Performance Evaluation of the Least-Cost Lumber Grade-Mix Solver
Keywords:
Rough mill, least-cost lumber grade-mix, performance evaluation, response surfaceAbstract
The least-cost lumber grade-mix problem is of high economic interest to industry. Finding the minimum grade or grade-mix for a given cutting bill can save a company large sums without incurring additional costs. To academia, the least-cost lumber grade-mix problem is of significance due to its complexity and the difficulty to obtain near optimal or optimal results.
An earlier study used a new statistical approach to solving the least-cost lumber grade-mix problem. A five-factor mixture design was used to create a lumber grade-mix response surface, on which the minimum cost point is determined. However, this model's merit has never been assessed so far. This study compares the performance of the new statistical model with solutions derived from the widely used OPTIGRAMI 2.0 least-cost lumber grade-mix program.
Results revealed that the statistical optimization approach provides better overall solutions for both raw material and total production cost scenarios. For 9 of 10 cutting bills tested, the statistical model found lower-cost solutions compared with those provided by OPTIGRAMI 2.0. The maximum savings found was $70/m3 of raw material (cost savings of 9%) and $105/m3 when processing costs were included (cost savings of 10%). Thus, the new model has the potential to help wood products manufacturers decrease their material and processing costs. This model has been incorporated into ROMI, the USDA Forest Service's rough-mill simulation tool.
References
Banks J (1998) Principles of simulation. Chapter 1 in Handbook of Simulation. J Banks, ed. Wiley. New York. Pp 3-30.nBuehlmann U, Zaech R (2001) Lumber grade cost evaluation. Final research report. North Carolina State University, Raleigh, NC. 12 pp.nBuehlmann U, Zuo X, Thomas RE (2004) Linear programming and optimizing lumber quality composition in secondary hardwood dimension mills. J Eng Manuf, Proc Institution of Mechanical Engineers Part B, Short Communications in Manufacture & Design. Issue B1, Volume 2004: 135-141.nBuehlmann U, Wiedenbeck JK, Noble R, Kline DE (2008). Creating a standardized and simplified cutting bill using group technology. Wood Fiber Sci 40(1):42-54.nCarino HF, Foronda SU (1990) SELECT: A model for minimizing blank costs in hardwood furniture manufacturing. Forest Prod J 40(5):21-36.nCaron M (2003) Comparison de l' optimization selon le prix et selon le rendement matière dans les usines de débitage de composants de bois franc. MS Thesis. Université Laval, Quèbec, Canada. 66 pp.nEnglerth GH, Schumann DR (1969) Charts for calculating dimension yields from hard maple lumber. Res. Pap. FPL-118. USDA Forest Service, Forest Products Lab., Madison, WI. 12 pp.nGatchell CJ, Thomas RE, Walker ES (1998) 1998 data bank for kiln-dried red oak lumber. Gen. Tech. Rep. NE-245. USDA Forest Service, Northeastern Forest Experiment Station. Radnor, PA. 60 pp.nHanover SJ, Hafley WL, Mullin AG, Perrin RK (1973) Linear programming and sensitivity analysis for hardwood dimension production. Forest Prod J 23(11):47-50.nKline DE, Widoyoko A, Wiedenbeck JK, Araman PA (1998) Performance of color camera-based machine vision system in automated furniture rough mill systems. Forest Prod J 48(3):38-45.nLawson PS, Thomas RE, Walker ES (1996) OPTIGRAMI V2 user's guide. Gen. Tech. Rep. NE-222. USDA Forest Service, Northeastern Forest Experiment Station, Radnor, PA. 46 pp.nMartens DG (1986) Produce Yellow—Poplar furniture dimension at minimum cost using YELLPOP. N.E.-592. Report Pap. USDA Forest Service, Northeastern Forest Experiment Station. Radnor, PA. 15 pp.nMartens DG (1986a) Reduce dimension costs by using Walnut. N.E.-586. Res. Pap. USDA Forest Service, Northeastern Forest Experiment Station. Radnor, PA. 10 pp.nMartens DG, Nevel RL (1985) OPTIMGRAMI: optimum lumber grade-mix program for hardwood dimension parts. Res. Pap. NE-563. USDA Northeastern Forest Experiment Station. Radnor, PA. 6 pp.nMyer RH, Montgomery DC (2002) Response surface methodology: process and product optimization using designed experiments. Second Edition: Wiley series in probability and statistics. Wiley, New York, 798 pp.nNHLA (1998) Rules for the measurement and inspection of hardwood and cypress. National Hardwood Lumber Association. Memphis, TN. 136 pp.nOtt RL (1993) An introduction to statistical methods and data analysis. Fourth edition. Wadsworth Publishing Company. Belmont, CA. 1183 pp.nRawling JO, Pantula SG, Dickey DA (1998) Applied regression analysis. Second Edition. Springer-Verlag New York Inc., NY. 657 pp.nSAS (2002) SAS system for windows 8.2. SAS Institute, Inc. Cary, NC.nSteele P, Harding OV, Boden C, Brunner CC (2001) RIPXcut user's manual. FWRC Research Bulletin # FP 2006. Forest and Wildlife Research Center. Mississippi State University. Mississippi State, MS. 34 pp.nThomas RE (1996) Prioritizing parts from cutting bills when gang-ripping first. Forest Prod J 46(10):61-66.nThomas RE (1999) ROMI-RIP version 2.0: a new analysis tool for rip-first rough mill operations. Forest Prod J 49(5): 35-40.nTimson FG, Martens DG (1990) OPTIMGRAMI for PC's: User's manual (Version 1.0). Gen. Tech. Rep. NE-143. USDA Forest Service, Northeastern Forest Experiment Station. Radnor, PA. 64 pp.nWeekly Hardwood Review (2002) Appalachian and Northern Pricing. 4 January 2002. Vol. 18, Issue 17. Charlotte, NC. 32 pp.nWeiss J, Thomas RE 2005. ROMI-3: Rough-Mill Simulator Version 3.0: User's Guide. Gen. Tech. Rep. NE-328. USDA Forest Service, Northeastern Research Station. Newtown Square, PA. 75 pp.nWengert EM, Lamb FM 1994. A handbook for improving quality and efficiency in rough mill operations: practical guidelines, examples, and ideas. R.C. Byrd Hardwood Technology Center, Princeton. WV. 107 pp.nZuo X, Buehlmann U, Thomas RE (2004) Investigating the linearity assumption between lumber grade-mix and yield using design of experiment (DOE). Wood Fiber Sci 36(4):536-547.n
Downloads
Published
Issue
Section
License
The copyright of an article published in Wood and Fiber Science is transferred to the Society of Wood Science and Technology (for U. S. Government employees: to the extent transferable), effective if and when the article is accepted for publication. This transfer grants the Society of Wood Science and Technology permission to republish all or any part of the article in any form, e.g., reprints for sale, microfiche, proceedings, etc. However, the authors reserve the following as set forth in the Copyright Law:
1. All proprietary rights other than copyright, such as patent rights.
2. The right to grant or refuse permission to third parties to republish all or part of the article or translations thereof. In the case of whole articles, such third parties must obtain Society of Wood Science and Technology written permission as well. However, the Society may grant rights with respect to Journal issues as a whole.
3. The right to use all or part of this article in future works of their own, such as lectures, press releases, reviews, text books, or reprint books.
