In this post, I describe how deeply uncertain parametric data can be controlled in an optimization model to gain conservative solutions and decisions. The investigated robust optimization approach can lead to least, partially, or fully conservative solutions by paying the […]

## Constraint programming for work scheduling with Google OR-Tools

## Linear integer programming with Google ortools in Python

## Lean coding of simple linear optimization ortools models in Python

## Simple linear programming with Google ortools in Python

## Categorization of optimization problems

## Multi-objective linear optimization with weighted sub-problems, using PuLP in Python

## Multi-objective linear optimization with PuLP in Python

## Continuous linear optimization in PuLP (Python)

## Solving linear problem with fuzzy constraints by sampling beta with FuzzyLP in R

## Linear optimization with fuzzy constraints conducted in R with FuzzyLP

## Simple linear problem with FuzzyLP, using crispLP and basic simplex

## Linear optimization in Python: Using SciPy for linear programming

## Gradient descent in R, for non-linear optimization (nloptr package)

This post introduces gradient descent optimization in R, using the nloptr package. For solving transport problems or network modelling problems, linear programming will suffice. Nevertheless, depending on the topic at hand, non-linear programming might become relevant when considering additional constraints […]