Load Building Benchmarks - ORTEC Scientific Benchmarks

Load Building Benchmark Sets

The problem of load building involves placing items, boxes, and pallets into loading spaces of containers according to some constraints, and in an effort to minimize a certain objective. A variety of constraints and objectives are available to create an assortment of interesting problems, and more will be added in the future. You are allowed to ignore certain constraints; which constraints are adhered to in your solution will be determined after uploading. Instances are divided into sets based on what kind of problem is considered in that particular set. The data files of the instances are available in JSON, XML, and YAML formats. A thorough specification of these file formats is available in the documentation provided below.

Documentation
Documentation is available for the instance file structure, the solution file structure, and the provided tools.

Instance Solution Tools

Tools
Several tools are available to get started with the given instances. All tools are written in and tested for Python 3.5 and above. The tools can be either downloaded using the pip-command given below (recommended), or by manually downloading and installing the tools from Github.

pip install ortec.scientific.benchmarks.loadbuilding

Tools (Github) Tools (zip)

Download JSON Download XML Download YAML

Transport by Rail

Many items of different lengths are to be shipped by train. There are two types of railcar and of both of these there are only a limited number to use. The goal is to minimize the number of required railcars to ship all items such that items may only be stacked if their lower and upper faces match exactly.

Used objectives: Container Count

Used constraints: Exact Stacking Must Place Orientation
Number of instances: 10
Download JSON Download XML Download YAML

Efficient Fill Rate

One of the tasks a certain company performs is the shipment of many small, light items in gitterboxes (stackable wire containers). The goods are to be separated per gitterbox, and weights are not an issue, and so the goal is to maximize the number of items of a single item type in a single gitterbox. Due to the high density of good loading patterns, the support constraint is not explicitly stated.

Used objectives: Item Count

Used constraints: Orientation
Number of instances: 101
Download JSON Download XML Download YAML

Heavy Palletized Items

This problem involves the loading of heavy items into containers. As the weight capacity is the limiting factor in this case, support constraints are explicitly stated. The pallets are preloaded and so they are modeled as items.

Used objectives: Container Count

Used constraints: Maximum Weight Must Place Orientation Support
Number of instances: 21

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