A revenue management slot allocation model for liner shipping networks

Sebastian Zurheide and Kathrin Fischer
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Sebastian Zurheide: Institute for Operations Research and Information Systems, Hamburg University of Technology, Schwarzenbergstr. 95 D, 21073 Hamburg, Germany.
Kathrin Fischer: Institute for Operations Research and Information Systems, Hamburg University of Technology, Schwarzenbergstr. 95 D, 21073 Hamburg, Germany.

Maritime Economics & Logistics, 2012, vol. 14, issue 3, 334-361

Abstract: The use of revenue management methods is still an up and coming topic in the liner shipping industry. In many liner shipping companies, decisions on container bookings are made by skilled employees without, or with little use of, decision support systems. Also in the literature, only a few publications on the topic of revenue management in the liner shipping industry can be found. Most of the models that have been suggested so far consider only one service and one ship cycle on this service. However, in liner shipping, it is important to consider the possibility of transhipment between services and of different demand situations at different times. Moreover, drawing inferences from similar developments in other industries and the literature, it seems promising to create a segmentation that divides container bookings into urgent and non-urgent cargo. This segmentation gives the customers more control over their cargo, and the carrier can gain additional revenue through extra charges. To achieve that aim, the carrier needs to keep some slots available until closing time, so he can offer slots on the next ship to customers with urgent cargo. On the basis of these facts, a new quantitative slot allocation model is developed that takes into account priority service segmentation, the network structure of liner shipping with the possibility of transhipment, and the existence of different ship cycles on the services. In contrast to the existing models, this approach leads to a more realistic representation of the situation in liner shipping. The booking limits resulting from the model can be used to decide whether a booking should be accepted or rejected in favour of a possible later and potentially more beneficial booking. A simulation study is done to test the model for different demand scenarios, which leads to promising results.

Date: 2012
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