Newsvendor

This tutorial was generated using Literate.jl. Download the source as a .jl file. Download the source as a .ipynb file.

This example is based on the classical newsvendor problem, but features an AR(1) spot-price.

   V(x[t-1], ω[t]) =         max p[t] × u[t]
                      subject to x[t] = x[t-1] - u[t] + ω[t]
                                 u[t] ∈ [0, 1]
                                 x[t] ≥ 0
                                 p[t] = p[t-1] + ϕ[t]

The initial conditions are

x[0] = 2.0
p[0] = 1.5
ω[t] ~ {0, 0.05, 0.10, ..., 0.45, 0.5} with uniform probability.
ϕ[t] ~ {-0.25, -0.125, 0.125, 0.25} with uniform probability.
using SDDP, HiGHS, Statistics, Test

function joint_distribution(; kwargs...)
    names = tuple([first(kw) for kw in kwargs]...)
    values = tuple([last(kw) for kw in kwargs]...)
    output_type = NamedTuple{names,Tuple{eltype.(values)...}}
    distribution = map(output_type, Base.product(values...))
    return distribution[:]
end

function newsvendor_example(; cut_type)
    model = SDDP.PolicyGraph(
        SDDP.LinearGraph(3);
        sense = :Max,
        upper_bound = 50.0,
        optimizer = HiGHS.Optimizer,
    ) do subproblem, stage
        @variables(subproblem, begin
            x >= 0, (SDDP.State, initial_value = 2)
            0 <= u <= 1
            w
        end)
        @constraint(subproblem, x.out == x.in - u + w)
        SDDP.add_objective_state(
            subproblem;
            initial_value = 1.5,
            lower_bound = 0.75,
            upper_bound = 2.25,
            lipschitz = 100.0,
        ) do y, ω
            return y + ω.price_noise
        end
        noise_terms = joint_distribution(;
            demand = 0:0.05:0.5,
            price_noise = [-0.25, -0.125, 0.125, 0.25],
        )
        SDDP.parameterize(subproblem, noise_terms) do ω
            fix(w, ω.demand)
            price = SDDP.objective_state(subproblem)
            @stageobjective(subproblem, price * u)
        end
    end
    SDDP.train(
        model;
        log_frequency = 10,
        time_limit = 20.0,
        cut_type = cut_type,
    )
    @test SDDP.calculate_bound(model) ≈ 4.04 atol = 0.05
    results = SDDP.simulate(model, 500)
    objectives =
        [sum(s[:stage_objective] for s in simulation) for simulation in results]
    @test round(Statistics.mean(objectives); digits = 2) ≈ 4.04 atol = 0.1
    return
end

newsvendor_example(; cut_type = SDDP.SINGLE_CUT)
newsvendor_example(; cut_type = SDDP.MULTI_CUT)
-------------------------------------------------------------------
         SDDP.jl (c) Oscar Dowson and contributors, 2017-25
-------------------------------------------------------------------
problem
  nodes           : 3
  state variables : 1
  scenarios       : 8.51840e+04
  existing cuts   : false
options
  solver          : serial mode
  risk measure    : SDDP.Expectation()
  sampling scheme : SDDP.InSampleMonteCarlo
subproblem structure
  VariableRef                             : [6, 6]
  AffExpr in MOI.EqualTo{Float64}         : [1, 3]
  AffExpr in MOI.LessThan{Float64}        : [2, 2]
  VariableRef in MOI.GreaterThan{Float64} : [3, 4]
  VariableRef in MOI.LessThan{Float64}    : [3, 3]
numerical stability report
  matrix range     [8e-01, 2e+00]
  objective range  [1e+00, 2e+00]
  bounds range     [1e+00, 1e+02]
  rhs range        [5e+01, 5e+01]
-------------------------------------------------------------------
 iteration    simulation      bound        time (s)     solves  pid
-------------------------------------------------------------------
        10   5.500000e+00  5.473308e+00  1.743262e-01      1350   1
        20   4.062500e+00  4.451907e+00  2.718911e-01      2700   1
        30   2.993750e+00  4.101372e+00  3.790200e-01      4050   1
        40   5.750000e+00  4.095757e+00  4.912910e-01      5400   1
        50   5.125000e+00  4.093536e+00  6.061361e-01      6750   1
        60   3.737500e+00  4.089225e+00  7.275441e-01      8100   1
        70   4.500000e+00  4.088152e+00  8.524292e-01      9450   1
        80   4.950000e+00  4.087904e+00  9.794741e-01     10800   1
        90   4.125000e+00  4.087756e+00  1.110167e+00     12150   1
       100   3.275000e+00  4.086300e+00  1.248573e+00     13500   1
       110   5.250000e+00  4.086125e+00  1.384426e+00     14850   1
       120   4.975000e+00  4.086054e+00  1.529795e+00     16200   1
       130   3.500000e+00  4.085422e+00  1.670722e+00     17550   1
       140   4.593750e+00  4.085327e+00  1.810949e+00     18900   1
       150   3.000000e+00  4.085258e+00  1.953153e+00     20250   1
       160   2.700000e+00  4.085247e+00  2.099313e+00     21600   1
       170   3.812500e+00  4.085151e+00  2.249419e+00     22950   1
       180   3.875000e+00  4.085121e+00  2.405500e+00     24300   1
       190   4.737500e+00  4.085102e+00  2.556252e+00     25650   1
       200   2.906250e+00  4.085073e+00  2.709201e+00     27000   1
       210   3.750000e+00  4.085050e+00  2.894691e+00     28350   1
       220   5.050000e+00  4.085037e+00  3.052034e+00     29700   1
       230   2.925000e+00  4.085012e+00  3.202331e+00     31050   1
       240   4.500000e+00  4.084970e+00  3.366430e+00     32400   1
       250   4.875000e+00  4.084908e+00  3.528083e+00     33750   1
       260   3.675000e+00  4.084905e+00  3.679285e+00     35100   1
       270   4.725000e+00  4.084903e+00  3.841454e+00     36450   1
       280   3.437500e+00  4.084900e+00  4.001794e+00     37800   1
       290   3.750000e+00  4.084879e+00  4.165520e+00     39150   1
       300   4.125000e+00  4.084879e+00  4.332723e+00     40500   1
       310   4.875000e+00  4.084803e+00  4.495145e+00     41850   1
       320   5.625000e+00  4.084803e+00  4.653374e+00     43200   1
       330   3.825000e+00  4.084800e+00  4.819614e+00     44550   1
       340   3.300000e+00  4.084796e+00  4.985861e+00     45900   1
       350   5.887500e+00  4.084796e+00  5.155859e+00     47250   1
       360   3.600000e+00  4.084786e+00  5.325351e+00     48600   1
       370   3.618750e+00  4.084786e+00  5.486185e+00     49950   1
       380   4.968750e+00  4.084782e+00  5.648801e+00     51300   1
       390   3.300000e+00  4.084780e+00  5.809624e+00     52650   1
       400   4.006250e+00  4.084779e+00  5.982027e+00     54000   1
       410   4.050000e+00  4.084779e+00  6.152761e+00     55350   1
       420   5.125000e+00  4.084776e+00  6.341784e+00     56700   1
       430   4.000000e+00  4.084776e+00  6.511230e+00     58050   1
       440   4.125000e+00  4.084776e+00  6.689984e+00     59400   1
       450   3.112500e+00  4.084776e+00  6.864893e+00     60750   1
       460   3.750000e+00  4.084771e+00  7.043900e+00     62100   1
       470   3.187500e+00  4.084767e+00  7.210600e+00     63450   1
       480   3.031250e+00  4.084757e+00  7.393886e+00     64800   1
       490   4.181250e+00  4.084753e+00  7.568820e+00     66150   1
       500   3.187500e+00  4.084746e+00  7.750634e+00     67500   1
       510   4.875000e+00  4.084741e+00  7.921358e+00     68850   1
       520   3.900000e+00  4.084737e+00  8.090199e+00     70200   1
       530   4.350000e+00  4.084737e+00  8.268584e+00     71550   1
       540   4.212500e+00  4.084737e+00  8.445743e+00     72900   1
       550   4.250000e+00  4.084734e+00  8.628527e+00     74250   1
       560   3.750000e+00  4.084734e+00  8.809055e+00     75600   1
       570   5.306250e+00  4.084730e+00  8.987904e+00     76950   1
       580   4.725000e+00  4.084730e+00  9.164302e+00     78300   1
       590   4.250000e+00  4.084730e+00  9.339590e+00     79650   1
       600   4.000000e+00  4.084730e+00  9.519629e+00     81000   1
       610   4.600000e+00  4.084730e+00  9.688460e+00     82350   1
       620   3.375000e+00  4.084730e+00  9.868716e+00     83700   1
       630   3.981250e+00  4.084725e+00  1.006623e+01     85050   1
       640   3.250000e+00  4.084725e+00  1.024013e+01     86400   1
       650   3.625000e+00  4.084725e+00  1.041337e+01     87750   1
       660   4.781250e+00  4.084725e+00  1.059746e+01     89100   1
       670   4.275000e+00  4.084725e+00  1.078696e+01     90450   1
       680   2.731250e+00  4.084725e+00  1.098281e+01     91800   1
       690   5.237500e+00  4.084725e+00  1.117682e+01     93150   1
       700   3.325000e+00  4.084725e+00  1.135793e+01     94500   1
       710   4.750000e+00  4.084725e+00  1.155076e+01     95850   1
       720   4.537500e+00  4.084725e+00  1.173737e+01     97200   1
       730   4.725000e+00  4.084725e+00  1.192951e+01     98550   1
       740   4.475000e+00  4.084725e+00  1.211305e+01     99900   1
       750   2.893750e+00  4.084725e+00  1.231296e+01    101250   1
       760   3.525000e+00  4.084725e+00  1.250638e+01    102600   1
       770   3.525000e+00  4.084725e+00  1.270108e+01    103950   1
       780   3.262500e+00  4.084725e+00  1.289019e+01    105300   1
       790   4.918750e+00  4.084725e+00  1.308782e+01    106650   1
       800   3.750000e+00  4.084725e+00  1.329055e+01    108000   1
       810   4.687500e+00  4.084725e+00  1.347995e+01    109350   1
       820   4.018750e+00  4.084725e+00  1.368059e+01    110700   1
       830   4.725000e+00  4.084725e+00  1.387749e+01    112050   1
       840   4.268750e+00  4.084725e+00  1.407353e+01    113400   1
       850   5.175000e+00  4.084725e+00  1.426122e+01    114750   1
       860   3.125000e+00  4.084725e+00  1.445789e+01    116100   1
       870   2.762500e+00  4.084725e+00  1.464156e+01    117450   1
       880   3.375000e+00  4.084725e+00  1.482894e+01    118800   1
       890   3.875000e+00  4.084725e+00  1.502640e+01    120150   1
       900   3.093750e+00  4.084725e+00  1.521964e+01    121500   1
       910   4.125000e+00  4.084724e+00  1.543982e+01    122850   1
       920   4.750000e+00  4.084724e+00  1.564346e+01    124200   1
       930   5.431250e+00  4.084724e+00  1.584579e+01    125550   1
       940   3.000000e+00  4.084724e+00  1.605179e+01    126900   1
       950   4.243750e+00  4.084724e+00  1.625313e+01    128250   1
       960   3.756250e+00  4.084724e+00  1.645871e+01    129600   1
       970   3.750000e+00  4.084724e+00  1.667515e+01    130950   1
       980   4.350000e+00  4.084724e+00  1.686638e+01    132300   1
       990   3.375000e+00  4.084722e+00  1.706720e+01    133650   1
      1000   4.750000e+00  4.084722e+00  1.726655e+01    135000   1
      1010   3.825000e+00  4.084722e+00  1.746295e+01    136350   1
      1020   4.000000e+00  4.084722e+00  1.764780e+01    137700   1
      1030   4.500000e+00  4.084722e+00  1.784382e+01    139050   1
      1040   3.637500e+00  4.084722e+00  1.804715e+01    140400   1
      1050   4.050000e+00  4.084722e+00  1.824395e+01    141750   1
      1060   4.925000e+00  4.084722e+00  1.845208e+01    143100   1
      1070   4.500000e+00  4.084722e+00  1.866891e+01    144450   1
      1080   5.000000e+00  4.084722e+00  1.888344e+01    145800   1
      1090   4.393750e+00  4.084722e+00  1.908709e+01    147150   1
      1100   3.750000e+00  4.084722e+00  1.930593e+01    148500   1
      1110   4.306250e+00  4.084722e+00  1.950306e+01    149850   1
      1120   4.200000e+00  4.084722e+00  1.972275e+01    151200   1
      1130   4.343750e+00  4.084722e+00  1.992831e+01    152550   1
      1134   2.700000e+00  4.084722e+00  2.000800e+01    153090   1
-------------------------------------------------------------------
status         : time_limit
total time (s) : 2.000800e+01
total solves   : 153090
best bound     :  4.084722e+00
simulation ci  :  4.100588e+00 ± 4.281479e-02
numeric issues : 0
-------------------------------------------------------------------

-------------------------------------------------------------------
         SDDP.jl (c) Oscar Dowson and contributors, 2017-25
-------------------------------------------------------------------
problem
  nodes           : 3
  state variables : 1
  scenarios       : 8.51840e+04
  existing cuts   : false
options
  solver          : serial mode
  risk measure    : SDDP.Expectation()
  sampling scheme : SDDP.InSampleMonteCarlo
subproblem structure
  VariableRef                             : [6, 6]
  AffExpr in MOI.EqualTo{Float64}         : [1, 3]
  AffExpr in MOI.LessThan{Float64}        : [2, 2]
  VariableRef in MOI.GreaterThan{Float64} : [3, 4]
  VariableRef in MOI.LessThan{Float64}    : [3, 3]
numerical stability report
  matrix range     [8e-01, 2e+00]
  objective range  [1e+00, 2e+00]
  bounds range     [1e+00, 1e+02]
  rhs range        [5e+01, 5e+01]
-------------------------------------------------------------------
 iteration    simulation      bound        time (s)     solves  pid
-------------------------------------------------------------------
        10   4.293750e+00  7.752581e+00  2.180679e-01      1350   1
        20   2.793750e+00  4.052432e+00  5.962629e-01      2700   1
        30   3.318750e+00  4.045364e+00  1.105634e+00      4050   1
        40   4.543750e+00  4.044755e+00  1.814145e+00      5400   1
        50   4.462500e+00  4.043080e+00  2.642404e+00      6750   1
        60   3.975000e+00  4.042026e+00  3.599988e+00      8100   1
        70   3.725000e+00  4.040053e+00  4.713917e+00      9450   1
        80   4.275000e+00  4.039783e+00  5.839163e+00     10800   1
        90   4.500000e+00  4.039014e+00  7.173338e+00     12150   1
       100   3.000000e+00  4.038950e+00  8.503462e+00     13500   1
       110   3.425000e+00  4.038914e+00  9.915895e+00     14850   1
       120   3.812500e+00  4.038865e+00  1.156993e+01     16200   1
       130   5.250000e+00  4.038824e+00  1.322924e+01     17550   1
       140   5.625000e+00  4.038227e+00  1.503969e+01     18900   1
       150   4.375000e+00  4.038203e+00  1.690311e+01     20250   1
       160   3.750000e+00  4.038124e+00  1.889547e+01     21600   1
       166   4.100000e+00  4.038124e+00  2.022044e+01     22410   1
-------------------------------------------------------------------
status         : time_limit
total time (s) : 2.022044e+01
total solves   : 22410
best bound     :  4.038124e+00
simulation ci  :  4.069869e+00 ± 1.135254e-01
numeric issues : 0
-------------------------------------------------------------------