Dynamic reduction of network flow optimization problem: Case of waste-to-energy infrastructure planning in Czech Republic

• A general approach to maintaining the complexity of network flow problems. • Variable network detail with emphasis on selected regions. • Transformation of node coordinates with subsequent application of cluster analysis. • Integration of key parameters to reduce errors caused by node aggregation. • Validation of the approach through a Waste-to-Energy plant planning case study. Nowadays, many sophisticated tools based on various mathematical approaches are used to support planning and strategic decision-making. In the field of waste management, allocation and location problems based mainly on the structure network flow problem are used with respect to infrastructure planning. Modern formulations of the problem allow the inclusion of integer and nonlinear constraints that reflect real-world operations. However, despite the advanced computational technology, such real-world problems are difficult to solve in adequate detail due to the large scale of the problem. Thus, the links in the system are simplified, but most often a transport network is aggregated. The individual nodes in the system may then represent areas with tens or hundreds of thousands of inhabitants, which does not provide sufficient insight for location tasks. This paper presents an approach to dynamically reduce the network with respect to selected points of interest. The selected areas are modeled in greater detail, while with increasing distance the entities are more aggregated into larger units. The approach is based on a transformation of the original network and subsequent cluster analysis, preferably using existing transport infrastructure. The presented approach provides the possibility of practical application of complex tools that are currently mostly theoretical due to high computational demands. The methodology is applied to a case study of Waste-to-Energy infrastructure planning, which needs to model a large area to fill a large capacity facility.