On a yearly basis, a huge number of goods is traded internationally and declared at Customs. Accordingly, there are large incoming and outgoing message flows that need to be processed. At the Dutch Customs, this mostly takes place electronically. In nowadays' global economy that is running 24 hours a day, 7 days a week, properly and continuously working systems have become fundamental. As such, the presence of constantly available automated systems used to handle declaration processes is highly important for Customs and businesses.
The Dutch Customs strives after a situation in which maintenance of its automated systems can be planned in such a way that inconveniences for businesses are minimized as much as possible. Inconveniences for businesses result from unavailability of Customs' systems, which is caused by planned maintenance or failures leading to unplanned maintenance.
Accordingly, Customs examined the most appropriate maintenance policy for planned maintenance at Customs' primary automated systems used for the different declaration processes, such that inconveniences for businesses are minimized. Here, the study focused on the Entry procedure and investigated the maintenance policy in terms of the timing of maintenance, i.e. the moment during which planned maintenance is initiated as well as the duration and frequency of planned maintenance. In addition, the effect of the way in which capacity is assigned was considered.
This was examined by building a Monte Carlo simulation model, such that various situations and scenarios related to the timing of maintenance could be considered by changing both single and multiple parameters. The objective function was to minimize the average total number of buffered messages.
In order to be able to run the simulation model, it was needed to find a best fitting distribution for several elements within the model. This was done with the help of Easyfit: based on the Kolmogorov-Smirnov Goodness of Fit test, the Gamma distribution was used for sample data related to declarations and message flows, the Weibull distribution was used for sample data related to the duration of unplanned maintenance, and the Burr distribution was used for sample data related to the Time between Failures.
Based on the results of this study, a follow-up study is currently being executed. Here, the newly developed simulation model acts as a framework that can be adjusted to find the most appropriate maintenance policy for planned maintenance in terms of the timing of maintenance for other procedures, i.e. Import, Export, Exit, Transit, and transport of Excise Goods as well.