diff --git a/docs/examples/two-stage-robust-facility-location-problem.rst b/docs/examples/two-stage-robust-facility-location-problem.rst
index edbbcada..508ee29d 100644
--- a/docs/examples/two-stage-robust-facility-location-problem.rst
+++ b/docs/examples/two-stage-robust-facility-location-problem.rst
@@ -18,7 +18,7 @@ Each customer :math:`j\in V_2` has a demand :math:`d_j`.
 The unitary cost for serving customer :math:`j\in V_2` from facility :math:`i\in V_1` is :math:`t_{ij}`.
 The uncertainty in customer demands is controlled by a parameter :math:`\Gamma`.
 
-In this robust variant, we consider that the demands are uncertain and can be expressed as :math:`d_j(\xi) = d_j(1 + p\xi_j)``
+In this robust variant, we consider that the demands are uncertain and can be expressed as :math:`d_j(\xi) = d_j(1 + p\xi_j)`
 with :math:`p` being the maximum increase in demand and :math:`\xi` being an unknown vector taken in the uncertainty set
 
 .. math::
diff --git a/docs/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.rst b/docs/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.rst
index 5718d08e..8c5529b7 100644
--- a/docs/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.rst
+++ b/docs/tutorials/robust-optimization/column-and-constraint-generation/write-ccg.rst
@@ -1,2 +1,49 @@
+.. _tutorial_ccg:
+
 Writing a Column-and-Constraint-Generation Algorithm
 ====================================================
+
+In this tutorial, we will see how to write a column-and-constraint-generation to solve a two-stage robust problem.
+
+To this end, we will assume that you have your two-stage robust problem modeled already in idol. In particular,
+we consider that you have
+
+1. :code:`(idol::Model) model` which is the deterministic model of the problem in which the uncertain data are seen as parameters.
+
+2. :code:`(idol::Model) uncertainty_set` which is the uncertainty set of the robust problem.
+
+3. :code:`(idol::Robust::StageDescription) stages` which stores the assignments of variables and constraints to each stage.
+
+If you do not know what these are, please refer to the tutorial on :ref:`how to model a two-stage robust problem <modeling_two_stage_robust_problem>`.
+
+Then, you can solve this two-stage robust problem using a column-and-constraint-generation algorithm as follows:
+
+.. code::
+
+     model.use(
+            Robust::ColumnAndConstraintGeneration(stages, uncertainty_set)
+                    .with_master_optimizer(Gurobi())
+                    .with_separator(Robust::CCGSeparators::Bilevel())
+                    .with_logs(true)
+    );
+
+    model.optimize();
+
+    std::cout << save_primal(model) << std::endl;
+
+Notice that the optimizer is attached to the deterministic model and that both the uncertainty set and the stages are passed as arguments.
+An optimizer to solve the master problem is necessary and will be created when necessary. Here, we use the Gurobi optimizer.
+
+Most importantly, it is also necessary to specify a separator. The separator is a sub-routine of the CCG algorithm
+which solves the separation problem, i.e., it finds the worst-case scenario for a given solution of the master problem.
+In this example, we use the Bilevel separator which calls the MibS bilevel optimization solver at each iteration.
+
+Finally, the method :code:`optimize` is called to solve the problem and the solution is printed.
+
+.. admonition::
+
+    Here, we have assumed that the problem does not satisfy the complete recourse assumption, i.e., it is not known if
+    :math:`\forall x\in X, \forall\xi\in\Xi, \exists y\in Y(x,\xi)`. If, to the contrary, this assumption holds,
+    it can be communicated to the solver by calling the method :code:`with_complete_recourse(true)`. By doing this,
+    the CCG algorithms will not solve the feasibility separation problem which is most likely to result in a faster convergence.
+
diff --git a/docs/tutorials/robust-optimization/modeling/two-stage-robust.rst b/docs/tutorials/robust-optimization/modeling/two-stage-robust.rst
index cf4fa7c4..ffd96f78 100644
--- a/docs/tutorials/robust-optimization/modeling/two-stage-robust.rst
+++ b/docs/tutorials/robust-optimization/modeling/two-stage-robust.rst
@@ -1,3 +1,216 @@
-Modeling a Two-Stage Robust Problem [TODO]
-==========================================
+.. _modeling_two_stage_robust_problem:
 
+Modeling a Two-Stage Robust Problem
+===================================
+
+In this tutorial, we will see how to model a two-stage robust problem in idol.
+
+To follow this tutorial, you should be familiar with two-stage robust optimization and modeling optimization problems in idol.
+If this is not the case, we recommend you to read the tutorial on :ref:`MIP modeling <mip_modeling>`.
+
+.. contents:: Table of Contents
+    :local:
+    :depth: 2
+
+A Two-Stage Robust Facility Location Problem
+--------------------------------------------
+
+We consider a two-stage robust facility location problem (FLP) where demands are uncertain.
+
+Given a set of potential facility locations :math:`V_1` and a set of customers :math:`V_2`, the goal is to select a subset of facility locations
+to activate in order to serve all customers' demand, while minimizing the total cost.
+This version introduces uncertainty in the customers' demands.
+
+Note that there is also an example for the :ref:`deterministic version of the FLP using Column Generation <example_flp>`.
+
+Each facility :math:`i\in V_1` has an opening cost :math:`f_i` and a maximum capacity :math:`q_i`.
+Each customer :math:`j\in V_2` has a demand :math:`d_j`.
+The unitary cost for serving customer :math:`j\in V_2` from facility :math:`i\in V_1` is :math:`{ij}`.
+The uncertainty in customer demands is controlled by a parameter :math:`\Gamma`.
+
+In this robust variant, we consider that the demands are uncertain and can be expressed as :math:`d_j(\xi) = d_j(1 + p\xi_j)`
+with :math:`p` being the maximum increase in demand and :math:`\xi` being an unknown vector taken in the uncertainty set
+
+.. math::
+
+    \Xi := \left\{ \xi\in[ 0, 1 ]^{|V_2|} : \sum_{j\in V_2} \xi_j \le \Gamma \right\}.
+
+We model the two-stage robust FLP as
+
+.. math::
+
+    \min_{x\in \{0,1\}^{|V_1|}} \ \left\{ \sum_{i\in V_1} f_i x_i + \max_{\xi\in \Xi} \ \min_{y\in Y(x,\xi)} \  \sum_{i\in V_1} \sum_{j\in V_2} {ij} y_{ij} \right\}
+
+where :math:`Y(x,\xi)` is the set of feasible solutions for the second stage problem, given the first stage solution :math:`x` and the realization :math:`\xi` of the uncertain demand vector.
+It is defined as the set of vectors :math:`y\in \mathbb{R}^{|V_1|\times|V_2|}` that satisfy the following constraints
+
+.. math::
+
+    \begin{align*}
+        & \sum_{i\in V_1} y_{ij} = d_j(\xi) && j\in V_2, \\
+        & \sum_{j\in V_2} y_{ij} \le q_i x_i && i\in V_1, \\
+        & y_{ij} \ge 0 && i\in V_1, j\in V_2.
+    \end{align*}
+
+In what follows, we will assume that we have a variable :code:`instance` of type :code:`idol::Problems::FLP::Instance`
+that contains the data of the problem. Most typically, you will want to read the instance from a file. This is done as follows.
+
+.. code::
+
+    // Read instance
+    const auto instance = Problems::FLP::read_instance_1991_Cornuejols_eal("robusccg.data.txt");
+
+Additionally, we create an optimization environment :code:`env` and some parameters.
+
+.. code::
+
+    Env env;
+
+    const double Gamma = 3;
+    const double percentage_increase = .2;
+
+Modeling Steps
+--------------
+
+To model a two-stage robust problem, one needs to follow the following steps:
+
+1. Define an uncertainty set :math:`\Xi`.
+2. Define the deterministic model in which :math:`\xi` is a parameter.
+3. Assign a stage to each variable and constraint.
+
+Let's now see how to model the two-stage robust FLP in idol.
+
+Defining the Uncertainty Set
+-----------------------------
+
+Modeling the uncertainty set :math:`\Xi` is done in the same way as modeling any classical optimization problem.
+For instance, one can do as follows.
+
+.. code::
+
+    const unsigned int n_customers = instance.n_customers();
+
+    Model uncertainty_set(env);
+
+    auto xi = uncertainty_set.add_vars(Dim<1>(n_customers), 0., 1, Binary, "xi");
+    uncertainty_set.add_ctr(idol_Sum(j, Range(n_customers), xi[j]) <= Gamma);
+
+Defining the Deterministic Model
+--------------------------------
+
+The deterministic model underlying the two-stage robust FLP is the same as the classical FLP, except that the demand is a parameter.
+
+Recall that a variable, e.g., :code:`xi[0]`, can be turned into a parameter by prepending it with :code:`!`.
+
+Hence,
+we can define the deterministic model as follows.
+
+.. code::
+
+    const unsigned int n_facilities = instance.n_facilities();
+
+    Model model(env);
+
+    const auto x = model.add_vars(Dim<1>(n_facilities), 0., 1., Binary, "x");
+    const auto y = model.add_vars(Dim<2>(n_facilities, n_customers), 0., Inf, Continuous, "y");
+
+    // Capacity constraints
+    for (unsigned int i = 0 ; i < n_facilities ; ++i) {
+        model.add_ctr(idol_Sum(j, Range(n_customers), y[i][j]) <= instance.capacity(i) * x[i]);
+    }
+
+    // Demand satisfaction constraints
+    for (unsigned int j = 0 ; j < n_customers ; ++j) {
+        // IMPORTANT: here we use the parameter "!xi[j]" instead of the variable "xi[j]"
+        model.add_ctr(idol_Sum(i, Range(n_facilities), y[i][j]) == instance.demand(j) * (1 + percentage_increase * !xi[j]));
+    }
+
+    // Objective function
+    model.seobj_expr(idol_Sum(i, Range(n_facilities),
+                                instance.fixed_cost(i) * x[i]
+                                + idol_Sum(j, Range(n_customers),
+                                           instance.per_unitransportation_cost(i, j) * y[i][j]
+                                )
+                       )
+    );
+
+Assigning Stages
+----------------
+
+The last step is to assign a stage to each variable and constraint. Here, variables :math:`x` are first-stage variables
+and variables :math:`y` are second-stage variables, i.e., they depend on the realization of the uncertain demand.
+Similarly, all constraints are second-stage constraints since they are part of the second-stage feasible region.
+
+Assigning stages is done by creating a new object of type :code:`idol::Robust::StageDescription`.
+Under the hood, this object does nothing more than defining new annotations for variables and constraints storing
+the assigned stage of each variable and constraint. It is created as follows.
+
+.. code::
+
+    Robust::StageDescription stages(t_model.env());
+
+By default, all variables and constraints are assigned to the first stage.
+To assign a variable or constraint to the second stage, one can use the method :code:`set_stage` of the object :code:`stages`.
+For instance, one can do as follows.
+
+.. code::
+
+    for (const auto& var : model.vars()) {
+        if (var.name().front() != 'x') {
+            stages.set_stage(var, 2);
+        }
+    }
+
+Similarly, since all constraints are second-stage constraints, one can do as follows.
+
+.. code::
+
+    for (const auto& ctr : model.ctrs()) {
+        stages.set_stage(ctr, 2);
+    }
+
+.. admonition:: About stage annotations
+
+    Note that it is also possible to define your own annotations to assign variables and constraints to stages.
+    This is a rather advanced feeature and it is your responsability to ensure that the annotations are consistent with the model.
+
+    The annotations are based on the following conventions: all first-stage variables and constraints have the annotation evaluating to :code:`MasterId`.
+    All second-stage variables and constraints have the annotation evaluating to :code:`0`.
+
+    For instance, the following code is equivalent to the previous one.
+
+    .. code::
+
+        Annotation<Var, unsigned int> stage_vars(model, "stage_vars", MasterId); // By default, all variables are first-stage variables
+        Annotation<Ctr, unsigned int> stage_ctrs(model, "stage_ctrs", MasterId); // By default, all constraints are first-stage constraints
+
+        for (const auto& var : model.vars()) {
+            if (var.name().front() != 'x') {
+                var.set(stage_vars, 0); // Assign variable to the second stage
+            }
+        }
+
+        for (const auto& ctr : model.ctrs()) {
+            ctr.set(stage_ctrs, 0); // Assign constraint to the second stage
+        }
+
+        idol::Robust::StageDescription stages(stage_vars, stage_ctrs);
+
+    By doing so, a call to :code:`stages.stage(var)` will return "1" for all first-stage variables and "2" for all second-stage variables.
+    The underlying annotation can be obtained using
+
+    .. code::
+
+        Annotation<Var, unsigned int> stage_vars = stages.stage_vars()
+
+    Finally, also note the method :code:`stages.stage_index(var)` that will return "0" for all first-stage variables and "1" for all second-stage variables.
+
+
+That's it! We have now modeled a two-stage robust FLP in idol. Note that you will now need
+to attach an optimizer to the model to solve it.
+To this end, be sure to check the tutorials on optimizers for two-stage robust problems, e.g., :ref:`the column-and-constraint generation tutorial <tutorial_ccg>`.
+
+Complete Example
+----------------
+
+A complete example is given :ref:`here <example_robust_flp_ccg>`
\ No newline at end of file