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Genetic Algorithm for Hybrid Flowshops Scheduling using Python

I apply optimization tool to solve pratical production planning problem.
My current problem is doing planning for a factory with various items in a unique production flow stage. In each stage, there are few parallel machines as graph below.
I have done maths MILP model, and try to solve by CPLEX but it too hard to handle the big scale model by itself.
Currently, I prepare to use Genetic Algorithm to solve it, but don't know where to start.
I have some knowdlege in Python Language. My friends, please advise how I start to deal with this problem?
Do someone have a similar solved problem with code, that I can have a reference?

Before you start writing code(or using someone else's) you must understand the theory behind the scene.
What are the main entities of your Production System?
You need to formulate optimization objectives,optimal schedule but defined in terms of optimization problem.
One example that comes to my mind
https://github.com/jpuigcerver/jsp-ga
Take a look at this thesis
http://lancet.mit.edu/~mbwall/phd/thesis/thesis.pdf

Solving system of linear inequalities in 3 or more variables - Python

I want to solve systems of linear inequalities in 3 or more variables. That is, to find all possible solutions.
I originally found GLPK and tried the python binding, but the last few updates to GLPK changed the APIs and broke the bindings. I haven't been able to find a way to making work.
I would like to have the symbolic answer, but numeric approximations will be fine too.
I would also be happy to use a library that solves maximization problem. I can always re-write the problems to be solve in that way.

make a matrix object and use Crammer's method

ABM under python with advanced visualization

sorry if this all seem nooby and unclear, but I'm currently learning Netlogo to model agent-based collective behavior and would love to hear some advice on alternative software choices. My main thing is that I'd very much like to take advantage of PyCuda since, from what I understand, it enables parallel computation. However, does that mean I still have to write the numerical script in some other environment and implement the visuals in yet another one???
If so, my questions are:
What numerical package should I use? PyEvolve, DEAP, or something else? It appears that PyEvolve is no longer being developed and DEAP is just a wrapper on the outdated(?) EAP.
Graphic-wise, I find mayavi2 and vtk promising. The problem is, none of the numerical package seems to bind to these readily. Is there no better alternative than to save the numerical output to datafile and feed them into, say, mayavi2?
Another option is to generate the data via Netlogo and feed them into a graphing package from (2). Is there any disadvantage to doing this?
Thank you so much for shedding light on this confusion.

You almost certainly do not want to use CUDA unless you are running into a significant performance problem. In general CUDA is best used for solving floating point linear algebra problems. If you are looking for a framework built around parallel computations, I'd look towards OpenCL which can take advantage of GPUs if needed..
In terms of visualization, I'd strongly suggest targeting a a specific data interchange format and then letting some other program do that rendering for you. The only reason I'd use something like VTK is if for some reason you need more control over the visualization process or you are looking for a real time solution.

Probably the best choice for visualization would be to use an intermediate format and do it in another program. But for performance, i'd rather configure a JVM for a cluster and run NetLogo on it. I've not tried it yet but i'm thinking seriously to try NetLogo on a Beowulf style cluster.
BTW, there is an ABM platform called Repast that is said to have Python interface if you're planning to implement your code in Python.

Nash equilibrium in Python

Is there a Python library out there that solves for the Nash equilibrium of two-person zero-games? I know the solution can be written down in terms of linear constraints and, in theory, scipy should be able to optimize it. However, for two-person zero-games the solution is exact and unique, but some of the solvers fail to converge for certain problems.
Rather than listing any of the libraries on Linear programing on the Python website, I would like to know what library would be most effective in terms of ease of use and speed.

Raymond Hettinger wrote a recipe for solving zero-sum payoff matrices. It should serve your purposes alright.
As for a more general library for solving game theory, there's nothing specifically designed for that. But, like you said, scipy can tackle optimization problems like this. You might be able to do something with GarlicSim, which claims to be for "any kind of simulation: Physics, game theory..." but I've never used it before so I can't recommend it.

There is Gambit, which is a little difficult to set up, but has a python API.

I've just started putting together some game theory python code: http://drvinceknight.github.com/Gamepy/
There's code which:
solves matching games,
calculates shapley values in cooperative games,
runs agent based simulations to identify emergent behaviour in normal form games,
(clumsily - my python foo is still growing) uses the lrs library (written in C: http://cgm.cs.mcgill.ca/~avis/C/lrs.html) to calculate the solutions to normal form games (this is I believe what you want).
The code is all available on github and that site (the first link at the beginning of this answer) explains how the code works and gives user examples.
You might also want to check out 'Gambit' which I've never used.

Simulation of molecular dynamics in Python

I am searching for a python package that I can use to simulate molecular dynamics in non-equilibrium situations. I need a setup that can handle a fairly large number of molecules in a primarily kinetic theory manner, and that can handle having solid surfaces present. With regards to the surfaces, I would need to be able to create arbitrary shapes and monitor pressure and other variables resulting from the molecular action. Alternatively, I could add the surface parts myself if I had molecules that could handle it.
Does anyone know of any packages that might be suitable?

Have you considered SimPy? SimPy is a rather generic Discrete Event Simulation package, but could feasibly meet your needs.
Better yet the Molecular Modelling ToolKit (MMTK) seems more specialized...
I have used neither, but this sounds like fun. Python, as a language, seems to be in privileged position for use in simulation software, whereby people can script the specific details of their model while relying on the framework for all the common logic, such as scheduling, visualization, monitoring etc. The unknown is how well such toolkits scale when fed with agent counts commensurate with biology models (BTW, how "big" is that?)

Lampps and gromacs are two well known molecular dynamics codes. These codes both have some python based wrapper stuff, but I am not sure how much functionality the wrappers expose. They may not give you enough control over the simulation.
Google for "GromacsWrapper" or google for "lammps" and "pizza.py"
Digital material and ASE are two molecular dynamics codes that expose a lot of functionality, but last time I looked, they were both fairly specialized. They may not allow you to use the force potentials that you want
Google for "digital material" and "cornell" or google for "ase" and dtu
Note to MJV: Normal MD-codes take one time step at a time, and they move all particles in each time step. Most of the time is spend calculating the total force on each atom. This involves iterating over a list of pairs of neighboring atoms. I think the best idea is to do the force calculation and a few more basics in c++ or fortran and then wrap that functionality in python. (But it could be fun to see how far one can get by using numpy matrices)

The following programs can be used to run MD symulations:
Gromacs
AMBER
charmm
OpenMM
many others...
The following Python packages are useful for preparing and analysing MD trajectories:
MDtraj and the OMNIA ecosystem
MDAnalysis
ProDy
MMTK

Another generic simulations framework is my own GarlicSim. You can try that. I could help you get a simpack up if you're serious about it.

I don't know if that programs does all the features you need but there is avogadro in the kde programs, i think it is extendable and since it is open source you could do anything with it. http://www.kde-apps.org/content/show.php/Avogadro?content=59521
It is really advanced and programmed by a friend of mine

I second MMTK, but take a look at VMD, which is the best MD software I'm aware of, and is Python-scriptable (in addition to Tk). See this for examples and tutorials.

I recommend to use molecular dynamics software to run MD simulations like Gromacs. This software is highly optimized for that particular purpose. You can also run on GPU's and you will be able to run larger systems in less time.
Afterwards, you run only the analysis with python packages using the generated trajectories.
mdtraj
pmx