1. RESEARCH CONTEXT AND OBJECTIVES
This dataset contains the raw computational results and discrete-event simulation logs generated during the operational evaluation of internal transport modernization. The study analyzes the integration of an Autonomous Mobile Robot (AMR) combined with Lean Logistics principles within an active industrial manufacturing facility.
The practical context of the data is rooted in the production ecosystem of the Schneider Electric Elda SA plant located in Szczecinek, Poland. The specific focus of the investigation is the intralogistics process of collecting finished goods from the factory's final packaging lines and transporting them to the shipping zone.
The primary objective of compiling these data points was to experimentally validate a newly developed AMR-assisted transport architecture designed to decouple finished goods transport from component delivery. The simulation tests the hypothesis of whether replacing traditional manual transport loops with a single, automated AMR unit can safely mitigate existing traffic congestion, eliminate non-value-adding operational waste (muda), and support future throughput scaling.
2. METHODOLOGY AND DATA GENERATION
The data generation framework utilized a quantitative and experimental scientific approach structured as follows:
1. Baseline Diagnosis: Operational data from the physical Schneider Electric Elda SA facility was gathered. A quantitative red-green waste assessment was applied to identify internal logistics capacity bottlenecks and manual handling inefficiencies.
2. Digital Twin Modeling: A discrete-event simulation (DES) model was built using the FlexSim 2026 environment to replicate the spatial layouts, transport paths, and timing parameters of the packaging department.
3. AMR Logic Integration: Kinematic behaviors of the AMR fleet (including acceleration profiles, localized path routing speeds, loading intervals, and battery constraints) were programmatically embedded into the model execution.
4. Simulation Runs & Logging: The system was executed under nominal stressed workloads. Raw performance data measuring transport distances, state allocations, station behaviors, and physical output units were systematically recorded and exported directly into tabular formats without statistical filtering. The findings prove a total execution time reduction and an overall internal transport efficiency enhancement of 8.3%.
3. DATASET STRUCTURE AND FILE DESCRIPTIONS
The dataset is composed of 6 interrelated comma-separated values (.csv) files representing distinct operational dimensions of the automated factory setup:
* AGV_dist.csv
Contains raw logging of the cumulative travel distances covered by AGV.
* AGV_Path.csv
Positional routing logs map out the specific spatial paths, node interactions.
* AGV_State.csv
Time-series profile capturing the exact operational state allocations of the robot. It records the chronological breakdown of time spent in active transport (Travel Loaded, Travel Empty), operational tasks (Loading, Unloading), and systemic delays (Charge, Idle).
* Output_pallets.csv
Throughput log of finished product pallets successfully cleared from the packing lines and safely delivered to the wearhouse.
* Stae_stations.csv
Operational data tracking the behavioral states, downtime, and material accumulation profiles of the physical packing stations serving as the source locations for the AMR.
* Time_parameters.csv
Comprehensive breakdown of cycle times of each station.
(2027-07)
