Unit load devices (ULD) demand forecasting in the air cargo for optimal cost management

• Autorzy: Mikram Mounia , Rhanoui Maryem , Yousf Siham , Briwa Houda

Identyfikatory

DOI

10.14313/JAMRIS/3‐2020/37

• Języki publikacji: EN

Abstrakty

EN

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 ele‐ ment 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 flig‐ hts. However, a deficient loading of the units causes ope‐ rating irregularities, costing the company and contribu‐ ting 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 de‐ mand for baggage in order to optimize the management of its ULD flow. Specifically, we build prediction models: ARIMA following the BOX‐JENKINS approach and expo‐ nential smoothing methods, in order to obtain more accu‐ rate forecasts. The approach is tested using the operatio‐ nal 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 obtai‐ ned with the exponential smoothing methods surpass the benchmarks by providing more accurate forecasts.

Słowa kluczowe

EN

air transport; ULD; machine learning; ARIMA; exponential smoothing

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2020
• Tom: Vol. 14, No. 3
• Strony: 71--80
• Opis fizyczny: Bibliogr. 25 poz., rys.

Twórcy

autor

Mikram Mounia

• LRIT, Faculty of Science, Mohammed V University in Rabat
• Meridian Team, LYRICA Laboratory, School of Information Sciences, Morocco

autor

Rhanoui Maryem

• IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University in Rabat
• Meridian Team, LYRICA Laboratory, School of Information Sciences, Morocco

autor

Yousf Siham

• SIP Research Team, Rabat IT Center, EMI, Mohammed V University in Rabat
• Meridian Team, LYRICA Laboratory, School of Information Sciences, Morocco

autor

Briwa Houda

• Meridian Team, LYRICA Laboratory, School of Information Sciences, Morocco

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