gurobi/R/mip.R

In tom-n-pdx/lpSolveS4: Title: Technology Forecasting using DEA (Data Envelopment Analysis)

# Copyright 2016, Gurobi Optimization, Inc.
#
# This example formulates and solves the following simple MIP model:
#  maximize
#        x +   y + 2 z
#  subject to
#        x + 2 y + 3 z <= 4
#        x +   y       >= 1
#        x, y, z binary

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)