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.