Unit load devices (Uld) demand forecasting in the air cargo fooptimal cost management

Abstract

In recent decades, the airline industry has become very competitive. With the advent of large aircraft in service, unit load devices (ULD) have become an essential element for efficient air transport. They can load a large amount of baggage, cargo or mail using only one unit. Since this results in fewer units to load, saving time and efforts of ground crews and helping to avoid delayed flights. However, a deficient loading of the units causes operating irregularities, costing the company and contributing to the dissatisfaction of the customers. In contrast, an excess load of containers is at the expense of cargo. In this paper we propose an approach to predict the demand for baggage in order to optimize the management of its ULD flow. Specifically, we build prediction models: ARIMA following the BOX‐JENKINS approach and exponential smoothing methods, in order to obtain more accurate forecasts. The approach is tested using the operational data of flight processing and the results are compared with four benchmark method (SES, DES, Holt‐Winters and Naive prediction) using different performance indicators: MAE, MSE, MAPE, WAPE, RMSE, SMPE. The results obtained with the exponential smoothing methods surpass the benchmarks by providing more accurate forecasts.