Published August 28, 2023 | Version v2
Dataset Open

Container Logistics Object-centric Event Log

  • 1. Chair of Process and Data Science, RWTH Aachen


  • 1. Chair of Process and Data Science, RWTH Aachen


General Description

Our company sells goods overseas. After receiving an order, the shipment of goods is scheduled. According to this schedule, the goods are picked up from the local production site and brought to a terminal where a logistics service provider receives and ships them.

This is an artificial event log according to the OCEL 2.0 Standard simulated using CPN-Tools. Both the CPN and the SQLite can be downloaded. 

Process Overview

From a customer order perspective, the process begins when the order is registered at our company (register customer order). After registration, a transport document is created in which details of the further process are recorded (create transport document).

Using this information, the logistics service provider is contacted to coordinate the transport of the ordered goods to the seaport. Twice a week, that provider sends a vehicle to a terminal, with a limited capacity for containers of ordered goods to be transported from the terminal to a seaport. For our company, available capacties vary from vehicle to vehicle, as we are not the only company booking spots. Once the logistics service provider receives our transport documents, they book capacities according to availability and container prioritizations in the upcoming weeks (book vehicles). Once the dates for transporting the goods to the terminal are set, our company contacts a container depot to reserve the required containers (order empty containers).

When a container’s vehicle departure approaches, the goods are prepared, packed and shipped to the terminal. For this purpose, a truck is sent to the container depot (pick up empty container). Meanwhile, the ordered goods to be shipped are packed into handling units at the production site. After loading the handling units (load truck), the truck drives the full container to the terminal (drive to terminal).

At the terminal, the container is picked up by a free forklift and weighed (weigh). Unless the vehicle departure is imminent, the container is placed in the storage location at the terminal (place in stock). Finally, it is moved to the vehicle (bring to loading bay, load to vehicle) which departs at a fixed time (depart).

Despite careful planning, containers sometimes miss a vehicle’s departure. In this case, the container is rescheduled to the next possible vehicle (reschedule container) and kept near the loading ramp until then.

Further information can be found at:

General Properties 

An overview of log properties is given below.

Property Value
Event Types 14
Object Types 7
Events 35761
Objects 14013

Control-Flow Behavior 

The behavior of the log is described by a respective object-centric Petri net. Also, individual object types exhibit behavior that can be described by simpler Petri nets. See below.

Container Transport Documents
Customer Order Truck
Forklift Vehicle
Handling Unit  


Object Relationships 


During the process, object-to-object relations can emerge at activity occurrences as follows.

Activity Source Object Type Target Object Type Qualifier
Create Transport
Customer Order Transport Document TD for CO
Book Vehicle Transport Document Vehicle Regular VH for TD
Book Vehicle Transport Document Vehicle High-Prio VH for TD
Order Empty
Transport Document Container CR for TD
Pick Empty
Truck Container TR loads CR
Load Truck Container Handling Unit CR contains HU
Transport Document Vehicle Substitute VH for TD

Simulation Model 

The CPN used to create this event log can also be downloaded.To obtain simulated data, extract the linked ZIP file and play out the CPN therein, e.g., by using CPN Tools.

The play-out produces CSV files according to the schema of OCEL2.0. This Python notebook can be used to convert these files to an SQLite dump.

For a technical documentation of the simulation model, please open the attached CPN with CPN Tools and see the annotations therein.


Funded under the Excellence Strategy of the Federal Government and the LänderFunded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2023 Internet of Production - 390621612. We also thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.



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