diff --git a/docs/make.jl b/docs/make.jl
index c213c1bb7c7..0927056ec24 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -22,10 +22,13 @@ end
 @time if _PDF || _IS_GITHUB_ACTIONS
     # Copy the src into a new directory for the latex build
     cp(joinpath(@__DIR__, "src"), joinpath(@__DIR__, "latex_src"); force = true)
-    f_latex = Distributed.@spawnat :any make_latex()
-    f_html = Distributed.@spawnat :any make_html()
-    fetch(f_latex)
-    fetch(f_html)
+    # w = Distributed.workers()
+    # f_latex = Distributed.remotecall(make_latex, w[1])
+    # f_html = Distributed.remotecall(make_html, w[2])
+    # fetch(f_latex)
+    # fetch(f_html)
+    make_html()
+    make_latex()
     # Hack for deploying: copy the pdf (and only the PDF) into the HTML build
     # directory! We don't want to copy everything in `latex_build` because it
     # includes lots of extraneous LaTeX files.
diff --git a/docs/src/tutorials/linear/sports_scheduling.jl b/docs/src/tutorials/linear/sports_scheduling.jl
index fa58b6611df..1315bb8291e 100644
--- a/docs/src/tutorials/linear/sports_scheduling.jl
+++ b/docs/src/tutorials/linear/sports_scheduling.jl
@@ -24,8 +24,9 @@
 # in 2016 as part of an operations research course at the University of
 # Wisconsin-Madison.**
 
-# The purpose of this tutorial is to demonstrate a simple model for scheduling
-# round-robin tournaments. As teams, it uses the [Big 10](https://en.wikipedia.org/wiki/Big_Ten_Conference).
+# The purpose of this tutorial is to demonstrate a simple mixed-integer linear
+# program for scheduling round-robin tournaments. As teams, it uses University
+# teams from the [Big Ten Conference](https://en.wikipedia.org/wiki/Big_Ten_Conference).
 # (You might notice that there are more than 10 teams. Our example was also
 # written before the expansion of the Conference in 2024.)
 
@@ -41,10 +42,20 @@ import HiGHS
 # Here are the teams in our tournament:
 
 M = [
-    #!format:off
-    "Ind", "UMD", "UMich", "MSU", "OSU", "Penn", "Rtgrs", "Ill", "Iowa", "UMN",
-    "UNL", "NU", "Purd", "UW",
-    #!format: on
+    "Ill",      # U. Illinois Urbana-Champaign
+    "Ind",      # Indiana University Bloomington
+    "Iowa",     # University of Iowa
+    "MSU",      # Michigan State University
+    "NU",       # Northwestern University
+    "OSU",      # The Ohio State Univesity
+    "Penn",     # Pennsylvania State University
+    "Purd",     # Purdue University
+    "Rtgrs",    # Rutgers University
+    "UMD",      # University of Maryland, College Park
+    "UMich",    # University of Michigan
+    "UMN",      # University of Minnesota Twin Cities
+    "UNL",      # University of Nebraska-Lincoln
+    "UW",       # University of Wisconsin-Madison
 ];
 
 # For each team to play each other exactly once, we need the number of teams - 1
@@ -85,6 +96,19 @@ model = Model(HiGHS.Optimizer);
     sum(x[m, n, :]) + sum(x[n, m, :]) == 1,
 );
 
+# One problem with out model is that there is a lot of symmetry. To make the
+# problem easier to solve, we fix the schedule of a random team to play every
+# team in order, alternating between home and away:
+
+m = rand(M)
+for (t, n) in enumerate(filter(!=(m), M))
+    if isodd(t)
+        fix(x[m, n, t], 1)
+    else
+        fix(x[n, m, t], 1)
+    end
+end
+
 # Now we can solve our model:
 
 set_silent(model)
@@ -113,7 +137,8 @@ Y = round.(Bool, value.(x))
 print_schedule(M, T, Y)
 
 # This schedule is okay, but it features a large number of back-to-back away
-# games. Let's count them:
+# games (in which a team plays away from home two weeks in a row). Let's count
+# them:
 
 number_of_back_to_back_away_games =
     sum(round(Int, value(sum(x[:, m, (t-1):t]))) == 2 for m in M, t in 2:T)