We introduce a fast and robust algorithm for finding a plane Γ with given normal nΓ, which truncates an arbitrary polyhedron P such that the remaining sub-polyhedron admits a given volume α⁎|P|. In the literature, this is commonly referred to as Volume-of-Fluid (VOF) interface positioning problem. The novelty of our work is twofold: firstly, we combine a recursive application of the Gaussian divergence theorem with a dynamic choice of the coordinate origin. This allows to compute the volume of a truncated polyhedron at high efficiency, based on summation of binary products associated to the faces of the polyhedron. One obtains a very convenient piecewise parametrization (within so-called brackets) in terms of the signed distance s to the plane Γ. As an implication, one can restrain from the costly necessity to establish topological connectivity, rendering the present approach most suitable for the application to unstructured computational meshes. In the vicinity of the truncation position s, the volume can be expressed exactly, i.e. in terms of a cubic polynomial of the normal distance to the PLIC plane. While this was exploited in previous publications, the local knowledge of derivatives enables to construct a root-finding scheme that pairs bracketing with a higher-order approximation. The latter constitutes the second novelty, which turns out to be crucial in terms of efficiency. The performance is assessed by conducting an extensive set of numerical experiments, considering convex and non-convex polyhedra of genus (i.e., number of holes) zero and one in combination with carefully selected volume fractions α⁎ (including α⁎≈0 and α⁎≈1) and normal orientations nΓ. For all configurations we obtain a significant reduction of the number of (computationally costly) truncations required for the positioning. On average, our algorithm requires between one and two polyhedron truncations to find the position of the plane Γ, outperforming existing methods. In terms of absolute execution times, the proposed algorithm induces speed-ups of up to 63% in comparison to existing methods.