Refocusing on Operational Harvest Planning Model for State-Owned Forestry in Turkey

The growing concerns on forest ecosystem services and sustainable management of the resources with workforce, material, and products require effective planning of forestry operations in a hierarchical level. Operational planning as a component of the hierarchy generates short-term harvest planning decisions to minimize total costs by making production and distribution decisions during all seasons. Operational harvest planning of wood harvesting has been not used in Turkish conditions. Many developments and changes in managerial and operational processes in Turkish state forestry require the right product in the right place at the right time. This indicates that it is time to use operational planning to solve the wood harvesting problem with respect to specific conditions of Turkish forestry. This study introduces a model for annual planning of harvest operations/operational harvest planning (OHARP) from stand to storage for a one-year time horizon. The article presents how the operational decisions can be optimized for selection of the most appropriate harvesting blocks, time, system, landing location, and transportation mode to provide the best balance between time and cost. The mathematical model of the planning problem was formulated with linear and mixed integer programming techniques. The data for the model comes from the forest planning units and operation systems which is combined to minimize total supply costs subject to technical, environmental and socio-economic constraints. The model was tested with the real harvesting data from a forest district in the Mediterranean Region for a one year planning horizon. The test results demonstrated that when the OHARP model was implemented in the test area and compared with the actual cost of the harvest operations realized in this area, a savings of at least 4% could be achieved by better matching appropriate harvesting systems and methods to the terrain using the OHARP methodology. When operational decisions including resource constraints were optimized, up to a 30% cost reduction could be achieved in terms of average harvesting and transportation cost.