A combination of many grades of veneer may be jointly produced by peeling any one type of logs. This means that the plywood manufacturer can not really know the profit margins of the panels he produces. And for most of the manufacturers, the cost of the logs constitutes about 65% of the total cost of producing the plywood panels. Consequently, the manufacturer finds it very difficult to select his sales strategy and to price his panels. The plywood manufacturer also has opportunities to minimize his log cost and processing cost by selecting the right combination of logs to peel and using the right constructions in laying up the panels. Linear programming techniques are used in this study to provide an approach to the above mentioned problems for a selected plywood manufacturer. Through this, it is hoped as well to provide an examination of the way to use linear programming techniques and an evaluation of their usefulness as management tools in plywood manufacturing. A survey of the reported experience of some plywood manufacturers indicated that the use of L. P. had been instrumental in saving some hundreds of thousands of dollars per annum for some manufacturers. An L. P. model was constructed for the largest of the four mills of the Case Company, using the operating situations predicted for the year 1967. Such problems (and their solutions) as were encountered in defining, identifying and measuring the variable process costs and the need for making simplifying assumptions were examined. The L. P. model seeks to optimize the choice of panel output, the choice of log input and the choice of panel constructions simultaneously because these three decisions are interrelated and somewhat interdependent. The L. P. analysis suggests that about 30% of the dollar sales of the Case Mill in 1967 were made in unprofitable (thin) panels. Even after allowing for possible over estimation in measuring the variable processing cost, this may call for a thorough re-examination of the sales strategy and the panel pricing system. To produce the panel output selected for 1967, the best log combination apparently includes the use of a much higher proportion of Fir Peeler #2 and Sawlog #3, Interior Fir and Hemlock-Balsam Mix than was used by the Company in its mill. Also, the model suggests proportions of Fir Peeler #1, #4 and S. F.P. might be much lower than what the Company tended to use. These tentative findings may have significant implications for choosing log acquisition policies. The model suggests the choice in panel construction may be to peel Peeler logs for .104" high quality veneer, Interior Fir for .130" veneer and other low quality logs for .171" core veneer. It also suggests down-grading some veneer and using some subsidiary panel constructions so as to utilize fully the total supply of veneer from the logs peeled. The usefulness of the L.P. analysis is fully realized by making a comprehensive post-optimal analysis of the sensitivity of the optimality of the solution to various changes in the log supply and/or panel demand and/or processing cost situations. This analysis may enable the company to determine which of the operating factors seem crucial in determining the profitability of the panels and what may constitute the best log combination to use. From this, the manufacturer could possibly know when and how to adapt his program of operation in response to any future changes in (or any revision in the forecast of) the operating situation. This analysis is also helpful in gauging the importance of the assumptions made when constructing the model. No comprehensive sensitivity analysis was carried out in this study. However, recommendations regarding appropriate post-optimal analyses are presented. Lastly, the study concludes by presenting an L. P. model of possible use to analyze the four mills of the Company together, recognizing the possibilities of specialization and cooperation among the mills. / Business, Sauder School of / Graduate

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This essay investigates the concept of linear programming in general and linear stochastic programming in particular. Linear stochastic programming is described as the model where the parameters of the linear programming admit random variability. The first three chapters present through a set-geometric approach the foundations of linear programming. Chapter one describes the evolution of the concepts which resulted in the adoption of the model. Chapter two describes the constructs in n-dimensional euclidian space which constitute the mathematical basis of linear programs, and chapter three defines the linear programming model and develops the computational basis of the simplex algorithm. The second three chapters analyze the effect of the introduction of risk into the linear programming model. The different approaches of estimating and measuring risk are studied and the difficulties arising in formulating the stochastic problem and deriving the equivalent deterministic problems are treated from the theoretical and practical point of view. Multiple examples are given throughout the essay for clarification of the salient points.

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Thesis (D.A.)--Illinois State University, 1989. / Title from title page screen, viewed October 14, 2005. Dissertation Committee: Michael J. Plantholt (chair), John A. Dossey, Patricia H. Klass, James T. Parr, Lawrence E. Spence. Includes bibliographical references (leaves 87-89) and abstract. Also available in print.