Metrics for Distribution Similarity Applied to the Bucking to Demand Procedure
Authors
Jukka Malinen
University of Joensuu
Teijo Palander
University of Joensuu
Abstract
In the computerized bucking to demand procedure bucking is done according to a given price list and demand matrix, which defines the demands for different log length-diameter class proportions. To achieve as good a log length-diameter distribution as possible, the computer compares demand and actual output to appropriately direct bucking. A comparison has been made with a variable called distribution level, which, however, is unable to distinguish between error that is close to the optimum log length-diameter class proportion and error that is further away. In addition, the distribution level does not distinguish between log length-diameter classes, even though error in one class can be far more undesirable than in another.
In this study, bucking to demand using the distribution level was compared to bucking to value and bucking to demand using the penalty segmented distribution level, squared distribution level, chi-square formula and flexible penalty segmented distribution level. The bucking outcome employing these various techniques was achieved by using a bucking simulator and artificially generated stand and stem data.
The results show that the best bucking outcomes were produced by methods with a squared error term, i.e. the squared distribution level, chi-squared formula and flexible penalty segmented distribution level. In addition, it was possible to direct error toward preferred log length-diameter classes without substantial loss in overall goodness of fit.
