Skidding Machines Allocation (SMA) Using Fuzzy Set Theory

Efficient allocation of resources is an essential principle in forest management. An important case in resource allocation is when the available resources are not sufficient to service all requests. One of the key elements in forest management is to minimize the total costs of the unallocated requests. With respect to high capital cost of forest operation machinery, it is necessary to reduce expenses of one cubic meter of wood extraction by appropriate Skidding Machines Allocation (SMA). Fuzzy set theory as a soft methodology and practical decision support system was used to handle uncertain variables and vague range of logs volume and physiographic conditions to develop models. The aim of this research is to present a decision support method to determine the economical activity zone of forest operation machines so that this allocation would result in the highest net profit for forest managers. To achieve this goal, all skid routes in the study area were divided into work units with 75 m width and 200 m length whereupon 379 units were formed collectively.Within each unit, the related mathematical productivity models were applied to estimate one cycle time and cost of machines. The effective factors of these models included Skidding Distance (SD), Volume of Logs per Cycle (VLC), and Number of Logs per Cycle (NLC). Three separate fuzzy inference models were developed to predict the skidding cost of each machine in the units, and then proper machines were allocated. 70% of data was used as training and the rest was feed to the models for validation and test process through the generation of fuzzy models. Membership functions and fuzzy rule bases were created with the help of scientific knowledge, experts’ viewpoints and existing machine productivity models. The results showed that the application of the presented approach helps forest managers to recognize the desirable conditions for skidding machines to reduce the total costs of skidding. In addition, SMA fuzzy rule-based models reflect how to integrate expert knowledge with engineering system design. To present an illustrative example, the models were applied to allocate three commonly used Skidders, i.e. Timberjack 450c, HSM 904, and Zetor, in a mountainous forest, whose inventory data were known and harvesting was planned for the next period. The results showed that the Zetor was the most economical option in »Very short« and »Short« (< 300 m) distances at all levels of NLC and VLC, while HSM 904 was the