cutlet

cutlet - Panel Cut Optimization

not production ready

cutlet is my naiive approach on guillotine panel cut optimization, primarily concerned with cutting panels from stock sheets of wood.

There are many commercial solutions available, and quite some Open Source implementations.

This project is implemented in Java 1.8, using Maven. Gui is in JavaFX/FXML.

If you want to contribute - please do so, any help would be appreciated.

If you want to use this project - please do so, I would be happy to hear how it is working for you, and what features are missing.

Overview

There are 3 projects:

Lib
Gui
Benchmark

The library implements the data structures and algorithms. Gui will become... well... the GUI. Benchmark runs examples and compares them.

A goal was to implement a solid data structure to provide a good framework for research / surveys.

Goals

Platform agnostic
easy "getting started" and deployment
Java 1.8, Maven, JavaFX
built around reusable core library
build solid data structure
optimization algorithm agnostic / easy to add new algorithms
open source
enable meaningful evaluation of optimization algorithms
translatable gui

Implementation status

This project is in it's very early stages, so don't expect too much.

Lib

Gui

Benchmark

basically works

It is executed as a unit test in "mvn test", also in travis. You can have a look at to see it's output.

Example output:

Method     dataset          #panels    #sheets     waste%      #cuts  cutlength    fitness
Smart      Data1                  7          1       58.8         12       3378    16214.4
Smart      Random_100            15          2       67.0         29       7651    73449.6
Smart      Random_201            14          2       73.1         28       9014    86534.4
Smart      DataRegal             40          3       22.9         60      37040  3472500.0
Smart      Tisch                 17          2       31.6         25      16104   483120.0
Smart      Data1                  0          0        NaN          0          0        0.0
Naiive     Data1                  7          1       58.8         14       4327    20769.6
Naiive     Random_100            15          1       34.0         30       8267    39681.6
Naiive     Random_201            14          1       46.2         28       8929    42859.2
Naiive     DataRegal             40          3       22.9         68      42208  3957000.0
Naiive     Tisch                 17          2       31.6         30      18020   540600.0
Naiive     Data1                  0          0        NaN          0          0        0.0
SA         Data1                  7          1       58.8         12       3629    17419.2
SA         Random_100            15          1       34.0         28       7055    33864.0
SA         Random_201            14          1       46.2         27       7816    37516.8
SA         DataRegal             40          3       22.9         61      38290  3589687.5
SA         Tisch                 17          2       31.6         25      15020   450600.0
SA         Data1                  0          0        NaN          0          0        0.0
SA2        Data1                  7          1       58.8         12       3629    17419.2
SA2        Random_100            15          1       34.0         29       6993    33566.4
SA2        Random_201            14          1       46.2         27       7739    37147.2
SA2        DataRegal             40          3       22.9         63      38374  3597562.5
SA2        Tisch                 17          2       31.6         25      15580   467400.0
SA2        Data1                  0          0        NaN          0          0        0.0
GA         Data1                  7          1       58.8         12       3629    17419.2
GA         Random_100            15          1       34.0         29       7911    37972.8
GA         Random_201            14          1       46.2         27       8300    39840.0
GA         DataRegal             40          3       22.9         67      38542  3613312.5
GA         Tisch                 17          2       31.6         25      16012   480360.0
GA         Data1                  0          0        NaN          0          0        0.0

Implemented optimization algorithms

Simmulated Annealing (based on http://cs.gettysburg.edu/~tneller/resources/sls/index.html)
another SA implementation
a Genetic Algorithm implementation
A naiive single pass algorithm
A less naiive single pass algorithm

TODO

Dependencies

Project	type	description
http://jenetics.io/	maven	genetic algorithm
https://github.com/google/guava	maven	general java stuff
https://pathfinder.readme.io/blog/simmulated-annealing	maven	simulated annealing algorithm
http://cs.gettysburg.edu/~tneller/resources/sls/index.html	source	another sim. ann.

Compile & run

This project uses Maven to manage library dependencies, build & run.

This means: install maven and execute:

mvn test

The code does not compile on openjdk, the dependency lombok does not work. So the ubuntu users out there should install the oracle jdk.

Related Work

https://github.com/Jack000/SVGnest

Simulated Annealing taken from http://cs.gettysburg.edu/~tneller/resources/sls/index.html

Open source implementations

Project	Looks active?	Lang	Comment
COPanno	yes	Java	french source code?
raskroy SF	no	C++ / C#	english c#, some russian
Cut Micro	yes	C#
Cutting Problem		Java

Commercial Products

Project	Looks active?	Lang	Comment
Optimierung		Delphi	1D optimization

mru00/cutlet