# Optimization

## Multi-objective linear optimization with weighted sub-problems, using PuLP in Python 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 In some of my posts I used lpSolve or FuzzyLP in R for solving linear optimization problems. I have also used PuLP and SciPy.optimize in Python for solving such problems. In all those cases the problem had only one objective […]

## Continuous linear optimization in PuLP (Python) In a previous post I demonstrated how to solve a linear optimization problem in Python, using SciPy.optimize with the linprog function. In this post I want to provide a coding example in Python, using the PuLP module to solve below […]

## Solving linear problem with fuzzy constraints by sampling beta with FuzzyLP in R In a previous post I demonstrated how to use crispLP to solve a simple linear optimization problem with well-defined objective function and constraints. I have also demonstrated how to solve a fuzzy linear optimization problem with uncertain constraints, i.e. fuzzy constraint […]

## Linear optimization with fuzzy constraints conducted in R with FuzzyLP Using the FuzzyLP package in R I have demonstrated simple linear optimization of a well defined linear problem using the crispLP function in a previous post. In that example basic simplex algorithm was applied to solve the problem. In this […]

## Simple linear problem with FuzzyLP, using crispLP and basic simplex In previous posts I have demonstrated how to solve linear programs in R using lpSolve – or in Python using SciPy.optimize. In this coding example I want to show how to conduct simple linear optimization using the FuzzyLP package. FuzzyLP […]

## Linear optimization in Python: Using SciPy for linear programming In previous posts I showed how to conduct optimization in R (linear optimization with lpSolve, quadratic optimization with quadprog and non-linear gradient descent optimization with nloptr). In this post I show how to model and solve the linear optimization problem […]

## Gradient descent in R, for non-linear optimization (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 or objectives that are non-linear. R provides a package for solving non-linear […]

## Cost minimal production scheduling – solving the assignment problem with lpSolve in R The assignment problem is a classic problem in linear program. If, for example, you have n jobs that need to be manufactured during the upcoming shift (in a manufacturing plant) and you have m machines to produce these tasks, then you want […]

## Solving linear transport problem with lp.transport in R, using lpSolve The transportation problem is one of the classical problems teached in linear programming classes. The problem, put simply, states that a given set of customers with a specified demand must be satisfied by another set of supplier with certain capacities […]

## Solving a simple linear programming problem using lpSolve in R In this post I show how to conduct simple linear optimization in R. You can also find other posts written by me that look at other linear optimization tasks, suchs as the transportation problem (can be solved with lp.transport), the […]