Solving product allocation problem (PAP) by using ANN and clustering

Abstract

Proper planning of a warehouse layout and the product allocation in it, constitute major challenges for companies. In the paper, the new approach for the classification of the problem is presented. Authors used real picking data from the Warehouse Management System (WMS) from peak season from September to January. Artificial Neural Network (ANN) and automatic clustering by using Calinski-Harabasz criterion were used to develop a new classification approach. Based on the picking list the clients' orders were prepared and analyzed. These orders were used as input data to ANN and clustering. In this paper, three variants were analyzed: the reference representing the current state, variant with product relocation by using ANN, and the variant with relocation by using automatic clustering. In the research over 380000 picks for almost 1600 locations were used. In the paper, the architecture of the system module for solving the PAP problem is presented. Presented research proved that using multi-criterion clustering can increase the efficiency of the order picking process.

Keywords

• product allocation problem
• artificial intelligence
• artificial neural network
• clustering
• picking list analysis