Presenting an improved heuristic algorithm for the storage space allocation problem under a dedicated storage policy

The correct storage and arrangement of products in the warehouse increase efficiency in responding to requests, accelerate the identification of products, increase accessibility of items in the warehouse, make more use of available space in the warehouse, reduce the possibility of product damage, and increase flexibility. The review of studies in the field of warehousing and arrangement of products in warehouses revealed that the use of machine learning algorithms in this field is one of the important research gaps. Therefore, in this article, using machine learning algorithms, we aim to present an innovative algorithm for allocating goods to different parts of a warehouse, for which a real case study is used. The goal of categorizing products based on their characteristics is to minimize the total cost of the system. Hence, spatial clustering algorithms based on the density of applications with noise (DBSCAN), self-organizing mapping neural network (SOM), and AGNES are used. The obtained results show that SOM has better performance than DBSCAN. Also, the DBSCAN algorithm performs better than AGNES.