Expert System Models for Forecasting Forklifts Engagement in a Warehouse Loading Operation: A Case Study

Dejan Mirčetić
University of Novi Sad, Faculty of Technical Sciences, Department of Traffic Engineering, Chair for Intermodal Transport and Logistics

Nebojša Ralević
University of Novi Sad, Faculty of Technical Sciences, Department of Fundamentals Sciences, Chair of Mathematics

Svetlana Nikoličić
University of Novi Sad, Faculty of Technical Sciences, Department of Traffic Engineering, Chair for Intermodal Transport and Logistics

Marinko Maslarić
University of Novi Sad, Faculty of Technical Sciences, Department of Traffic Engineering, Chair for Intermodal Transport and Logistics

Đurđica Stojanović
University of Novi Sad, Faculty of Technical Sciences, Department of Traffic Engineering, Chair for Intermodal Transport and Logistics

Abstract

The paper focuses on the problem of forklifts engagement in warehouse loading operations. Two expert system (ES) models are created using several machine learning (ML) models. Models try to mimic expert decisions while determining the forklifts engagement in the loading operation. Different ML models are evaluated and adaptive neuro fuzzy inference system (ANFIS) and classification and regression trees (CART) are chosen as the ones which have shown best results for the research purpose. As a case study, a central warehouse of a beverage company was used. In a beverage distribution chain, the proper engagement of forklifts in a loading operation is crucial for maintaining the defined customer service level. The created ES models represent a new approach for the rationalization of the forklifts usage, particularly for solving the problem of the forklifts engagement in
cargo loading. They are simple, easy to understand, reliable, and practically applicable tool for deciding on the engagement of the forklifts in a loading operation.