277 linear constraints, and 684 indicator constraints, was solved using the Gurobi Optimizer. The resulting layout meets all geometric and operational requirements, providing a robust and scalable solution for smart manufacturing environments.
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Factory layout optimization using Mixed-Integer Programming. Integrates real-world constraints and production data; solved with Gurobi to improve flow efficiency, minimize material handling, and support smart manufacturing design.
liereynaldo/Algorithmic-Factory-Layout-Planning-Schaeffler-AG
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Factory layout optimization using Mixed-Integer Programming. Integrates real-world constraints and production data; solved with Gurobi to improve flow efficiency, minimize material handling, and support smart manufacturing design.
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