Network problems are everywhere. We can easily find instances in logistics, telecom, project mangement, among others. In order to attack these problems using linear programming we need to go beyond assign and transportation problems that we saw in part I. Our goal here is to expand the problems we can solve using lpsove and igraph […]

# linear programming

## Data Science for Operational Excellence (Part-2)

Below are the solutions to these exercises on Linear Programming. #################### # # # Exercise 1 # # # #################### library(lpSolve) library(igraph) f.obj <- c(1,9,1) f.con <- matrix(c(1,2,3,3,2,2), nrow = 2, byrow = T) f.dir <- c("<=", "<=") f.rhs <- c(9,15) lp("max", f.obj, f.con, f.dir, f.rhs) ## Success: the objective function is 40.5 lp("max", f.obj, […]

## Data Science for Operational Excellence (Part-1)

Below are the solutions to these exercises on Linear Programming. #################### # # # Exercise 1 # # # #################### library(lpSolve) library(igraph) #################### # # # Exercise 2 # # # #################### set.seed(1234) assign.costs <- matrix(sample(50:100, 16, replace=T), ncol=4) #################### # # # Exercise 3 # # # #################### x <- lp.assign(assign.costs) x$solution ## [,1] […]

## Data Science for Operational Excellence (Part-1)

R has many powerful libraries to handle operations research. This exercise tries to demonstrate a few basic functionality of R while dealing with linear programming. Linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints. The lpsolve package in R provides a set […]