Forecasting Booking Behaviour in the Freight Ferry Industry: An Artificial Neural Network Approach

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dc.contributor.authorRänkeskog, Arvid
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerLemurell, Stefan
dc.contributor.supervisorOlsson, Alfred
dc.contributor.supervisorSöyland, Christian
dc.contributor.supervisorJonasson, Johan
dc.date.accessioned2023-06-19T09:20:51Z
dc.date.available2023-06-19T09:20:51Z
dc.date.issued2023
dc.date.submitted2023
dc.description.abstractThe freight ferry industry operates in a stochastic environment where the arrival of vehicles on the departure day is uncertain. This thesis aimed to explore the application of artificial neural networks (ANNs) to predict whether active bookings at a given time before departure are going to become transferred, cancelled, or no-show. The sum of these bookings at a given time before departure was assumed to follow a Poisson binomial random variable. A comparative analysis was conducted between ANNs and Gradient Boosting Trees (LightGBM) trained on structured booking data, such as customer number and check-in status, and recurrent neural networks (RNNs) trained on a sequence of previous cumulative transfer and cancellation rates. The findings revealed that LightGBM was the better option to predict the mean of the distribution, while an ensemble of ANNs demonstrated promising potential in predicting the distribution’s variance and error estimates.
dc.identifier.coursecodeMVEX03
dc.identifier.urihttp://hdl.handle.net/20.500.12380/306282
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectArtificial neural networks, machine learning, poisson-binomial probability distribution, entity embedding, recurrent neural networks, revenue management, overbooking
dc.titleForecasting Booking Behaviour in the Freight Ferry Industry: An Artificial Neural Network Approach
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
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