# install.packages("lpSolve")
# Load lpSolve
library(lpSolve)
## koefisien variabel / fungsi objectif
Cost <- c(30, 40, 80)
# Buat matrix sebelah kiri
Bahan <- matrix(c(1, 1, -10,
              4, 3, -20,
              1, 0, -2,
              1, 1, 0), nrow=4, byrow=TRUE)
# Buat matrix sebelah kanan
Batas <- c(500, 200, 100, 1000)
# set operator perbandingannya
constranints_direction  <- c("<=", "<=", "<=", ">=")
# panggil fungsi linear programming
optimum <-  lp(direction="min", #minimalkan biaya
               objective.in = Cost,
               const.mat = Bahan,
               const.dir = constranints_direction,
               const.rhs = Batas,
               all.int = T)
# jika menghasilkan 0 maka feasible!
print(optimum$status)

# nilai optimal dari A, B and t
best_sol <- optimum$solution
names(best_sol) <- c("A", "B", "t") 
print(best_sol)

#fungsi biaya
print(paste("Total cost: ", optimum$objval, sep=""))