To make this algorithm applicable to the hypergraph layout problem, [PERSON] [6] first proposed reordering the nodes on each layer to minimize the number of edge crossings, and then calculated the preliminary coordinates for the nodes and segments to avoid hyperedge crossings, finally obtaining the coordinates that rendered a balanced drawing. In [7], an exact formulation of the mathematical problem of hyperedge routing was proposed, and it satisfied the constraints discussed above. The final step of the hypergraph drawing algorithm is assigning each upper vertical segment to a port of its source node and each lower vertical segment to a port of its target node. Computing the optimal order of vertices for all layers of a graph is a highly time-consuming procedure. That is why it is reasonable to reorder vertices in each layer separately to minimize the number of edge crossings related to one of adjacent layers. A heuristic proposed in [8] was improved in further work; see [9]. Plenty of exact and heuristic algorithms have been presented for the minimization of edge crossings by reordering nodes on layers; see, e.g., [8; 10]. However, application of these methods for a hypergraph might not provide acceptable results. It is known that decreasing the number of edge crossings does not ensure the same for the corresponding hypergraph [11]; see, for instance, Figures 2 and 3, which present examples of single-source one-port hypergraphs. Figure 1: An example of the layering of a hierarchical hypergraph with orthogonal hyperedges according to the accepted rules. Figure 3: The same reordering nodes on the lower layer of the corresponding hypergraph leads to an increase in the number of hyperedge crossings: one crossing for the layout (**a**) and two crossings for the layout (**b**). An additional optimization problem arises if one takes ports for the source and target nodes into account because each vertical segment of a hyperedge must be assigned to a unique port. This problem should be considered as a part of a hierarchical hypergraph drawing problem, together with the reordering of nodes and horizontal segments. Figures 4 and 5 show three layouts of the same hypergraph: without ports (optimal layout), with the assignment of ports as a result of a post-processing procedure, and, finally, with the optimal assignment of ports. The reordering heuristic by [PERSON] et al. [12] only gives a local minimum of the number of hyperedge crossings. [PERSON] et al. [11] proposed an algorithm for quite accurate (but not exact) counting of hyperedge crossings depending on the order of nodes without computing the hyperedge routing. A hyperedge drawing is applied in a variety of practical areas: from cooperation scheme drawing and information and financial flow visualization to schematics of displays of automotive communication networks and VLSI design. Interesting applications of the hypergraph layout problem can be found in the development of effective routing protocols for IoT applications and in the development of energy-balanced routing protocols for terrestrial/underwater wireless sensor networks, as well as in the assessment of their safety. The information system for the national investment projects that started in Russia in 2015 is an example of graphical data analysis [7]. One of the goals of this information system is to control all financial transactions and contracts related to large-scale national projects, in which hundreds of companies are involved. The embedded software visualizes companies as nodes of a hierarchical hypergraph and their business links and financial transactions as directed hyperedges. This visualization scheme enables one to highlight potential risks and to estimate the status of a project under consideration. It is clear that this visualization is only helpful as an analytical tool if it is easy to understand. It is more to the point that the hypergraph density is high.