qmkpy 1.2.0

Description:

qmkpy 1.2.0

QMKPy: A Python Testbed for the Quadratic Multiple Knapsack Problem






This software package primarily aims at research in the areas of operations
research and optimization.
It serves as a testbed that provides a way of quickly implementing and testing
new algorithms to solve the quadratic multiple knapsack problem (QMKP) and
compare it with existing solutions.
The goal is to encourage researchers and developers to share their algorithms
and make them publicly available.
Problem Description
The QMKP is defined as the following combinatorial optimization problem
max∑u∈K(∑i∈A(u)pi+∑j∈A(u), j≠ipij) s.t.∑i∈A(u)wi≤cu∀u∈K ∑u=1Kaiu≤1∀i∈1,2,…,N
This describes an assignment problem where one wants to assign N∈N
items to K∈N knapsacks, which are described by the index set
K=1,2,…,K.
Item i has the weight wi∈R+ and knapsack u has the weight
capacity cuR+.
If item i is assigned to a knapsack, it yields the (non-negative) profit
pi∈R+.
If item j (with j≠i ) is assigned to the same knapsack, we get the
additional joint profit pij∈R+.
The set of items which are assigned to knapsack u is denoted by
A(u) and aiu∈0,1 is an indicator whether item i is
assigned to knapsack u.
The objective of the above problem is to maximize the total profit such that
each item is assigned to at most one knapsack and such that the weight capacity
constraints of the knapsacks are not violated.
Remark: The profits p are also referred to as "values" in the literature.
Features

Quick and simple creation of QMKP instances
Saving/loading of problem instances for a simple creation and use of
reference datasets
Easy implementation of novel algorithms to solve the QMKP
High reproducibility and direct comparison between different algorithms

The benefit of enabling a simple and direct way of implementing novel
algorithms is highlighted by an example in the provided Jupyter notebook in
examples/Custom
Algorithm.ipynb.

Installation
The package can easily be installed via pip.
Either from the PyPI
pip3 install qmkpy

or from the GitHub repository
git clone https://github.com/klb2/qmkpy.git
cd qmkpy
git checkout dev # optional for the latest development version
pip3 install -r requirements.txt
pip3 install .
pip3 install pytest # optional if you want to run the unit tests

Usage
In order to test the installation and get an idea of how to use the QMKPy
package, you can take a look at the examples/ directory.
It contains some standalone scripts that can be executed and perform some
simple tasks.
More detailed descriptions of the implemented algorithms and a documentation of
the API can be found in the documentation.
A collection of reference datasets can be found at
https://github.com/klb2/qmkpy-datasets.
Contributing
Please see
CONTRIBUTING.md
for guidelines on how to contribute to this project.
In particular, novel algorithms are always welcome. Please check out the
documentation
for a brief overview on how to implement new algorithms for the QMKPy
framework.