Problem instances for the two-dimensional bin packing problem with multiple levels of prioritization

This repository contains a collection of 90 problem instances for the
two-dimensional bin packing problem with multiple levels of prioritization
(2D-BPP-P). These instances were generated to support research that
integrates spatial optimization with traditional bin packing, a scenario
where the arrangement of items is as critical as the efficiency of the
packing itself. Dataset Structure and Contents: The dataset comprises 90
unique, computationally generated problem instances. Each instance defines
a set of rectangular items to be packed into a single, larger rectangular
bin with a designated access point. The data for each instance includes:
Bin Dimensions: The length (L) and width (W) of the single bin. The bin
length is intentionally extended to provide sufficient space for
prioritization-based layouts, moving beyond simple space minimization.
Item characteristics: For each rectangular item, the dataset specifies its
length, width, and group affiliation. Items may be rotated by 90 degrees.
Item dimensions are generated to reflect realistic aspect ratios (1:1 to
1:3), analogous to military vehicles or varied package sizes. Group
Structure: Items are organized into 1 to 5 groups, with group sizes
categorized as small (1–4 items), medium (5–8), or large (9–12). Group
item dimensions are classified as homogeneous, weakly heterogeneous, or
strongly heterogeneous. Prioritization Scheme: Each item is assigned a
group-level priority and an item-level priority within its group. This
two-tiered system allows for the modeling of complex operational
requirements, such as maintaining cohesion among functionally related
items while ensuring high-priority items are positioned near a bin access
point. The raw priority data is provided, from which a full prioritization
matrix can be constructed to weigh the objective function in optimization
models. Reuse Potential: This dataset is intended for researchers and
practitioners in operations research, industrial engineering, computer
science, logistics, and other fields. The instances are useful for
benchmarking and developing new solution methodologies for combinatorial
optimization problems that blend packing and facility layout concepts.
Potential applications include: Validating and comparing the performance
of exact algorithms, heuristics, and metaheuristics for spatial
optimization problems. Studying the trade-offs between space utilization
and operational priorities in logistics applications such as military
combat loading. Extending the problem to multi-bin scenarios, or
incorporating additional real-world constraints such as load balancing or
non-adjacency requirements. Legal and Ethical Considerations: The data is
synthetically generated and does not contain any sensitive, confidential,
or proprietary information. There are no legal or ethical restrictions on
its use. The authors encourage its reuse and dissemination for academic
and research purposes, with appropriate citation to the associated
article(s).