In my most recent post on linear programming I applied PuLP for solving below linear optimization problem, using two approaches. Approach #1 was based on solving a sub-problem with one objective only first, then adding the optimal outcome of that […]

## 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

## Cost minimal production scheduling – solving the assignment problem with lpSolve in R

## Solving linear transport problem with lp.transport in R, using lpSolve

## Simple linear programming with integer variables in R, using lpSolve

In the previous post (http://www.supplychaindataanalytics.com/solving-a-simple-linear-programming-problem-using-lpsolve-in-r/) I demonstrated how a simple linear optimization problem can be modelled and solved using the lpSolve package in R. In this post I want to add the topic of modelling integer variables. In the previous […]