This readme file was generated on 2026-02-20 by Florian Möhle


GENERAL INFORMATION

Title of Dataset: Scenario 1, Scenario 2, Scenario 3, Scenario 4, Scenario 5, Scenario 6, Scenario 7, Dataset of Simulated and Approximated Logistic Operating Curves for Closed Queueing Systems at Seaport Container Terminals

Author/Principal Investigator Information
Name: Florian Möhle
ORCID: https://orcid.org/0009-0007-3444-8195
Institution: Institute of Production Management and Technology (IPMT), Hamburg University of Technology (TUHH)
Address: Denickestraße 17, 21073 Hamburg, Germany 
Email: florian.moehle@tuhh.de

Date of data collection: 2026-02-19

Geographic location of data collection: Hamburg, Germany

Information about funding sources that supported the collection of the data: Deutsche Forschungsgemeinschaft (DFG) (508626914)


SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: Public Domain Mark 1.0 Universal


DATA & FILE OVERVIEW

File List:	Scenario 1.csv to Scenario 7.csv: Raw parameter files describing the experimental setup and simulation constraints for each of the seven scenarios.

		Dataset of Simulated and Approximated Logistic Operating Curves for Closed Queueing Systems at Seaport Container Terminals.csv: Collected data containing both simulated 		results and analytical approximations for the logistic operating curves across all scenarios.

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:
The data was generated using discrete-event simulations of closed queueing systems, specifically modeling the interaction between terminal trucks (TTs) and parallel ship-o-shore cranes (STS) at a seaport container terminal. The study focuses on "Logistic Operating Curves", which describe the relationship between mean Work-in-Process (WIPO), the number of TTs and performance metrics like STS productivity.

Methods for processing the data: The raw simulation outputs (PRO Sim) were compared against analytical approximations (PRO Approx) based on two different mathematical models (referred to as Formula 8 and Formula 9 in the dataset, see publication). Deviations between the simulation and the approximations were calculated to validate the accuracy of the models.

Instrument- or software-specific information: The data was generated using simulation software (discrete-event simulation environment, salabim.org, python) utilizing triangular distributions for handling times. Files are provided in CSV format with semicolon (;) delimiters.

Experimental conditions: The scenarios vary based on the number of parallel cranes (4, 8, or 12), number of TTs (1 - seven times the number of parallel cranes), mean handling times for cranes and trucks, and the coefficients of variation for these times. Each simulation run processed 39,000 transport orders (TOs).


DATA-SPECIFIC INFORMATION FOR: Scenario 1.csv - Scenario 7.csv
Number of variables: 15 (parameters)

Number of cases/rows: 21

Variable List:

no. of trucks [-]: Range of terminal trucks (min, max, step).

no. of parallel cranes [-]: Number of active cranes.

runs: Number of simulation iterations.

no. of simulated TOs per Run [-]: Transport orders simulated per run.

orders sequencing: Logic used for order processing (e.g., First unloading, then loading).

mean crane handling time [s]: Average time for crane operations without waiting.

mean truck handling time [s]: Average time for truck operationswithout waiting.

coefficient of variation [-]: Statistical variability for crane and truck times.

ideal minimum WIP / no. of trucks: Theoretical performance benchmarks.

DATA-SPECIFIC INFORMATION FOR: Dataset of Simulated and Approximated Logistic Operating Curves for Closed Queueing Systems at Seaport Container Terminals.csv

Number of variables: 7 per scenario block (Total 18 columns including separators).

Number of cases/rows: 259 (containing data for all 7 scenarios in blocks).

Variable List:

Number of TTs in Sim: Independent variable representing the number of terminal trucks in the system.

Mean WIPO [-]: Mean Work-in-Process (in number of Transport orders).

PRO Approx (8) [-/h]: Productivity calculated using Approximation Formula 8 (see publication) (moves/hour).

PRO Approx (9) [-/h]: Productivity calculated using Approximation Formula 9 (see publication) (moves/hour).

PRO Sim [-/h]: Productivity observed in the simulation (moves/hour).

Deviation (8) [-/h]: Difference between simulated and approximated productivity (Formula 8).

Deviation (9) [-/h]: Difference between simulated and approximated productivity (Formula 9).

Missing data codes: NaN or empty cells represent separators between scenario blocks or lack of data for specific configurations.