Sample data are in high demand for testing and benchmarking purposes. Like many other fields, warehousing and specifically order picking process are not exempt from the need for sample data. Sample data are used in order picking pro- cesses as a way of testing new methodologies such as new routing and new storage allocation approaches. Unfortunately, access to real order picking data is limited because of confidentiality and privacy issues which make it difficult to obtain practical results from the new methodologies. On the other hand, order data follows a highly complex and correlated structure that cannot be easily extracted and replicated. We propose a two-part synthetic data generator that extracts and mimics the general fabric of a set of real data and produces a conceptually unlimited number of orders with any number of SKUs while keeping the structure largely intact. Such data can fill the gap of missing data in order picking process benchmarking.
Progress in Material Handling Research
Ansari, Mohammadnaser; Rasoolian, Behnam; and Smith, Jeffrey S., "Synthetic Order Data Generator for Picking Data" (2018). 15th IMHRC Proceedings (Savannah, Georgia. USA – 2018). 15.