vec_simple = @stochastic_model begin
    @stage 1 begin
        @parameters begin
            lb = [40.0, 20.0]
            c  = [100., 150.]
            A  = [1. 1.]
            b  = [120.]
        end
        @decision(model, x[i in 1:2] >= lb[i])
        @objective(model, Min, dot(c, x))
        @constraint(model, A * x - b in MOI.Nonpositives(1))
    end
    @stage 2 begin
        @parameters begin
            lb = [0., 0.]
            T  = [-60. 0.;
                  0.  -80.]
            W  = [6. 10.;
                  8. 5.]
        end
        @uncertain ξ[1:4]
        q  = [ξ[1], ξ[2]]
        ub = [ξ[3], ξ[4]]
        @recourse(model, lb[i] <= y[i in 1:2] <= ub[i])
        @objective(model, Max, dot(q, y))
        @constraint(model, T * x + W * y in MOI.Nonpositives(2))
    end
end

ξ₁ = @scenario ξ[1:4] = [24.0, 28.0, 500.0, 100.0] probability = 0.4
ξ₂ = @scenario ξ[1:4] = [28.0, 32.0, 300.0, 300.0] probability = 0.6

vec_simple_res = SPResult([46.67, 36.25], Dict(1 => [300., 100.], 2 => [300., 100.]), -855.83, -1518.75, 662.92, 286.92, -1445.92, -568.92)
push!(problems, (vec_simple, [ξ₁,ξ₂], vec_simple_res, "Vectorized Simple"))
