The Terminal Location Problem in the Forest Fuels Supply Network
Authors
Peter Rauch
BOKU University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, 1180 Vienna, Austria
Manfred Gronalt
BOKU University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, 1180 Vienna, Austria
Abstract
Being sustainably available and CO2-neutral, woody biomass is becoming increasingly more important as an alternative energy source on a worldwide basis. However, despite broad acceptance of bioenergy plants in Austria, more and more neighboring residents are lodging a protest because of the noise and dust burden during wood-chipping operations. These circumstances force plant operators to utilize separate terminals for storing and chipping forest wood, in turn resulting in a redesigning of the forest fuel supply chains. The present paper focuses on the choice of spatial arrangement and the type of terminals used. For redesigning the forest fuels supply network, a Mixed Integer Linear Programming (MILP) model was developed and subsequently implemented for a study region. The network consists of direct supplies from the forest for combined heat and power facilities (CHP) and indirect supply lines via terminals. The MILP model provides a cost-optimal spatial arrangement of terminals by considering different terminal types with respect to spatial context, chipping technology, and the volume processed. Different scenarios are used to test the robustness of the network design. A simulation of a transportation cost increase shows that the optimal network design is stable within an increase of 20 to 50% and between 70 and 110%. At other levels of increase, the number of terminals used decreases. Furthermore, the number of terminals decreases as the domestic forest timber utilization rate increases. It was possible to demonstrate that industrial terminals offer considerable saving potentials. Therefore, the cooperation of CHP operators with a forest-based industry partner as a terminal provider is one of main management implications of the study results.
