Considering you have a set of n variables (elements) $p_1, p_2, …, p_n$ to be sorted in ascending order so that
$p_{[1]}, p_{[2]}, …, p_{[n]}$
where $p_{[1]} \leq p_{[2]} \leq …\leq p_{[n]}$,
you can introduce $n^2$ binary decision variable $ x_{i,j}$ and $n$ variables $A_i$.
$ x_{i,j}=1$ if $i-th$ element is assigned to $j-th$ position.
$ min \left \{ \sum_{i = 1}^n A_i \right \} $
Subject to
:$ \left\{ \begin{array}{l}
A_1 = \sum_{i=1}^n x_{i,1} \cdot \ p_{i} \\
A_2 = A_1 + \sum_{i=1}^n x_{i,2} \cdot \ p_{i} \\
\vdots \\
A_n = A_{n-1} + \sum_{i=1}^n x_{i,n} \cdot \ p_{i} \\
\sum_{i=1}^n x_{i,k} = 1 \forall \ k \\
\sum_{k=1}^n x_{i,k} = 1 \forall \ i \\
A_i \ge 0 \forall \ i \\
x_{i,k} \in \left \{ 0 ; 1 \right \} \forall \ i, k
\end{array} \right.$
What it has been written works for example in one machine scheduling problem where $p_1, p_2, …, p_n$ represent processing times.
For example: the set $p_1=16.2, p_2=16, p3=16.1 , p_4=15.9, p_5=15.7, p_6=15.8 $ is easily ordered by the model as $p_{[1]}=15.7, p_{[2]}=15.8, …, p_{[6]}=16.2$
The element in first position is equal to $p_{[1]} = \sum_{i=1}^n x_{i,1} \cdot \ p_{i}$
The element in second position is equal to $p_{[2]} = \sum_{i=1}^n x_{i,2} \cdot \ p_{i}$
The element in third position is equal to $p_{[3]} = \sum_{i=1}^n x_{i,3} \cdot \ p_{i}$
...
Remark
If you wish to sort in descending order the set, it is sufficient to consider $ max \left \{ \sum_{i = 1}^n A_i \right \} $.