An Optimization Technique

This essay aims to assess the various methods employed in the grouping of harvesting stands inside a forest plantation. The issue is that integrating clusters for harvesting within a forest is really difficult (Augustynczik, Arce & Silva, 2016). It is expensive to tend and gather the trees when the harvesting stands are divided. It is essential to have stands grouped together and in one location in order to lower the financial costs of managing and harvesting within a forest. The possibility of merging harvesting stands and building roadways adjacent to these gathering locations is something the researchers are trying to measure. An Integer Linear Programming (ILP) model is used to solve for the two approaches in this research. First, the researchers use ILP to find ways of reducing the distance between harvesting stands. ILP is also utilized in the development of a model that would be used to maximize the adjacencies of the harvesting stands. ILP is particularly employed in the quantification of the costs of creating roads and the development of minimal distances between the harvesting stands. Using the ILP formula as the objective equation, the researchers quantify for the use of minimal resources in the development of roads and adjacent harvesting stands. This is common in ILP equations (Diwekar, 2008).

Using several scenarios, the researchers show that it is possible to aggregate harvesting stands in one area so as to reduce the costs of collecting the trees. Using the ILP model, the researchers come up with several scenarios that show that reducing the distance between one harvesting stand and another as well as building roads will reduce the cost of harvesting trees in a forest plantation.

References

Augustynczik, A.L.D., Arce, J.E., & Silva, A.C. (2016). Aggregating forest harvesting activities in forest plantations through integer linear programming and goal programming. Journal of Forest Economics, 24, 72–81.

Diwekar, U. (2008). Introduction to applied optimization: Springer optimization and its applications. New York: Springer.