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Analysis of the Suez Canal blockage with queueing theory
Citation Link: https://doi.org/10.15480/882.3967
Publikationstyp
Conference Paper
Date Issued
2021-12-01
Sprache
English
Herausgeber*innen
Institut
TORE-DOI
First published in
Number in series
31
Start Page
943
End Page
959
Citation
Hamburg International Conference of Logistics (HICL) 31: 943-959 (2021)
Contribution to Conference
Scopus ID
Publisher
epubli
Peer Reviewed
true
Purpose: The Suez Canal blockage in March 2021 delayed around $9.6bn of trade each day. The delay affected more than 400 vessels and likely disrupted further Supply Chain and transport operations even after clearing the blockage.
Methodology: The model of this paper has two goals: first, predicting how long the queued-up vessels need to wait until continuing their voyage; second, at what time the entire queue resolves, and a new service cycle continues with steady-state behaviour.
Findings: The model predicted that the queued vessels' behaviour, i.e., that the last ship will pass the canal five days after the clearing, which equals the number reported by the Suez Canal Authorities. AIS-data can further validate the model's input and output. The discussed model supports the decision-making processes by proving the tool to assess at what time circumventing the blockage is more beneficial.
Originality: Supply Chain Management literature already established models from Queueing Theory to evaluate the efficiency of services and infrastructure. However, the literature does not use queueing models to assess Supply Chain risk. This research introduces a queueing model to Supply Chain Risk Management to analyse the recovery of a disrupted transport route, thereby forecasting delays caused by disrupted transport routes.
Methodology: The model of this paper has two goals: first, predicting how long the queued-up vessels need to wait until continuing their voyage; second, at what time the entire queue resolves, and a new service cycle continues with steady-state behaviour.
Findings: The model predicted that the queued vessels' behaviour, i.e., that the last ship will pass the canal five days after the clearing, which equals the number reported by the Suez Canal Authorities. AIS-data can further validate the model's input and output. The discussed model supports the decision-making processes by proving the tool to assess at what time circumventing the blockage is more beneficial.
Originality: Supply Chain Management literature already established models from Queueing Theory to evaluate the efficiency of services and infrastructure. However, the literature does not use queueing models to assess Supply Chain risk. This research introduces a queueing model to Supply Chain Risk Management to analyse the recovery of a disrupted transport route, thereby forecasting delays caused by disrupted transport routes.
Subjects
Supply Chain Risk Management
Supply Chain Security
DDC Class
330: Wirtschaft
380: Handel, Kommunikation, Verkehr
Publication version
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