Biopharma Scheduling

Work in progress...

Introduction
Setup
Examples

Introduction

This is a genetic algorithm (GA) based optimisation approach for medium-term capacity planning and scheduling of multi-product biopharmaceutical facilities using a continuous-time representation. The continuous-time model is implemented by utilising a variable-length chromosome structure capable of adapting to the problem by growing in length from a single gene corresponding to a production campaign in a manufacturing schedule.

This approach has been presented during a keynote lecture at the 27th European Symposium on Computer Aided Process Engineering (ESCAPE):

Jankauskas, K., Papageorgiou, L. G., & Farid, S. S. (2017). Continuous-Time Heuristic Model for Medium-Term Capacity Planning of a Multi-Suite, Multi-Product Biopharmaceutical Facility. In Computer Aided Chemical Engineering (Vol. 40, pp. 1303-1308). Elsevier. DOI: 10.1016/B978-0-444-63965-3.50219-1.

Setup

Docker (recommended option)

Download and install docker >= docker version 17.12.0
For Windows 10 users:
- docker supports only Windows 10 Professional and Enterprise editions. Also, switch to using Linux containers
- For other Windows 10 editions, a Linux Subsystem can be installed to either install a Linux version of docker or build biopharma-scheduling from source (see below).

Run the following in the terminal

git clone https://github.com/UCL-Biochemical-Engineering/BiopharmaScheduling
cd BiopharmaScheduling
docker build -t biopharma-scheduling/base -f ./docker/base.docker .
docker build -t biopharma-scheduling/lab -f ./docker/lab.docker .

macOS

Install brew

Install the necessary build tools

brew update && brew install coreutils && brew install gcc --without-multilib

Install anaconda

Create and activate virtual Python environment

conda create -n <environment-name> python=3.5
source activate <environement-name>

Install Python libraries

python -m pip install -r requirements.txt

Find the path to the g++ binary with brew ls gcc | grep g++. It should be in
```
/usr/local/Cellar/gcc/<version>/bin/g++-<version>
```

Export the path to the g++ binary

export CC=<path to g++ binary> && export CXX=<path to g++ binary>

Compile and install the biopharma-scheduling

git clone https://github.com/karolisjan/BiopharmaScheduling.git
cd BiopharmaScheduling
python setup.py
pip install dist/*whl

Ubuntu 16.04 LTS

Install the essentials first

sudo apt-get update && sudo apt-get install build-essential software-properties-common -y 
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y 
sudo apt-get update && sudo apt-get install gcc-snapshot -y 
sudo apt-get update && sudo apt-get install gcc-8 g++-8 -y
sudo apt-get install git python-dev python3-dev python-pip python3-pip python-wheel python3-wheel python-virtualenv

Create and activate virtual Python environment

virtualenv -p python3 ~/<environment-name>
echo "alias <environment-name>='source ~/<environment-name>/bin/activate'" >> ~/.bash_aliases
source ~/.bash_aliases
<environment-name>

Install Python libraries

python -m pip install -r requirements.txt

Export the path to the g++ binary
```
export CC=g++-8 && export CXX=g++-8
```

Compile and install the biopharma-scheduling

git clone https://github.com/UCL-Biochemical-Engineering/BiopharmaScheduling
cd BiopharmaScheduling
python setup.py
pip install dist/biopharma_scheduling-1.0-cp35-cp35m-linux_x86_64.whl

Examples

Using docker

docker run -it -p 8888:8888 -v <absolute path to BiopharmaScheduling folder>:/BiopharmaScheduling biopharma-scheduling/lab bash -c "jupyter lab --ip 0.0.0.0 --no-browser --allow-root"

Go to localhost::8888/?token=<token ID>and navigate to examples folder

Using Jupyter Lab
- Setup the ipykernel for the environment created earlier
```
python -m ipykernel install --user --name <environment-name> --display-name "<display-name>"
```
- Create and activate a separate Python enviroment, and run pip install jupyter jupyterlab inside it
- Install Node.js
- Setup Plotly extension with jupyter labextension install @jupyterlab/plotly-extension
- Launch jupyter lab and navigate to examples folder