Optimization of logistics operator planning and dock allocation in a warehouse

Abstract

This article aims to enhance the productivity of Numilog, a logistics and transportation service provider, through two main objectives. The first step involves using traditional forecasting methods to estimate goods flow. These forecasts form the basis for applying artificial intelligence techniques to predict the optimal number of logistics operators needed daily. The second step focuses on developing an algorithm dedicated to optimizing dock allocation within the warehouse. The goal is to select the nearest dock for each order preparation and stock placement operation. The results obtained show a significant improvement in the accuracy of flow forecasts, as well as in the prediction of the number of necessary operators. Additionally, the dock allocation algorithm has reduced the distance traveled, thereby improving logistical efficiency