A Capacity Allocation Model for Air Cargo Industry: A Case Study

Ilgün D., Alptekin S. E.

International Conference on Intelligent and Fuzzy Systems, INFUS 2022, İzmir, Turkey, 19 - 21 July 2022, vol.505 LNNS, pp.426-437 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 505 LNNS
  • Doi Number: 10.1007/978-3-031-09176-6_49
  • City: İzmir
  • Country: Turkey
  • Page Numbers: pp.426-437
  • Keywords: Capacity allocation, Artificial neural network, CVaR, Air cargo industry
  • Galatasaray University Affiliated: Yes


© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.The transportation sector is one of the basic elements of the country’s economy. Today, the growth and enrichment of the economy, competitive conditions due to the need to send products faster, such as security increases the importance of the transport sector. Due to meeting these needs, the importance of air cargo transportation is increasing day by day. Air cargo transportation can be reserved for allotment agreements or opened directly for free sale. Defining the proportion of the capacity for allotment agreements and free sale are among the most fundamental issues in the air cargo industry. That remains an open problem; since capacity allocation is an essential factor affecting the profitability of air cargo companies. In this paper, we used Conditional Value at Risk (CVaR) and Artificial Neural Network (ANN) models to solve the capacity allocation problem. We used real datasets for different destinations. The results of the capacity allocation model provide a basis for pricing policies. The paper concludes by giving open research issues related to the capacity allocation problem and the cargo industry.