Using Markov Chains to analyze days delay due to port congestion

William Groeneveld, Austin Kana, Jeroen Pruijn

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientific


Days delay due to port congestion are the number of days delay a ship undergoes before being able to make use of the port’s infrastructure. The prediction of days delay falls in the field of queuing theory. Queuing predictions are done by modelling queuing theory. Queuing theory modelling consists of simulating each individual operation based on stochastics. Drawbacks of the stochastics lie in the distribution of probabilities. Certain parameters are obtained from a distribution which does not take sequences and trends into account. This paper investigates the suitability on predicting days delay in the bulk shipping industry due to port congestion using a Markov Chain Analysis (MCA). A MCA predicts a future situation from the current situation based on historical data. Currently MCA’s are not yet widely combined with shipping and shipbuilding. In other industries MCA’s are widely applied in weather models, stock pricing and costs of repairs. The data obtained from this research concerns with measuring costs for decision-making purposes.
Original languageEnglish
Title of host publicationConference WCTRS, Special Interest Group 2, 3-4 May, 2018, Antwerp, Belgium
Number of pages11
Publication statusPublished - 2018
EventSiga2 2018 Conference 'Maritime and Ports': The Port and Maritime Sector: Key Developments and Challenges - University of Antwerp, Antwerp, Belgium
Duration: 3 May 20184 May 2018


ConferenceSiga2 2018 Conference 'Maritime and Ports'
OtherTwo-day international conference organized by:
The Special Interest Group A2 (Ports and Maritime) of the World Conference on Transport Research Society (WCTRS)
Internet address


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