prtpy

Python code for multiway number partitioning and bin packing algorithms.

Supports several exact and approximate algorithms, with several input formats, optimization objectives and output formats.

Installation

Basic installation:

pip install prtpy

To run simulation experiments:

pip install prtpy[simulations]

To speed up the ILP code, you can install the GUROBI solver. See the documentation of Python-MIP for more information.

Usage

The function prtpy.partition can be used to activate all number-partitioning algorithms. For example, to partition the values [1,2,3,4,5] into two bins using the greedy approximation algorithm, do:

import prtpy
prtpy.partition(algorithm=prtpy.partitioning.greedy, numbins=2, items=[1,2,3,4,5])

To use the exact algorithm based on ILP, and maximize the smallest sum:

prtpy.partition(algorithm=prtpy.partitioning.ilp, numbins=2, items=[1,2,3,4,5], objective=prtpy.obj.MaximizeSmallestSum)

Similarly, the function prtpy.packing can be used to activate all bin-packing algorithms.

For more features and examples, see:

1. Number-partitioning algorithms;
2. Bin-packing algorithms;
3. Bin-covering algorithms;
4. Input formats;
5. Optimization objectives;
6. Output formats.

Adding new algorithms

To add a new algorithm for number partitioning, write a function that accepts the following parameters:

• binner - an item of class Binner structure (see below).
• numbins - an integer - how many bins to put the items into.
• items - a list of item-names (the item values are given by the function binner.valueof).
• Any other parameters that are required by your algorithm.

For an example, see the implementation of existing algorithms, e.g. greedy.

To add a new algorithm for bin packing or bin covering, write a function that accepts the following parameters:

• binner - an item of class Binner structure (see below).
• binsize - the capacity of a bin (maximum sum in bin-packing; minimum sum in bin-covering).
• items - a list of item-names (the item values are given by the function binner.valueof).
• Any other parameters that are required by your algorithm.

For an example, see the implementation of existing algorithms, e.g. first_fit.

The Binner class contains methods for handling bins in a generic way --- handling both item-names and item-values with a single interface. The main supported methods are:

• bins = binner.new_bins(numbins) --- constructs a new array of empty bins.
• binner.add_item_to_bin(bins, item, index) --- updates the given bins array by adding the given item to the bin with the given index.
• binner.sums(bins) --- extracts, from the bins array, only the array of sums.
• bins = binner.add_empty_bins(bins, numbins) --- creates a new bins array with some additional empty bins at the end.
• bins = binner.remove_bins(bins, numbins) --- creates a new bins array with some bins removed at the end.
• binner.valueof(item) --- returns the value (size) of the given item.

Related libraries

• numberpartitioning by Søren Fuglede Jørgensen - the code for complete_greedy and complete_karmarkar_karp was originally adapted from there.
• binpacking by Ben Maier.

Limitations

The package is tested on Python versions 3.8, 3.9 and 3.10. Other versions are not supported.