diff --git a/docs/source/models/san.rst b/docs/source/models/san.rst
index b2970d47..b12ff9ea 100644
--- a/docs/source/models/san.rst
+++ b/docs/source/models/san.rst
@@ -108,22 +108,24 @@ Optimal Objective Function Value
 Unknown
 
 Optimization Problem: Minimize Longest Path Plus Penalty with Stochastic Constraints (SAN-2)
-============================================================================================
+--------------------------------------------------------------------------------------------
 
 Decision Variables
--------------------
+^^^^^^^^^^^^^^^^^^
+
 * **arc_means**
 
 Objectives
------------
-Suppose that we can select :math:`\theta_i > 0` for each :math:`i`, but there is an associated cost.  
+^^^^^^^^^^
+
+Suppose that we can select :math:`\theta_i > 0` for each :math:`i`, but there is an associated cost.
 In particular, we want to minimize:
 
 .. math::
 
     \mathbb{E}[T(\theta)] + f(\theta),
 
-where :math:`T(\theta)` is the (random) duration of the longest path from node :math:`a` to node :math:`i`, and  
+where :math:`T(\theta)` is the (random) duration of the longest path from node :math:`a` to node :math:`i`, and
 
 .. math::
 
@@ -131,11 +133,12 @@ where :math:`T(\theta)` is the (random) duration of the longest path from node :
 
 where :math:`n` is the number of arcs.
 
-The objective function is convex in :math:`\theta`.  
+The objective function is convex in :math:`\theta`.
 
 Constraints
-------------
-We require that :math:`\theta_i > 0` for each :math:`i`.  
+^^^^^^^^^^^
+
+We require that :math:`\theta_i > 0` for each :math:`i`.
 Additionally, we allow :math:`n` stochastic constraints that restrict the expected time to reach node :math:`i`, of the form:
 
 .. math::
@@ -143,35 +146,41 @@ Additionally, we allow :math:`n` stochastic constraints that restrict the expect
     \mathbb{E}[T_i(\theta)] \leq a_i.
 
 Problem Factors
-----------------
-* **budget**: Maximum number of replications the solver is allowed to take.  
-  *Default:* ``10000``
+^^^^^^^^^^^^^^^
 
-* **arc_costs**: Cost associated with each arc.  
-  *Default:* ``(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)``
+* **budget**: Maximum number of replications the solver is allowed to take.
+    *Default:* ``10000``
 
-* **constraint_nodes**: Nodes with corresponding stochastic constraints.  
-  *Default:* ``[6, 8]``
+* **arc_costs**: Cost associated with each arc.
+    *Default:* ``(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)``
 
-* **length_to_node_constraint**: Maximum expected length to corresponding constraint nodes.  
-  *Default:* ``[5, 5]``
+* **constraint_nodes**: Nodes with corresponding stochastic constraints.
+    *Default:* ``[6, 8]``
+
+* **length_to_node_constraint**: Maximum expected length to corresponding constraint nodes.
+    *Default:* ``[5, 5]``
 
 Fixed Model Factors
---------------------
+^^^^^^^^^^^^^^^^^^^
+
 * **N/A**
 
 Starting Solution
-------------------
+^^^^^^^^^^^^^^^^^
+
 * **initial_solution**: ``(8,) * 13``
 
 Random Solutions
------------------
+^^^^^^^^^^^^^^^^
+
 Each arc mean is sampled independently from a lognormal distribution with 2.5th and 97.5th percentiles equal to 0.1 and 10, respectively.
 
 Optimal Solution
------------------
+^^^^^^^^^^^^^^^^
+
 * **Unknown**
 
 Optimal Objective Function Value
----------------------------------
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 * **Unknown**