Let us now turn our attention to an example of using Gurobi to solve a simple MIP model. Our example optimizes the following model:
| maximize | x | + | y | + | 2 z | ||
| subject to | x | + | 2 y | + | 3 z | 4 | |
| x | + | y | 1 | ||||
| x, y, z binary | |||||||
This is the complete source code for our example (also available in
<installdir>/examples/R/mip.R)...
library('gurobi')
model <- list()
model$A <- matrix(c(1,2,3,1,1,0), nrow=2, ncol=3, byrow=T)
model$obj <- c(1,1,2)
model$modelsense <- "max"
model$rhs <- c(4,1)
model$sense <- c('<', '>')
model$vtype <- 'B'
params <- list(OutputFlag=0)
result <- gurobi(model, params)
print('Solution:')
print(result$objval)
print(result$x)