Suppose that there are $n$ objects, for simplicity labeled 1 to $n$. You have an unbiased $k$ sided dice.
What is the minimum number of times you need to roll the dice to get an algorithm that samples a uniformly random permutation of $[n] = \{1,2,\dots, n\}$?
For a $k$ sided dice, my intuition is that using one dice roll you can totally order $x$ numbers such that $x! \leq k$ and $(x+1)! > k$.
So the minimum number of $k$ sided dice rolls needed would be $\sum_{i=0}^{ log_x(n)-1} x^i = \frac{n-1}{x-1}$ using a similar tree structure.
This makes sense to me for when $k$ is a "perfect" factorial (i.e. a factorial of a natural number) since there are no extra numbers on the dice that could come up and when $n$ is a perfect power of $k$. But what about when this doesn't hold?
Edit: Actually, the algorithm above doesn't work but I would still like to know the answer to the original question
The answer is to express $n$ in base-$k$. Then you can express $n$ with $\log_k(n)$ base-$k$ numerals and just roll a $k$-sided die for each numeral.
Obviously, $\log_k(n)$ might not be an integer. In that case, you just round up. Thus the number of dice to roll $d$ is given by: $$ d = \lceil \log_k(n) \rceil. $$
Then do like so: roll $d$ dice. If the base-$k$ number implied by the rolls is greater than $n$, reject it and reroll all $d$ dice. (That is important; otherwise your sample will not be uniformly drawn.)
After this is done, relabel the remaining numbers in $\{1,\ldots,n\}$, decrement the set size to $n-1$, and redo the above. Your best case is you make $\sum_{i=2}^n\lceil\log_k(i)\rceil$ die rolls.