Loading Stability Benchmark for Pallet Loading Problems

The dataset contains information about approx. 28.000 pallet loading problem (PLP) cargo layouts derived from a physical simulation with the MSC ADAMS software. The question of the study is to compare and benchmark four static stability algorithms: full base support, partial base support, static mechanical equilibrium, and physical simulation. As there are no real-world cargo loading datasets available, we decided to approximate real-world loading. We used a multibody simulator (MSC ADAMS) and simulated cargo loadings for a set of layouts. The benchmark simulation was adjusted in an iterative process, in which we visually tested simulations and incorporated insights from literature about physical cargo properties until we were satisfied with the plausibility of the results. However, the benchmark simulation depends on many parameters that need to be adjusted, and our adjustments might be unprecise. 
The dataset contains 3 sub-datasets: Dataset 1 is based on the ACLPP instances generated by Brandt & Nickel (2019). Dataset 2 is a random re-assignment of Dataset 1. Dataset 3 is based on the instances from Ali et al. (2024). For every sub-dataset, we assembled three complexity scenarios with four stages. Scenario 1 is the easiest scenario, which assumes all items (boxes) have uniform density and no displaced center of mass (in relation to their geometric center). Scenario 2a moves the CoM now to a random position in the items' dimensions according to a Gaussian distribution around the item's geometric center. Scenario 2b now applies the same procedure as Scenario 2a but employs a uniform distribution.
Stage 1 ("input") are the input cargo items with itemLabel, weight, (box)-shape with width, height, depth, loading coordinate (x,y,z), sequence, and center of mass (x, y, z). Stage 2 ("done_raw") is the raw output of our physical simulation, which tracks relevant physical characteristics such as angular momentum, angular velocity, acceleration, position, velocity (about the center of mass), translation, rotation, and contact forces with other items. We measured multiple observations per loading sequence, which all have an assigned time step. The total simulation length is 0.3 s. Stage 3 ("done_intermediate") now filters the raw data, such that we track the largest translation and rotation in x, y, and z- directions per loading step per item. We also calculate the Euclidean translation and rotation by comparing the last timestep's position and rotation to the initial position and rotation. Finally, Stage 4 ("done") transforms the intermediate steps into a final quantified stability outcome, in case any item exceeds the threshold values for translation and rotation. The final outcome is normalized, such that 1 represents a stable cargo layout and 0 represents a layout in which the first item is unstable. Divide the number of stable loading steps by the total number of items in the cargo layout.