Military maritime load planning instances for prioritized two-dimensional orthogonal packing

This dataset supports the computational validation of prioritized
two-dimensional orthogonal packing algorithms applied to military combat
loading scenarios, as detailed in "Enhancing Military Load Planning:
A Prioritized 2-D Orthogonal Packing Approach". The repository
comprises 70 problem instances derived from authoritative U.S. Army
equipment databases, specifically the Joint Equipment Characteristic
Database (JECD) and the Modified Table of Organizational Equipment (MTOE)
for a representative Armored Brigade Combat Team (ABCT). The data
represents approximately two brigades of equipment filtered for
roll-on/roll-off capability—including tracked combat vehicles,
self-propelled artillery, and heavy wheeled vehicles—to simulate realistic
amphibious embarkation requirements. Instances are provided in JSON format
and categorize items by Unit Identification Code (UIC) and Paragraph
Number (PARNO) to enforce hierarchical packing priorities that balance
access-point proximity with unit cohesion. The dataset spans six
representative vessel classes (Whidbey Island, Wasp, Harpers Ferry,
Besson, America, and Runnymede) with target space utilization levels
ranging from 65% to 85%. Supplementary Julia scripts utilizing the Gurobi
optimizer are included to reproduce computational experiments across three
solution methods: a monolithic Mixed-Integer Linear Program (MILP), a
standard sliding-window matheuristic, and an in-stride balancing variant.
These scripts evaluate algorithmic performance against strict
center-of-gravity deviation tolerances (δ∈{0.01,0.05,0.10,0.15}), enabling
the assessment of trade-offs between load balancing feasibility, solution
quality, and computational efficiency.