This is an instance of the Weber problem: Where to locate a facility such that the total weighted transportation cost to the clients is minimal? In this case, the facility will be located in $(a,b)$ and the locations are in $(0,0)$, $(1,0)$, and $(3,4)$, with weights 1, 2, and 1.
"A historical perspective on location problems" discusses the history of this problem, and mentions iterative algorithms, but also analog approaches. The analog approach is explained in this paper for example:
A map of the area in question is placed on a board, and holes are drilled in the points where the demand locations are. Strings are passed through the holes, and weights proportional to the economic “weights” are hung on them. The other edges of the strings are tied together. It is quite obvious that the stationary situation reached after possibly a few oscillations is the equilibrium point, namely, the solution of the minimization problem. Obviously, the accuracy of the method is limited by the friction of the strings in the holes, and, in fact, it looks quite primitive when the alternative of an efficient numerical procedure is available.