The traveling salesman problem (TSP) is a classical combinatorial optimization problem that involves finding the shortest or fastest route among a set of cities. To formalize the uncertainty and imprecision in input data, often caused by subjective evaluations of the travel time intervals, this paper employs fuzzy numbers. The form of these fuzzy numbers is based on a Gaussian-like approach. This work examines the specifics of applying the ant colony optimization (ACO) algorithm and proposes an approach for its optimal use. The impact of the algorithm's parameters on the quality of the approximated best solution is analyzed. The problem is illustrated with numerical examples involving a sufficiently large number of
cities in the transportation network.