Part 4: Optimizing schedules, allocations and plans through multiple programming models with Nerav Doshi
Learn to use tools to develop DO models - There are plenty of tools on the market to develop and solve optimization models, including some open source ones. IBM offers two ways to start learning DO at no cost: Decision Optimization for Watson Studio (DO for WS) and CPLEX Optimization Studio (COS).
Watson Studio makes it possible to use DO in Jupyter notebooks (docplex) and in a dedicated model builder (docplex and OPL). It can be used with for free for some limited duration as part of the Trial plan.
- Blog post on starting to use DO for WS Python notebooks
- Demo collection for DO in WS, videos, product tours and hands-on lab from Nerav Doshi, Digital Technical Engagement at IBM.
- Experiment with docplex API – Reserve an instance
- Getting Started with Decision Optimization on Watson Machine Learning
- Finding optimal locations of new store using Decision Optimization
- Maximizing the profit of an oil company
- Predictive Maintenance Optimization
- The Nurse Assignment Problem
- The Unit Commitment Problem (UCP)
CPLEX Optimization Studio (COS) can be very easily downloaded and installed on your laptop.
There is an unlimited free edition for academics as part of the Academic Initiative, and there is a free limited edition for everyone (MP problems limited to 1000 variables and 1000 constraints). CPLEX Optimization Studio developer edition or free community edition.
On premise on your computer CPLEX Optimization Studio
- Tutorial: Optimization modeling with IBM ILOG CPLEX Optimization Studio
- Hands on Lab – CPLEX Fundamentals
- Introduction — IBM ILOG CPLEX Optimization Studio
- Latest online COS 12.10 documentation
- IDE tutorials
- Getting Started HowTos, QuickTips and workshops – IDE videos
- Planning Analytics, SPSS and CPLEX Tutorial - Step by Step Tutorial – Increase forecast accuracy and achieve better planning
- Mathematical Optimization for Business Problems: A complete free course on Mathematical Programming with introduction, application cases, modeling and introduction to MP algorithms, by Victoria Genin and Shirley de Jonk, IBM Technical writers.
- IBM ILOG CP optimizer for scheduling, a very complete academic publication from the IBM development team of CP Optimizer
- Introduction to CP Optimizer for Scheduling, from Philippe Laborie from the IBM CP Optimizer development team.
- Linear Programming Tutorial notebook part 1.
- Linear Programming Tutorial notebook part 2.
- Scheduling Tutorial notebook
- IBM Decision Optimization Modeling for Python (DOcplex) documentation, the official docplex documentation including reference manuals.