diff --git a/docs/src/developers/roadmap.md b/docs/src/developers/roadmap.md
index 2ff09876ae5..881741b664e 100644
--- a/docs/src/developers/roadmap.md
+++ b/docs/src/developers/roadmap.md
@@ -25,7 +25,7 @@ represent broad themes that we see as areas in which JuMP could be improved.
    [https://github.com/jump-dev/JuMP.jl/issues/2227](https://github.com/jump-dev/JuMP.jl/issues/2227)
    JuMP has a strong focus on linear, conic and nonlinear optimization problems.
    We want to add better support for constraint programming.
- - Add support for multiobjective problems:
+ - **Done [#3176](https://github.com/jump-dev/JuMP.jl/pull/3176)** Add support for multiobjective problems:
    [https://github.com/jump-dev/JuMP.jl/issues/2099](https://github.com/jump-dev/JuMP.jl/issues/2099)
    JuMP is restricted to problems with scalar-valued objectives. We want to
    extend this to vector-valued problems.
diff --git a/docs/src/should_i_use.md b/docs/src/should_i_use.md
index 8e7a8cf905d..e78d1d8ae62 100644
--- a/docs/src/should_i_use.md
+++ b/docs/src/should_i_use.md
@@ -12,7 +12,7 @@ You should use JuMP if you have a constrained optimization problem for which you
 can formulate using the language of mathematical programming, that is:
 
  * a set of decision variables
- * a scalar objective function
+ * a scalar- or vector-valued objective function
  * a set of constraints.
 
 Key reasons to use JuMP include:
@@ -110,15 +110,6 @@ Alternatives to consider are:
  * [InfiniteOpt.jl](https://github.com/infiniteopt/InfiniteOpt.jl)
  * [pyomo.DAE [Python]](https://pyomo.readthedocs.io/en/stable/modeling_extensions/dae.html)
 
-### Multiobjective programs
-
-If your problem has more than one objective, JuMP is not the right tool for the
-job. However, [we're working on fixing this!](https://github.com/jump-dev/JuMP.jl/issues/2099).
-
-Alternatives to consider are:
-
- * [vOptGeneric.jl](https://github.com/vOptSolver/vOptGeneric.jl)
-
 ### Disciplined convex programming
 
 JuMP does not support [disciplined convex programming (DCP)](https://dcp.stanford.edu).