Biopharma Scheduling

Work in progress...

• Introduction
• Setup
  • Docker
  • macOS
  • Ubuntu 16.04 LTS
• 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
  

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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
  

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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

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