The marking for bucking under uncertain in automatic harvesting of forests
University of Tampere, P.O. Box 607, SF-33101 Tampere, FinlandInternational Journal of Production Economics (Impact Factor: 2.75). 02/1996; 46-47(1):373-385. DOI: 10.1016/0925-5273(95)00085-2
The marking for bucking is the problem of converting a single tree stem into logs in such a way that the total stem value according to a given price list for logs is maximized. Proper marking for bucking is of crucial importance in harvesting of forests. The annual production of saw timber in Scandinavia is tens of millions m3. Since, furthermore, sawn timber is by far the most valuable forest product in Scandinavia, any improvement in the marking for bucking procedure will yield a large profit. To solve the marking for bucking problem optimally one has to know the whole tree stem. However, it is not economically feasible to run the whole stem through the measuring device before cutting. Therefore, it is a normal situation under computer-based marking for bucking that the first cutting decisions are made before the whole stem is known.In this paper the forest harvesting process will be considered under a general growth curve model useful especially for repeated-measures data. The main objective is to predict the unknown portion yn(2) of the current stem, which will be estimated from the stem data y1,y2,…,yn − 1on the previously processed n − 1 stems and from the known diameter values yn(1)on the current stem. Then a predictor of yn(2), say ŷn(2), jointly with the known part yn(1) is used in marking for bucking. It turns out that this technique can improve radically the efficiency of harvesting, so that our results provide important knowledge for developing automatic bucking systems of modern harvesters.
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ABSTRACT: This article focuses on the problem of predicting future measurements on a statistical unit given past measurements on the same and other similar units. We introduce a conditional predictor that uses the information contained in previous measurements. The prediction technique is based on the iterative EM algorithm, but a noniterative variant is also provided. We use the sample-reuse methodology to select an appropriate predictor. The technique is illustrated in three engineering applications. The first considers prediction in the context of marketing for bucking in automatic forest harvesters. In fatigue-crack-growth data, the interest is in predicting the future crack-growth development of the test unit, and the third application concerns evaluation of pulp from the point of view of its papermaking potential.Technometrics 02/1996; 38(1):25-36. DOI:10.1080/00401706.1996.10484413 · 1.81 Impact Factor
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