Logistics Warehouse

This dataset, denoted as 𝑾_data, represents a synthetic yet structurally authentic warehouse management dataset comprising 4,132 records and 11 well-defined attributes. It was generated using the Gretel.ai platform, following the structural standards provided by the TI Supply Chain API–Storage Locations specification. The dataset encapsulates essential operational and spatial parameters of warehouses, including unique identifiers, geospatial coordinates, storage capacities, and categorical capacity statuses.

This project aims to optimize the routing of large-scale Automated Guided Vehicles (AGVs) in a large-scale logistics warehouse in Japan using Quantum Annealing. The project will generate real-world operational problems and use them for performance evaluation. It is designed to be compatible with various solvers, including classical solvers and quantum annealing solvers.