using JuMP, GLPK

profit = [5, 3, 2, 7, 4]
weight = [2, 8, 4, 2, 5]
capacity = 10

model = Model(GLPK.Optimizer)

n = 5
@variable(model, x[1:n], Bin) # Binary
@variable(model, y[1:n], Int) # Integer
@variable(model, z[1:n]) # Continuous

@objective(model, Max, sum(profit[i]*x[i] for i=1:n))
@constraint(model, sum(weight[i]*x[i] for i=1:n) <= capacity)
@constraint(model, [i=1:n], x[i] <= 1) # unnecessary, just to include this syntax in the example

JuMP.optimize!(model)

if termination_status(model) == MOI.OPTIMAL
    println("Objective is: ", JuMP.objective_value(model))
    print("Solution is:")
    for i=1:n
        print(" ", JuMP.value(x[i]))
    end
    println()
else
    println("Optimise was not successful. Return code ", termination_status(model))
end