From fe813e4dcc904a2bad18231b9b239c14a45236c0 Mon Sep 17 00:00:00 2001
From: visr <visr@users.noreply.github.com>
Date: Thu, 22 Aug 2024 15:14:10 +0000
Subject: [PATCH] deploy: 61f777d86c97359411ae46e1514a106acaace5bf

---
 concept/allocation.html                       |  28 +++++-----
 concept/concept.html                          |  31 ++++++++++-
 dev/allocation.html                           |  52 +++++++++---------
 dev/release.html                              |   4 +-
 .../figure-html/cell-18-output-1.png          | Bin 87163 -> 87147 bytes
 .../figure-html/cell-19-output-1.png          | Bin 113080 -> 113118 bytes
 .../figure-html/cell-57-output-2.png          | Bin 61237 -> 61229 bytes
 .../figure-html/cell-68-output-1.png          | Bin 44294 -> 44287 bytes
 reference/node/basin.html                     |  14 ++---
 reference/node/discrete-control.html          |   2 +-
 reference/node/manning-resistance.html        |   2 +-
 reference/node/tabulated-rating-curve.html    |   2 +-
 reference/test-models.html                    |   2 +-
 reference/validation.html                     |   4 +-
 search.json                                   |  20 +++----
 15 files changed, 93 insertions(+), 68 deletions(-)

diff --git a/concept/allocation.html b/concept/allocation.html
index 22f055569..9674eabe9 100644
--- a/concept/allocation.html
+++ b/concept/allocation.html
@@ -284,7 +284,7 @@ <h2 id="toc-title">On this page</h2>
    
   <ul>
   <li><a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction"><span class="header-section-number">1</span> Introduction</a></li>
-  <li><a href="#the-high-level-algorithm" id="toc-the-high-level-algorithm" class="nav-link" data-scroll-target="#the-high-level-algorithm"><span class="header-section-number">2</span> The high level algorithm</a></li>
+  <li><a href="#sec-high-level-algorithm" id="toc-sec-high-level-algorithm" class="nav-link" data-scroll-target="#sec-high-level-algorithm"><span class="header-section-number">2</span> The high level algorithm</a></li>
   <li><a href="#elements-of-allocation" id="toc-elements-of-allocation" class="nav-link" data-scroll-target="#elements-of-allocation"><span class="header-section-number">3</span> Elements of allocation</a>
   <ul class="collapse">
   <li><a href="#schematisation-input" id="toc-schematisation-input" class="nav-link" data-scroll-target="#schematisation-input"><span class="header-section-number">3.1</span> Schematisation input</a>
@@ -355,7 +355,7 @@ <h1 data-number="1"><span class="header-section-number">1</span> Introduction</h
 </div>
 </div>
 </section>
-<section id="the-high-level-algorithm" class="level1" data-number="2">
+<section id="sec-high-level-algorithm" class="level1" data-number="2">
 <h1 data-number="2"><span class="header-section-number">2</span> The high level algorithm</h1>
 <p>The allocation algorithm contains 3 types of optimization:</p>
 <ul>
@@ -584,7 +584,7 @@ <h2 data-number="4.3" class="anchored" data-anchor-id="the-optimization-constrai
 <section id="example" class="level2" data-number="4.4">
 <h2 data-number="4.4" class="anchored" data-anchor-id="example"><span class="header-section-number">4.4</span> Example</h2>
 <p>The following is an example of an optimization problem for the example shown <a href="../guide/examples.html#model-with-allocation-user-demand">here</a>:</p>
-<div id="6a791467" class="cell" data-execution_count="1">
+<div id="7006a8bc" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">using</span> <span class="bu">Ribasim</span></span>
@@ -607,27 +607,27 @@ <h2 data-number="4.4" class="anchored" data-anchor-id="example"><span class="hea
 <span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="fu">println</span>(p.allocation.allocation_models[<span class="fl">1</span>].problem)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-stdout">
-<pre><code>Min F[(Basin #5, UserDemand #6)]² + F[(Basin #2, UserDemand #3)]²
+<pre><code>Min F[(Basin #2, UserDemand #3)]² + F[(Basin #5, UserDemand #6)]²
 Subject to
- F[(UserDemand #3, Basin #2)] ≥ 0
- F[(UserDemand #6, Basin #5)] ≥ 0
  F[(TabulatedRatingCurve #7, Terminal #8)] ≥ 0
+ F[(Basin #5, TabulatedRatingCurve #7)] ≥ 0
+ F[(Basin #2, UserDemand #3)] ≥ 0
+ F[(UserDemand #3, Basin #2)] ≥ 0
  F[(Basin #2, LinearResistance #4)] ≥ 0
  F[(LinearResistance #4, Basin #2)] ≥ 0
- F[(LinearResistance #4, Basin #5)] ≥ 0
- F[(Basin #5, LinearResistance #4)] ≥ 0
  F[(Basin #5, UserDemand #6)] ≥ 0
  F[(FlowBoundary #1, Basin #2)] ≥ 0
- F[(Basin #5, TabulatedRatingCurve #7)] ≥ 0
- F[(Basin #2, UserDemand #3)] ≥ 0
- flow_conservation[Terminal #8] : F[(TabulatedRatingCurve #7, Terminal #8)] = 0
- flow_conservation[Basin #2] : F[(UserDemand #3, Basin #2)] - F[(Basin #2, LinearResistance #4)] + F[(LinearResistance #4, Basin #2)] + F[(FlowBoundary #1, Basin #2)] - F[(Basin #2, UserDemand #3)] = 0
- flow_conservation[Basin #5] : F[(UserDemand #6, Basin #5)] + F[(LinearResistance #4, Basin #5)] - F[(Basin #5, LinearResistance #4)] - F[(Basin #5, UserDemand #6)] - F[(Basin #5, TabulatedRatingCurve #7)] = 0
+ F[(UserDemand #6, Basin #5)] ≥ 0
+ F[(LinearResistance #4, Basin #5)] ≥ 0
+ F[(Basin #5, LinearResistance #4)] ≥ 0
  flow_conservation[LinearResistance #4] : F[(Basin #2, LinearResistance #4)] - F[(LinearResistance #4, Basin #2)] - F[(LinearResistance #4, Basin #5)] + F[(Basin #5, LinearResistance #4)] = 0
+ flow_conservation[Terminal #8] : F[(TabulatedRatingCurve #7, Terminal #8)] = 0
+ flow_conservation[Basin #5] : -F[(Basin #5, TabulatedRatingCurve #7)] - F[(Basin #5, UserDemand #6)] + F[(UserDemand #6, Basin #5)] + F[(LinearResistance #4, Basin #5)] - F[(Basin #5, LinearResistance #4)] = 0
  flow_conservation[TabulatedRatingCurve #7] : -F[(TabulatedRatingCurve #7, Terminal #8)] + F[(Basin #5, TabulatedRatingCurve #7)] = 0
+ flow_conservation[Basin #2] : -F[(Basin #2, UserDemand #3)] + F[(UserDemand #3, Basin #2)] - F[(Basin #2, LinearResistance #4)] + F[(LinearResistance #4, Basin #2)] + F[(FlowBoundary #1, Basin #2)] = 0
  source[(FlowBoundary #1, Basin #2)] : F[(FlowBoundary #1, Basin #2)] ≤ 172800
- source_user[UserDemand #3] : F[(UserDemand #3, Basin #2)] ≤ 0
  source_user[UserDemand #6] : F[(UserDemand #6, Basin #5)] ≤ 0
+ source_user[UserDemand #3] : F[(UserDemand #3, Basin #2)] ≤ 0
 </code></pre>
 </div>
 </div>
diff --git a/concept/concept.html b/concept/concept.html
index eda18fae9..0adabbab4 100644
--- a/concept/concept.html
+++ b/concept/concept.html
@@ -20,7 +20,27 @@
   margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
   vertical-align: middle;
 }
-</style>
+/* CSS for citations */
+div.csl-bib-body { }
+div.csl-entry {
+  clear: both;
+  margin-bottom: 0em;
+}
+.hanging-indent div.csl-entry {
+  margin-left:2em;
+  text-indent:-2em;
+}
+div.csl-left-margin {
+  min-width:2em;
+  float:left;
+}
+div.csl-right-inline {
+  margin-left:2em;
+  padding-left:1em;
+}
+div.csl-indent {
+  margin-left: 2em;
+}</style>
 
 
 <script src="../site_libs/quarto-nav/quarto-nav.js"></script>
@@ -267,7 +287,7 @@ <h1 data-number="1"><span class="header-section-number">1</span> Introduction</h
 <h1 data-number="2"><span class="header-section-number">2</span> Concept</h1>
 <section id="sec-physical" class="level2" data-number="2.1">
 <h2 data-number="2.1" class="anchored" data-anchor-id="sec-physical"><span class="header-section-number">2.1</span> Physical layer</h2>
-<p>To represent the physical characteristics of the water system in an area, Ribasim allows you to divide the area into a network of connected representative elementary watersheds (<a href="https://deltares.github.io/Ribasim/#ref-REGGIANI1998367">Reggiani, Sivapalan, and Majid Hassanizadeh 1998</a>). Within Ribasim, these elements are called basins, which are essentially buckets or reservoirs holding an aggregated volume of water bodies in an area. Basins are chained in a graph with connector nodes determining the exchange of water between the basins. These connector nodes can represent open water connections (e.g.&nbsp;bifurcations or resistance in a free flowing open water channel) or infrastructure elements such as pumps, gates or weirs. An overview of node types and associated data inputs is provided on the <a href="../reference/usage.html">usage page</a>, while the associated mathematical formations are described on the <a href="../concept/equations.html">equations page</a>.</p>
+<p>To represent the physical characteristics of the water system in an area, Ribasim allows you to divide the area into a network of connected representative elementary watersheds <span class="citation" data-cites="REGGIANI1998367">(<a href="#ref-REGGIANI1998367" role="doc-biblioref">Reggiani, Sivapalan, and Majid Hassanizadeh 1998</a>)</span>. Within Ribasim, these elements are called basins, which are essentially buckets or reservoirs holding an aggregated volume of water bodies in an area. Basins are chained in a graph with connector nodes determining the exchange of water between the basins. These connector nodes can represent open water connections (e.g.&nbsp;bifurcations or resistance in a free flowing open water channel) or infrastructure elements such as pumps, gates or weirs. An overview of node types and associated data inputs is provided on the <a href="../reference/usage.html">usage page</a>, while the associated mathematical formations are described on the <a href="../concept/equations.html">equations page</a>.</p>
 </section>
 <section id="sec-control" class="level2" data-number="2.2">
 <h2 data-number="2.2" class="anchored" data-anchor-id="sec-control"><span class="header-section-number">2.2</span> Control layer</h2>
@@ -323,10 +343,15 @@ <h2 data-number="2.3" class="anchored" data-anchor-id="sec-allocation"><span cla
 </div>
 
 
+
 </section>
 </section>
 
-</main> <!-- /main -->
+<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
+<div id="ref-REGGIANI1998367" class="csl-entry" role="listitem">
+Reggiani, Paolo, Murugesu Sivapalan, and S. Majid Hassanizadeh. 1998. <span>“A Unifying Framework for Watershed Thermodynamics: Balance Equations for Mass, Momentum, Energy and Entropy, and the Second Law of Thermodynamics.”</span> <em>Advances in Water Resources</em> 22 (4): 367–98. https://doi.org/<a href="https://doi.org/10.1016/S0309-1708(98)00012-8">https://doi.org/10.1016/S0309-1708(98)00012-8</a>.
+</div>
+</div></section></div></main> <!-- /main -->
 <script id="quarto-html-after-body" type="application/javascript">
 window.document.addEventListener("DOMContentLoaded", function (event) {
   const toggleBodyColorMode = (bsSheetEl) => {
diff --git a/dev/allocation.html b/dev/allocation.html
index 789e14dac..eea3422d7 100644
--- a/dev/allocation.html
+++ b/dev/allocation.html
@@ -254,11 +254,11 @@
     <h2 id="toc-title">On this page</h2>
    
   <ul>
-  <li><a href="#overview-of-allocation-implementation" id="toc-overview-of-allocation-implementation" class="nav-link active" data-scroll-target="#overview-of-allocation-implementation"><span class="header-section-number">1</span> Overview of allocation implementation</a>
+  <li><a href="#sec-allocation-overview" id="toc-sec-allocation-overview" class="nav-link active" data-scroll-target="#sec-allocation-overview"><span class="header-section-number">1</span> Overview of allocation implementation</a>
   <ul class="collapse">
-  <li><a href="#the-allocation-struct" id="toc-the-allocation-struct" class="nav-link" data-scroll-target="#the-allocation-struct"><span class="header-section-number">1.1</span> The <code>Allocation</code> struct</a></li>
-  <li><a href="#the-allocationmodel-struct" id="toc-the-allocationmodel-struct" class="nav-link" data-scroll-target="#the-allocationmodel-struct"><span class="header-section-number">1.2</span> The <code>AllocationModel</code> struct</a></li>
-  <li><a href="#jump.jl-problem-interaction" id="toc-jump.jl-problem-interaction" class="nav-link" data-scroll-target="#jump.jl-problem-interaction"><span class="header-section-number">1.3</span> <code>JuMP.jl</code> problem interaction</a></li>
+  <li><a href="#sec-allocation-struct" id="toc-sec-allocation-struct" class="nav-link" data-scroll-target="#sec-allocation-struct"><span class="header-section-number">1.1</span> The <code>Allocation</code> struct</a></li>
+  <li><a href="#sec-allocation-model-struct" id="toc-sec-allocation-model-struct" class="nav-link" data-scroll-target="#sec-allocation-model-struct"><span class="header-section-number">1.2</span> The <code>AllocationModel</code> struct</a></li>
+  <li><a href="#sec-jump-problem" id="toc-sec-jump-problem" class="nav-link" data-scroll-target="#sec-jump-problem"><span class="header-section-number">1.3</span> JuMP problem interaction</a></li>
   </ul></li>
   <li><a href="#initialization" id="toc-initialization" class="nav-link" data-scroll-target="#initialization"><span class="header-section-number">2</span> Initialization</a>
   <ul class="collapse">
@@ -267,10 +267,10 @@ <h2 id="toc-title">On this page</h2>
   <li><a href="#deriving-edge-capacities" id="toc-deriving-edge-capacities" class="nav-link" data-scroll-target="#deriving-edge-capacities"><span class="header-section-number">2.1.1</span> Deriving edge capacities</a></li>
   <li><a href="#handling-the-connection-between-the-main-network-and-subnetworks" id="toc-handling-the-connection-between-the-main-network-and-subnetworks" class="nav-link" data-scroll-target="#handling-the-connection-between-the-main-network-and-subnetworks"><span class="header-section-number">2.1.2</span> Handling the connection between the main network and subnetworks</a></li>
   </ul></li>
-  <li><a href="#the-optimization-problem" id="toc-the-optimization-problem" class="nav-link" data-scroll-target="#the-optimization-problem"><span class="header-section-number">2.2</span> The optimization problem</a>
+  <li><a href="#sec-optimization-problem" id="toc-sec-optimization-problem" class="nav-link" data-scroll-target="#sec-optimization-problem"><span class="header-section-number">2.2</span> The optimization problem</a>
   <ul class="collapse">
   <li><a href="#setting-up-the-optimization-variables" id="toc-setting-up-the-optimization-variables" class="nav-link" data-scroll-target="#setting-up-the-optimization-variables"><span class="header-section-number">2.2.1</span> Setting up the optimization variables</a></li>
-  <li><a href="#setting-up-initial-optimization-constraints" id="toc-setting-up-initial-optimization-constraints" class="nav-link" data-scroll-target="#setting-up-initial-optimization-constraints"><span class="header-section-number">2.2.2</span> Setting up initial optimization constraints</a></li>
+  <li><a href="#sec-initial-constraints" id="toc-sec-initial-constraints" class="nav-link" data-scroll-target="#sec-initial-constraints"><span class="header-section-number">2.2.2</span> Setting up initial optimization constraints</a></li>
   </ul></li>
   </ul></li>
   <li><a href="#optimization" id="toc-optimization" class="nav-link" data-scroll-target="#optimization"><span class="header-section-number">3</span> Optimization</a>
@@ -282,7 +282,7 @@ <h2 id="toc-title">On this page</h2>
   </ul></li>
   <li><a href="#looping-over-priorities" id="toc-looping-over-priorities" class="nav-link" data-scroll-target="#looping-over-priorities"><span class="header-section-number">3.2</span> Looping over priorities</a>
   <ul class="collapse">
-  <li><a href="#updating-capacities" id="toc-updating-capacities" class="nav-link" data-scroll-target="#updating-capacities"><span class="header-section-number">3.2.1</span> Updating capacities</a></li>
+  <li><a href="#sec-updating-capacities" id="toc-sec-updating-capacities" class="nav-link" data-scroll-target="#sec-updating-capacities"><span class="header-section-number">3.2.1</span> Updating capacities</a></li>
   </ul></li>
   <li><a href="#output-data" id="toc-output-data" class="nav-link" data-scroll-target="#output-data"><span class="header-section-number">3.3</span> Output data</a></li>
   <li><a href="#communicating-to-the-physical-layer" id="toc-communicating-to-the-physical-layer" class="nav-link" data-scroll-target="#communicating-to-the-physical-layer"><span class="header-section-number">3.4</span> Communicating to the physical layer</a>
@@ -316,7 +316,7 @@ <h1 class="title">Allocation</h1>
 </header>
 
 
-<section id="overview-of-allocation-implementation" class="level1" data-number="1">
+<section id="sec-allocation-overview" class="level1" data-number="1">
 <h1 data-number="1"><span class="header-section-number">1</span> Overview of allocation implementation</h1>
 <p>In this document, the allocation workflow is explained. Below is an overview of it.</p>
 <div class="cell" data-layout-align="default">
@@ -341,18 +341,18 @@ <h1 data-number="1"><span class="header-section-number">1</span> Overview of all
 </div>
 </div>
 </div>
-<p>If allocation is used in a model, <a href="https://deltares.github.io/Ribasim/dev/allocation.html#the-allocation-struct">Allocation structs</a> are created. The allocation struct stores the data that is needed for the calculations and stores also the results of the calculation. In allocation, optimization is an essential part. <code>JuMP.jl</code> is used to model and solve the optimization problems that are defined by allocation. The <a href="https://deltares.github.io/Ribasim/dev/allocation.html#the-allocation-struct">AllocationModel struct</a> is used for constructing the JuMP model. When an instance of <code>AllocationModel</code> is created, a JuMP optimization model is defined and initialized in the instance. More details on how allocation interacts with <code>JuMP.jl</code> is explained <a href="https://deltares.github.io/Ribasim/dev/allocation.html#jump.jl-problem-interaction">here</a>.</p>
+<p>If allocation is used in a model, <a href="#sec-allocation-struct">Allocation structs</a> are created. The allocation struct stores the data that is needed for the calculations and stores also the results of the calculation. In allocation, optimization is an essential part. <code>JuMP.jl</code> is used to model and solve the optimization problems that are defined by allocation. The <a href="#sec-allocation-model-struct">AllocationModel struct</a> is used for constructing the JuMP model. When an instance of <code>AllocationModel</code> is created, a JuMP optimization model is defined and initialized in the instance. More details on how allocation interacts with <code>JuMP.jl</code> is explained <a href="#sec-jump-problem">here</a>.</p>
 <p>After initialization, as the simulation starts, the allocation problem is solved and updated after every allocation timestep (which is specified in the TOML). With every allocation timestep a new optimization problem is formulated and solved, using the latest available (simulation) model conditions and forcing and demand predictions.</p>
 <p>The update of allocation (<code>update_allocation</code>) is repeating and spread into three parts:</p>
 <ul>
 <li>Updating the mean flows. The mean flow data is only used only for output, not used by any internal functions.</li>
-<li><a href="https://deltares.github.io/Ribasim/concept/allocation.html#the-high-level-algorithm">“Collect demand”</a>. This step initialize and solve the optimization problems that collects the demand from the subnetworks.</li>
-<li><a href="https://deltares.github.io/Ribasim/concept/allocation.html#the-high-level-algorithm">“Allocate”</a>. This step solves the optimization problems that allocates the demand. For the main network this step allocates to the subnetworks and demand nodes that are in the main network. For the subnetwork this step allocates to the demand nodes.</li>
+<li><a href="../concept/allocation.html#sec-high-level-algorithm">“Collect demand”</a>. This step initialize and solve the optimization problems that collects the demand from the subnetworks.</li>
+<li><a href="../concept/allocation.html#sec-high-level-algorithm">“Allocate”</a>. This step solves the optimization problems that allocates the demand. For the main network this step allocates to the subnetworks and demand nodes that are in the main network. For the subnetwork this step allocates to the demand nodes.</li>
 </ul>
 <p>The steps “collect demand” and “allocate” correspond to the function <code>collect_demand</code> and <code>allocate_demand</code> in the code.</p>
 <p>The iteration stops when it reaches the end time step.</p>
-<section id="the-allocation-struct" class="level2" data-number="1.1">
-<h2 data-number="1.1" class="anchored" data-anchor-id="the-allocation-struct"><span class="header-section-number">1.1</span> The <code>Allocation</code> struct</h2>
+<section id="sec-allocation-struct" class="level2" data-number="1.1">
+<h2 data-number="1.1" class="anchored" data-anchor-id="sec-allocation-struct"><span class="header-section-number">1.1</span> The <code>Allocation</code> struct</h2>
 <p>The <code>Allocation</code> struct stores necessary data and calculation results.</p>
 <table class="caption-top">
 <colgroup>
@@ -421,8 +421,8 @@ <h2 data-number="1.1" class="anchored" data-anchor-id="the-allocation-struct"><s
 </tbody>
 </table>
 </section>
-<section id="the-allocationmodel-struct" class="level2" data-number="1.2">
-<h2 data-number="1.2" class="anchored" data-anchor-id="the-allocationmodel-struct"><span class="header-section-number">1.2</span> The <code>AllocationModel</code> struct</h2>
+<section id="sec-allocation-model-struct" class="level2" data-number="1.2">
+<h2 data-number="1.2" class="anchored" data-anchor-id="sec-allocation-model-struct"><span class="header-section-number">1.2</span> The <code>AllocationModel</code> struct</h2>
 <p>The <code>AllocationModel</code> struct has all the data that is needed for the JuMP optimization problem.</p>
 <table class="caption-top">
 <colgroup>
@@ -461,16 +461,16 @@ <h2 data-number="1.2" class="anchored" data-anchor-id="the-allocationmodel-struc
 </tbody>
 </table>
 </section>
-<section id="jump.jl-problem-interaction" class="level2" data-number="1.3">
-<h2 data-number="1.3" class="anchored" data-anchor-id="jump.jl-problem-interaction"><span class="header-section-number">1.3</span> <code>JuMP.jl</code> problem interaction</h2>
+<section id="sec-jump-problem" class="level2" data-number="1.3">
+<h2 data-number="1.3" class="anchored" data-anchor-id="sec-jump-problem"><span class="header-section-number">1.3</span> JuMP problem interaction</h2>
 <p>When working with optimization problems using JuMP, there are three fundamental components that need to be defined:</p>
 <ul>
 <li>Optimization variables: These are the <a href="https://jump.dev/JuMP.jl/stable/api/JuMP/#@constraints">variables</a> that are optimized in the allocation problem formulation. They are defined using the <a href="https://jump.dev/JuMP.jl/stable/api/JuMP/#@variables"><code>@variable</code></a> macro. For example, to specify the flow rates in all the edges in the allocation network as variables:</li>
 </ul>
 <div class="sourceCode" id="cb1"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>problem[<span class="op">:</span>F] <span class="op">=</span> JuMP.<span class="pp">@variable</span>(problem, F[edge <span class="op">=</span> edges] <span class="op">&gt;=</span> <span class="fl">0.0</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<p>More details about setting up variables in allocation can be found in the section <a href="https://deltares.github.io/Ribasim/dev/allocation.html#optimization-problem">below</a>.</p>
+<p>More details about setting up variables in allocation can be found in the section <a href="#sec-optimization-problem">below</a>.</p>
 <ul>
-<li><p>Constraints: These are the constraints that the optimization variables must satisfy. They are defined using the <a href="https://jump.dev/JuMP.jl/stable/api/JuMP/#@constraints"><code>@constraint</code></a> macro. The definition of the edge capacity constraints is shown in section <a href="https://deltares.github.io/Ribasim/dev/allocation.html#setting-the-constraints-and-capacities">below</a>. <code>add_constraints_...</code> functions are used to <a href="https://deltares.github.io/Ribasim/dev/allocation.html#setting-up-initial-optimization-constraints">add constraints</a> to the optimization problem. The <a href="https://deltares.github.io/Ribasim/dev/allocation.html#setting-the-constraints-and-capacities">initial value of the constraints</a> is set in the function <code>set_initial_values_...</code>. During the iteration, the constraints are updated based on the current state of the allocation network. When <a href="https://deltares.github.io/Ribasim/dev/allocation.html#updating-capacities">looping over priorities</a>, the constraints are updated by the function <code>adjust_...</code>.</p></li>
+<li><p>Constraints: These are the constraints that the optimization variables must satisfy. They are defined using the <a href="https://jump.dev/JuMP.jl/stable/api/JuMP/#@constraint"><code>@constraint</code></a> macro. The definition of the edge capacity constraints is shown in section <a href="#sec-constraints-and-capacities">below</a>. <code>add_constraints_...</code> functions are used to <a href="#sec-initial-constraints">add constraints</a> to the optimization problem. The <a href="#sec-constraints-and-capacities">initial value of the constraints</a> is set in the function <code>set_initial_values_...</code>. During the iteration, the constraints are updated based on the current state of the allocation network. When <a href="#updating-capacities">looping over priorities</a>, the constraints are updated by the function <code>adjust_...</code>.</p></li>
 <li><p>Objective function: This is the function that sets the objective of the optimization problem. It is defined using the <a href="https://jump.dev/JuMP.jl/stable/api/JuMP/#@objective"><code>@objective</code></a> macro.</p></li>
 </ul>
 <p>The functions <code>JuMP.normalized_rhs</code> and <code>JuMP.set_normalized_rhs</code> are used to read and write the constant right hand side of constraints.</p>
@@ -498,8 +498,8 @@ <h3 data-number="2.1.2" class="anchored" data-anchor-id="handling-the-connection
 <p>The function <code>find_subnetwork_connetions</code> finds the edges that connected the main network to a subnetwork. <code>subnetwork_demands</code> and <code>subnetwork_allocateds</code> will be created, which stores demands and allocated values for subnetworks as a whole. <code>main_network_connections</code> is a vector of edges that connect a subnetwork with the main network.</p>
 </section>
 </section>
-<section id="the-optimization-problem" class="level2" data-number="2.2">
-<h2 data-number="2.2" class="anchored" data-anchor-id="the-optimization-problem"><span class="header-section-number">2.2</span> The optimization problem</h2>
+<section id="sec-optimization-problem" class="level2" data-number="2.2">
+<h2 data-number="2.2" class="anchored" data-anchor-id="sec-optimization-problem"><span class="header-section-number">2.2</span> The optimization problem</h2>
 <section id="setting-up-the-optimization-variables" class="level3" data-number="2.2.1">
 <h3 data-number="2.2.1" class="anchored" data-anchor-id="setting-up-the-optimization-variables"><span class="header-section-number">2.2.1</span> Setting up the optimization variables</h3>
 <p>There are three types of variables in the optimization problems:</p>
@@ -524,8 +524,8 @@ <h3 data-number="2.2.1" class="anchored" data-anchor-id="setting-up-the-optimiza
 <span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>    JuMP.<span class="pp">@variable</span>(problem, F_basin_out[node_id <span class="op">=</span> node_ids_basin,] <span class="op">&gt;=</span> <span class="fl">0.0</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <p>The last set of optimization variables is the flow edges in and out of the buffer of nodes with a flow demand. It is defined in a similar way to the second set of variables.</p>
 </section>
-<section id="setting-up-initial-optimization-constraints" class="level3" data-number="2.2.2">
-<h3 data-number="2.2.2" class="anchored" data-anchor-id="setting-up-initial-optimization-constraints"><span class="header-section-number">2.2.2</span> Setting up initial optimization constraints</h3>
+<section id="sec-initial-constraints" class="level3" data-number="2.2.2">
+<h3 data-number="2.2.2" class="anchored" data-anchor-id="sec-initial-constraints"><span class="header-section-number">2.2.2</span> Setting up initial optimization constraints</h3>
 <p>All the variables are greater and equal to 0. This is set when the variables are added to the optimization problem.</p>
 <p>Other constraints are <code>capacity</code>, <code>source_user</code>, <code>source</code>, <code>flow_conservation</code>, <code>fractional_flow</code>, <code>basin_outflow</code>, <code>flow_buffer_outflow</code> and <code>flow_demand_outflow</code>.</p>
 <p>For each set of constraints, a function named <code>add_constrains_[constraints name]</code> is created.</p>
@@ -599,8 +599,8 @@ <h3 data-number="3.1.2" class="anchored" data-anchor-id="setting-the-constraints
 </section>
 <section id="looping-over-priorities" class="level2" data-number="3.2">
 <h2 data-number="3.2" class="anchored" data-anchor-id="looping-over-priorities"><span class="header-section-number">3.2</span> Looping over priorities</h2>
-<section id="updating-capacities" class="level3" data-number="3.2.1">
-<h3 data-number="3.2.1" class="anchored" data-anchor-id="updating-capacities"><span class="header-section-number">3.2.1</span> Updating capacities</h3>
+<section id="sec-updating-capacities" class="level3" data-number="3.2.1">
+<h3 data-number="3.2.1" class="anchored" data-anchor-id="sec-updating-capacities"><span class="header-section-number">3.2.1</span> Updating capacities</h3>
 <p>While optimizing a given priority, the function <code>set_capacities_flow_demand_outflow</code> updates the constraints <code>flow_demand_outflow</code>. If the current priority is the same as the priority of the flow demand, constraints will be infinite, otherwise 0. At priorities where there is no flow demand, flow can go freely trough the node. When there is flow demand, flow is directed into the buffer. This is to make sure that flow can go to the node with the flow demand, even though the flow might have nowhere to go after that node.</p>
 <p>The optimization objective function is updated based on the new demands and the given priority.</p>
 <p>If a solution is found by the solver, the allocation result will be updated. And it will be <a href="../dev/allocation.html#output-data">saved</a>, so the <a href="../dev/allocation.html#communicating-to-the-physical-layer">physical layer</a> can make use of it.</p>
@@ -628,7 +628,7 @@ <h2 data-number="3.4" class="anchored" data-anchor-id="communicating-to-the-phys
 <p>The function <code>assign_allocations</code> updates the subnetwork demand if the optimization task is <code>collect_demands</code>. It assigns the allocated amount to the <code>UserDemand</code> nodes with the result of the optimization if the optimization task is <code>allocate</code>. Afterwards, it writes the resulting flow to the <code>Allocation</code> object.</p>
 <section id="userdemand-abstraction" class="level3" data-number="3.4.1">
 <h3 data-number="3.4.1" class="anchored" data-anchor-id="userdemand-abstraction"><span class="header-section-number">3.4.1</span> <code>UserDemand</code> abstraction</h3>
-<p>When allocation is active, the amount each <code>UserDemand</code> node is allowed to extract from its upstream basin is determined by the allocation algorithm. See <a href="https://deltares.github.io/Ribasim/reference/node/user-demand.html">here</a> for more details on how allocation updates the <code>UserDemand</code> node.</p>
+<p>When allocation is active, the amount each <code>UserDemand</code> node is allowed to extract from its upstream basin is determined by the allocation algorithm. See <a href="../reference/node/user-demand.html">here</a> for more details on how allocation updates the <code>UserDemand</code> node.</p>
 </section>
 <section id="controlling-pumpsweirs-based-on-allocation-results" class="level3" data-number="3.4.2">
 <h3 data-number="3.4.2" class="anchored" data-anchor-id="controlling-pumpsweirs-based-on-allocation-results"><span class="header-section-number">3.4.2</span> Controlling pumps/weirs based on allocation results</h3>
diff --git a/dev/release.html b/dev/release.html
index 0a28d371e..65438af27 100644
--- a/dev/release.html
+++ b/dev/release.html
@@ -306,7 +306,7 @@ <h2 data-number="2.2" class="anchored" data-anchor-id="update-version-numbers-of
 </section>
 <section id="update-the-changelog" class="level2" data-number="2.3">
 <h2 data-number="2.3" class="anchored" data-anchor-id="update-the-changelog"><span class="header-section-number">2.3</span> Update the changelog</h2>
-<p>The <code>docs/changelog.qmd</code> file, hosted on https://deltares.github.io/Ribasim/changelog, records the most important changes for users. Review the commits since the <a href="https://github.com/Deltares/Ribasim/releases/latest">latest Ribasim release</a> to make sure these are listed. Change the “Unreleased” section to the new version number and date, and create a new empty “Unreleased” section at the top.</p>
+<p>The <code>docs/changelog.qmd</code> file, hosted on <a href="https://ribasim.org/changelog">ribasim.org/changelog</a>, records the most important changes for users. Review the commits since the <a href="https://github.com/Deltares/Ribasim/releases/latest">latest Ribasim release</a> to make sure these are listed. Change the “Unreleased” section to the new version number and date, and create a new empty “Unreleased” section at the top.</p>
 </section>
 <section id="submit-a-pull-request" class="level2" data-number="2.4">
 <h2 data-number="2.4" class="anchored" data-anchor-id="submit-a-pull-request"><span class="header-section-number">2.4</span> Submit a pull request</h2>
@@ -350,7 +350,7 @@ <h2 data-number="2.7" class="anchored" data-anchor-id="do-manual-checks"><span c
 </section>
 <section id="announce-release" class="level2" data-number="2.8">
 <h2 data-number="2.8" class="anchored" data-anchor-id="announce-release"><span class="header-section-number">2.8</span> Announce release</h2>
-<p>Announce the release in appropriate channels. Include a link to the release notes and assets, which is whatever <a href="https://github.com/Deltares/Ribasim/releases/latest">this</a> resolves to at that time. Also include a link to the <a href="https://deltares.github.io/Ribasim/">documentation</a>.</p>
+<p>Announce the release in appropriate channels. Include a link to the release notes and assets, which is whatever <a href="https://github.com/Deltares/Ribasim/releases/latest">this</a> resolves to at that time. Also include a link to the <a href="https://ribasim.org/">documentation</a>.</p>
 
 
 </section>
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diff --git a/reference/node/basin.html b/reference/node/basin.html
index c10f6ed06..f4173781f 100644
--- a/reference/node/basin.html
+++ b/reference/node/basin.html
@@ -476,7 +476,7 @@ <h2 data-number="1.2" class="anchored" data-anchor-id="time"><span class="header
 <section id="interpolation" class="level3" data-number="1.2.1">
 <h3 data-number="1.2.1" class="anchored" data-anchor-id="interpolation"><span class="header-section-number">1.2.1</span> Interpolation</h3>
 <p>At the given timestamps the values are set in the simulation, such that the timeseries can be seen as forward filled.</p>
-<div id="1f218de8" class="cell" data-execution_count="1">
+<div id="edc6b135" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
@@ -675,7 +675,7 @@ <h3 data-number="1.4.1" class="anchored" data-anchor-id="interpolation-1"><span
 <section id="level-to-area" class="level4" data-number="1.4.1.1">
 <h4 data-number="1.4.1.1" class="anchored" data-anchor-id="level-to-area"><span class="header-section-number">1.4.1.1</span> Level to area</h4>
 <p>The level to area relationship is defined with the <code>Basin / profile</code> data using linear interpolation. An example of such a relationship is shown below.</p>
-<div id="2901bac8" class="cell" data-execution_count="2">
+<div id="a8cf0d39" class="cell" data-execution_count="2">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots()</span>
@@ -758,7 +758,7 @@ <h4 data-number="1.4.1.2" class="anchored" data-anchor-id="level-to-storage"><sp
 <p><span class="math display">\[
     S(h) = \int_{h_0}^h A(h')\text{d}h'.
 \]</span></p>
-<div id="042cd90c" class="cell" data-execution_count="3">
+<div id="5cc6cf35" class="cell" data-execution_count="3">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>storage <span class="op">=</span> np.diff(level) <span class="op">*</span> area[:<span class="op">-</span><span class="dv">1</span>] <span class="op">+</span> <span class="fl">0.5</span> <span class="op">*</span> np.diff(area) <span class="op">*</span> np.diff(level)</span>
@@ -803,7 +803,7 @@ <h4 data-number="1.4.1.2" class="anchored" data-anchor-id="level-to-storage"><sp
 <section id="interactive-basin-example" class="level4" data-number="1.4.1.3">
 <h4 data-number="1.4.1.3" class="anchored" data-anchor-id="interactive-basin-example"><span class="header-section-number">1.4.1.3</span> Interactive basin example</h4>
 <p>The profile data is not detailed enough to create a full 3D picture of the basin. However, if we assume the profile data is for a stretch of canal of given length, the following plot shows a cross section of the basin.</p>
-<div id="a98cdb88" class="cell" data-execution_count="4">
+<div id="d87cbf92" class="cell" data-execution_count="4">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.graph_objects <span class="im">as</span> go</span>
@@ -918,9 +918,9 @@ <h4 data-number="1.4.1.3" class="anchored" data-anchor-id="interactive-basin-exa
 <span id="cb5-110"><a href="#cb5-110" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </details>
 <div class="cell-output cell-output-display">
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@@ -1227,7 +1227,7 @@ <h2 data-number="2.1" class="anchored" data-anchor-id="sec-reduction_factor"><sp
     \end{cases}
 \end{align}\]</span></p>
 <p>Here <span class="math inline">\(p &gt; 0\)</span> is the threshold value which determines the interval <span class="math inline">\([0,p]\)</span> of the smooth transition between <span class="math inline">\(0\)</span> and <span class="math inline">\(1\)</span>, see the plot below.</p>
-<div id="6d7c1ac5" class="cell" data-execution_count="5">
+<div id="d84b894b" class="cell" data-execution_count="5">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
diff --git a/reference/node/discrete-control.html b/reference/node/discrete-control.html
index 3e93c86eb..f256ec32a 100644
--- a/reference/node/discrete-control.html
+++ b/reference/node/discrete-control.html
@@ -383,7 +383,7 @@ <h1 class="title">DiscreteControl</h1>
 
 
 <p>Set parameters of other nodes based on model state conditions (e.g.&nbsp;Basin level). The table below shows which parameters are controllable for a given node type.</p>
-<div id="021f3133" class="cell" data-execution_count="1">
+<div id="17b28e6c" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">using</span> <span class="bu">Ribasim</span></span>
diff --git a/reference/node/manning-resistance.html b/reference/node/manning-resistance.html
index 35c9930ea..6fe4486e7 100644
--- a/reference/node/manning-resistance.html
+++ b/reference/node/manning-resistance.html
@@ -533,7 +533,7 @@ <h1 data-number="2"><span class="header-section-number">2</span> Equations</h1>
   \end{cases}
 \end{align}
 \]</span></p>
-<div id="3c1970df" class="cell" data-execution_count="1">
+<div id="4c5c79e8" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
diff --git a/reference/node/tabulated-rating-curve.html b/reference/node/tabulated-rating-curve.html
index 8f8b65f76..5f8277100 100644
--- a/reference/node/tabulated-rating-curve.html
+++ b/reference/node/tabulated-rating-curve.html
@@ -474,7 +474,7 @@ <h2 data-number="1.1" class="anchored" data-anchor-id="static"><span class="head
 <section id="interpolation" class="level3" data-number="1.1.1">
 <h3 data-number="1.1.1" class="anchored" data-anchor-id="interpolation"><span class="header-section-number">1.1.1</span> Interpolation</h3>
 <p>The <span class="math inline">\(Q(h)\)</span> relationship of a tabulated rating curve is defined as a linear interpolation.</p>
-<div id="974e3404" class="cell" data-execution_count="1">
+<div id="e2a8575e" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
diff --git a/reference/test-models.html b/reference/test-models.html
index b1fe6743e..d5f06cf40 100644
--- a/reference/test-models.html
+++ b/reference/test-models.html
@@ -343,7 +343,7 @@ <h1 class="title">Test models</h1>
 
 
 <p>Ribasim developers use the following models in their testbench and in order to test new features.</p>
-<div id="09e9e62a" class="cell" data-execution_count="1">
+<div id="488b8420" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> ribasim_testmodels</span>
diff --git a/reference/validation.html b/reference/validation.html
index f81ff6c90..1a110717e 100644
--- a/reference/validation.html
+++ b/reference/validation.html
@@ -353,7 +353,7 @@ <h1 class="title">Validation</h1>
 <section id="connectivity" class="level1" data-number="1">
 <h1 data-number="1"><span class="header-section-number">1</span> Connectivity</h1>
 <p>In the table below, each column shows which node types are allowed to be downstream (or ‘down-control’) of the node type at the top of the column.</p>
-<div id="89e87782" class="cell" data-execution_count="1">
+<div id="09d470e8" class="cell" data-execution_count="1">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">using</span> <span class="bu">Ribasim</span></span>
@@ -703,7 +703,7 @@ <h1 data-number="1"><span class="header-section-number">1</span> Connectivity</h
 <section id="neighbor-amounts" class="level1" data-number="2">
 <h1 data-number="2"><span class="header-section-number">2</span> Neighbor amounts</h1>
 <p>The table below shows for each node type between which bounds the amount of in- and outneighbors must be, for both flow and control edges.</p>
-<div id="881a552e" class="cell" data-execution_count="2">
+<div id="f5cfc05b" class="cell" data-execution_count="2">
 <details class="code-fold">
 <summary>Code</summary>
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode julia code-with-copy"><code class="sourceCode julia"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>flow_in_min <span class="op">=</span> <span class="fu">Vector</span><span class="dt">{String}</span>()</span>
diff --git a/search.json b/search.json
index cc28d4d42..88d1897fa 100644
--- a/search.json
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@@ -99,7 +99,7 @@
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-    "text": "4.4 Example\nThe following is an example of an optimization problem for the example shown here:\n\n\nCode\nusing Ribasim\nusing Ribasim: NodeID\nusing SQLite\nusing ComponentArrays: ComponentVector\n\ntoml_path = normpath(@__DIR__, \"../../generated_testmodels/allocation_example/ribasim.toml\")\np = Ribasim.Model(toml_path).integrator.p\nu = ComponentVector(; storage = zeros(length(p.basin.node_id)))\n\nallocation_model = p.allocation.allocation_models[1]\nt = 0.0\npriority_idx = 1\n\nRibasim.set_flow!(p.graph, 1, 1.0, u)\nRibasim.set_objective_priority!(allocation_model, p, u, t, priority_idx)\nRibasim.set_initial_values!(allocation_model, p, u, t)\n\nprintln(p.allocation.allocation_models[1].problem)\n\n\nMin F[(Basin #5, UserDemand #6)]² + F[(Basin #2, UserDemand #3)]²\nSubject to\n F[(UserDemand #3, Basin #2)] ≥ 0\n F[(UserDemand #6, Basin #5)] ≥ 0\n F[(TabulatedRatingCurve #7, Terminal #8)] ≥ 0\n F[(Basin #2, LinearResistance #4)] ≥ 0\n F[(LinearResistance #4, Basin #2)] ≥ 0\n F[(LinearResistance #4, Basin #5)] ≥ 0\n F[(Basin #5, LinearResistance #4)] ≥ 0\n F[(Basin #5, UserDemand #6)] ≥ 0\n F[(FlowBoundary #1, Basin #2)] ≥ 0\n F[(Basin #5, TabulatedRatingCurve #7)] ≥ 0\n F[(Basin #2, UserDemand #3)] ≥ 0\n flow_conservation[Terminal #8] : F[(TabulatedRatingCurve #7, Terminal #8)] = 0\n flow_conservation[Basin #2] : F[(UserDemand #3, Basin #2)] - F[(Basin #2, LinearResistance #4)] + F[(LinearResistance #4, Basin #2)] + F[(FlowBoundary #1, Basin #2)] - F[(Basin #2, UserDemand #3)] = 0\n flow_conservation[Basin #5] : F[(UserDemand #6, Basin #5)] + F[(LinearResistance #4, Basin #5)] - F[(Basin #5, LinearResistance #4)] - F[(Basin #5, UserDemand #6)] - F[(Basin #5, TabulatedRatingCurve #7)] = 0\n flow_conservation[LinearResistance #4] : F[(Basin #2, LinearResistance #4)] - F[(LinearResistance #4, Basin #2)] - F[(LinearResistance #4, Basin #5)] + F[(Basin #5, LinearResistance #4)] = 0\n flow_conservation[TabulatedRatingCurve #7] : -F[(TabulatedRatingCurve #7, Terminal #8)] + F[(Basin #5, TabulatedRatingCurve #7)] = 0\n source[(FlowBoundary #1, Basin #2)] : F[(FlowBoundary #1, Basin #2)] ≤ 172800\n source_user[UserDemand #3] : F[(UserDemand #3, Basin #2)] ≤ 0\n source_user[UserDemand #6] : F[(UserDemand #6, Basin #5)] ≤ 0",
+    "text": "4.4 Example\nThe following is an example of an optimization problem for the example shown here:\n\n\nCode\nusing Ribasim\nusing Ribasim: NodeID\nusing SQLite\nusing ComponentArrays: ComponentVector\n\ntoml_path = normpath(@__DIR__, \"../../generated_testmodels/allocation_example/ribasim.toml\")\np = Ribasim.Model(toml_path).integrator.p\nu = ComponentVector(; storage = zeros(length(p.basin.node_id)))\n\nallocation_model = p.allocation.allocation_models[1]\nt = 0.0\npriority_idx = 1\n\nRibasim.set_flow!(p.graph, 1, 1.0, u)\nRibasim.set_objective_priority!(allocation_model, p, u, t, priority_idx)\nRibasim.set_initial_values!(allocation_model, p, u, t)\n\nprintln(p.allocation.allocation_models[1].problem)\n\n\nMin F[(Basin #2, UserDemand #3)]² + F[(Basin #5, UserDemand #6)]²\nSubject to\n F[(TabulatedRatingCurve #7, Terminal #8)] ≥ 0\n F[(Basin #5, TabulatedRatingCurve #7)] ≥ 0\n F[(Basin #2, UserDemand #3)] ≥ 0\n F[(UserDemand #3, Basin #2)] ≥ 0\n F[(Basin #2, LinearResistance #4)] ≥ 0\n F[(LinearResistance #4, Basin #2)] ≥ 0\n F[(Basin #5, UserDemand #6)] ≥ 0\n F[(FlowBoundary #1, Basin #2)] ≥ 0\n F[(UserDemand #6, Basin #5)] ≥ 0\n F[(LinearResistance #4, Basin #5)] ≥ 0\n F[(Basin #5, LinearResistance #4)] ≥ 0\n flow_conservation[LinearResistance #4] : F[(Basin #2, LinearResistance #4)] - F[(LinearResistance #4, Basin #2)] - F[(LinearResistance #4, Basin #5)] + F[(Basin #5, LinearResistance #4)] = 0\n flow_conservation[Terminal #8] : F[(TabulatedRatingCurve #7, Terminal #8)] = 0\n flow_conservation[Basin #5] : -F[(Basin #5, TabulatedRatingCurve #7)] - F[(Basin #5, UserDemand #6)] + F[(UserDemand #6, Basin #5)] + F[(LinearResistance #4, Basin #5)] - F[(Basin #5, LinearResistance #4)] = 0\n flow_conservation[TabulatedRatingCurve #7] : -F[(TabulatedRatingCurve #7, Terminal #8)] + F[(Basin #5, TabulatedRatingCurve #7)] = 0\n flow_conservation[Basin #2] : -F[(Basin #2, UserDemand #3)] + F[(UserDemand #3, Basin #2)] - F[(Basin #2, LinearResistance #4)] + F[(LinearResistance #4, Basin #2)] + F[(FlowBoundary #1, Basin #2)] = 0\n source[(FlowBoundary #1, Basin #2)] : F[(FlowBoundary #1, Basin #2)] ≤ 172800\n source_user[UserDemand #6] : F[(UserDemand #6, Basin #5)] ≤ 0\n source_user[UserDemand #3] : F[(UserDemand #3, Basin #2)] ≤ 0",
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@@ -1186,7 +1186,7 @@
     "href": "dev/release.html#update-the-changelog",
     "title": "Release process",
     "section": "2.3 Update the changelog",
-    "text": "2.3 Update the changelog\nThe docs/changelog.qmd file, hosted on https://deltares.github.io/Ribasim/changelog, records the most important changes for users. Review the commits since the latest Ribasim release to make sure these are listed. Change the “Unreleased” section to the new version number and date, and create a new empty “Unreleased” section at the top.",
+    "text": "2.3 Update the changelog\nThe docs/changelog.qmd file, hosted on ribasim.org/changelog, records the most important changes for users. Review the commits since the latest Ribasim release to make sure these are listed. Change the “Unreleased” section to the new version number and date, and create a new empty “Unreleased” section at the top.",
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     "text": "The Allocation struct stores necessary data and calculation results.\n\n\n\n\n\n\n\n\nfield\ntype\ndescription\n\n\n\n\nsubnetwork_ids\nVector{Int32}\nThe unique sorted allocation network IDs\n\n\nallocation_models\nAllocationModel\nThe allocation models for the main network and subnetworks corresponding to subnetwork_ids\n\n\nmain_network_connections\nVector{Vector{Tuple{NodeID, NodeID}}}\n(from_id, to_id) from the main network to the subnetwork per subnetwork\n\n\npriorities\nVector{Int32}\nAll used priority values.\n\n\nsubnetwork_demands\nDict{Tuple{NodeID, NodeID}, Vector{Float64}}\nThe demand of an edge from the main network to a subnetwork\n\n\nsubnetwork_allocateds\nDict{Tuple{NodeID, NodeID}, Vector{Float64}}\nThe allocated flow of an edge from the main network to a subnetwork\n\n\nmean_input_flows\nDict{Tuple{NodeID, NodeID}, Float64}\nFlows averaged over Δt_allocation over edges that are allocation sources\n\n\nmean_realized_flows\nDict{Tuple{NodeID, NodeID}, Float64}\nFlows averaged over Δt_allocation over edges that realize a demand\n\n\nrecord_demand\n\nA record of demands and allocated flows for nodes that have these\n\n\nrecord_flow\n\nA record of all flows computed by allocation optimization, eventually saved to output file",
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+    "href": "dev/allocation.html#sec-allocation-model-struct",
     "title": "Allocation",
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     "text": "The AllocationModel struct has all the data that is needed for the JuMP optimization problem.\n\n\n\n\n\n\n\n\nfield\ntype\ndescription\n\n\n\n\nsubnetwork_id\nInt32\nThe ID of this allocation network\n\n\ncapacity\nJuMP.Containers.SparseAxisArray\nThe capacity per edge of the allocation network, as constrained by nodes that have a max_flow_rate\n\n\nproblem\nJuMP.Model\nThe JuMP.jl model for solving the allocation problem\n\n\nΔt_allocation\nFloat64\nThe time interval between consecutive allocation solves",
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     "title": "Allocation",
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     "text": "When working with optimization problems using JuMP, there are three fundamental components that need to be defined:\n\nOptimization variables: These are the variables that are optimized in the allocation problem formulation. They are defined using the @variable macro. For example, to specify the flow rates in all the edges in the allocation network as variables:\n\nproblem[:F] = JuMP.@variable(problem, F[edge = edges] &gt;= 0.0)\nMore details about setting up variables in allocation can be found in the section below.\n\nConstraints: These are the constraints that the optimization variables must satisfy. They are defined using the @constraint macro. The definition of the edge capacity constraints is shown in section below. add_constraints_... functions are used to add constraints to the optimization problem. The initial value of the constraints is set in the function set_initial_values_.... During the iteration, the constraints are updated based on the current state of the allocation network. When looping over priorities, the constraints are updated by the function adjust_....\nObjective function: This is the function that sets the objective of the optimization problem. It is defined using the @objective macro.\n\nThe functions JuMP.normalized_rhs and JuMP.set_normalized_rhs are used to read and write the constant right hand side of constraints.\nFor example, to update the capacity of one of the edges, JuMP.normalized_rhs moves all the constants to the right-hand sides and all variables to the left-hand side and JuMP.set_normalized_rhs sets the new right-hand-side value.\nJuMP.set_normalized_rhs(\n    constraints_capacity[edge_id],\n    JuMP.normalized_rhs(constraints_capacity[edge_id]) - JuMP.value(F[edge_id]),\n)\nSome JuMP data structures are used to store intermediate or result data. For more information, see JuMP API.",
@@ -1303,8 +1303,8 @@
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     "title": "Allocation",
     "section": "2.2 The optimization problem",
     "text": "2.2 The optimization problem\n\n2.2.1 Setting up the optimization variables\nThere are three types of variables in the optimization problems:\n\nflows between the edges in the allocation model\nflows in and out of a basin with a level demand\nflows in and out of nodes that have a buffer, which are nodes that have a flow demand\n\nThe function add_variables_flow is used to add the variable of flows between the edges. The variables are obtained from the capacity array. And variables named by F($startnode, $endnode) are created.\nedges = keys(capacity.data)\nproblem[:F] = JuMP.@variable(problem, F[edge = edges] &gt;= 0.0)\nIn the function add_variables_basin, variables that represent flows of those basins that are connected with level demand are defined. Part of the function is shown in the code block below. A variable is named F_basin_in if the corresponding basin is supplied by a level demand and F_basin_out if consumed by a level demand.\n# Get the node IDs from the subnetwork for basins that have a level demand\nnode_ids_basin = [\n    node_id for\n    node_id in graph[].node_ids[subnetwork_id] if graph[node_id].type == :basin &&\n    has_external_demand(graph, node_id, :level_demand)[1]\n]\nproblem[:F_basin_in] =\n    JuMP.@variable(problem, F_basin_in[node_id = node_ids_basin,] &gt;= 0.0)\nproblem[:F_basin_out] =\n    JuMP.@variable(problem, F_basin_out[node_id = node_ids_basin,] &gt;= 0.0)\nThe last set of optimization variables is the flow edges in and out of the buffer of nodes with a flow demand. It is defined in a similar way to the second set of variables.\n\n\n2.2.2 Setting up initial optimization constraints\nAll the variables are greater and equal to 0. This is set when the variables are added to the optimization problem.\nOther constraints are capacity, source_user, source, flow_conservation, fractional_flow, basin_outflow, flow_buffer_outflow and flow_demand_outflow.\nFor each set of constraints, a function named add_constrains_[constraints name] is created.\nTake add_constraints_user_source as an example, the nodes that are relevant for the constraints are added to the optimization problem by calling JuMP.@constraint.\nnode_ids_user = [node_id for node_id in node_ids if node_id.type == NodeType.UserDemand]\n\nproblem[:source_user] = JuMP.@constraint(\n    problem,\n    [node_id = node_ids_user],\n    F[(node_id, outflow_id(graph, node_id))] &lt;= 0.0,\n    base_name = \"source_user\"\n)",