Risk management in particleboard production system design : a simulation study of the stochastic optimization problem Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/zs25xb846

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  • Effects of stochastic variations of parameters in the planning and design of a particleboard production system are studied. The solution obtained from a linear deterministic optimization model is compared against both the solution derived from the traditional stochastic programming techniques and the distribution of optimal objective function values obtained from models whose parameters were Monte Carlo generated. Resource Planning and Management System was used to produce a network representation of a particleboard operation that is planned in the Yucatan region of Mexico. The plant will use the cactus plants that naturally grow in the area and produce particleboards for housing construction. The stochastic elements are introduced in the process variability, risks associated with the quality and the quantity of supply of materials and labor, and the market demand where the products must compete against imported particleboards. The network model included the risk elements as triangular distributions using the three parameters (L = minimum, M = most likely, U = maximum) similar to the beta distribution assumption commonly made in Program Evaluation and Review Technique. Three major goals are pursued in this thesis: (1) practical contribution: to ascertain the viability of constructing and operating a particleboard production facility in Mexico; (2) theoretical contribution: to determine the effects of risk upon optimal solutions; and (3) industrial engineering contribution: to develop a practical approach to planning, scheduling and control of production systems under stochastic considerations. Four hypotheses were proposed for testing: (1) though only a few empirical applications of stochastic programming are now available, a practical industrial model can be constructed by modifying a linear programming model to incorporate the stochastic features; (2) Monte Carlo simulation provides more objective and meaningful data to management than the use of expected values in the linear programming techniques; (3) variations in objective function are proportional to the variations in the parameters; and (4) the problem of estimating parameters in the modeling phase, in terms of suitable sample functions, are nontrivial and practically insurmountable. The first goal and the first two hypotheses were achieved through RPMS and Monte Carlo simulation by graphical representation of decision processes involved. The second and third goals and the third hypothesis were achieved by interpreting the results in such a way as to be useful and meaningful to management. Finally, by the use of management experience, machine tolerances and direct estimate methods, the fourth hypothesis was rejected. Two major models were constructed and experimented by utilizing RPM1 (linear programming) and RPM2 (simulation) packages developed by Steve Shu-Kang Chou. The first model, containing 35 activity processes and 41 resource constraints, was used, first to validate selected activity levels observed from LP against historical records, and second to obtain the expected effects of risk in three phases of management consideration: (1) variations only in costs and prices: two stage programming simulation); (2) consideration of process variability: chance-constrained programming simulation; and (3) combination of the above two: stochastic programming simulation. The second model was used to prove and remedy the drop in production output and profit due to stochasticity. This was made possible by bounding the processes with the solutions depicted from the deterministic linear programming run. This model contained 35 activity processes and 71 resource constraints. Following is a summary of the conclusions drawn from this study: (1) computer simulation facilities for stochastic programming are now available and can be used at a relatively low cost ($450.00 for this entire project); (2) the manner in which the models and techniques were utilized would constitute a viable tool for planning production systems; (3) no consideration of risk in production planning could result in underachievement of profit of, say, 28.28%, and of production yields of, say, 7.18% as in the case of stochastic programming simulation. However, the resulting payback resulted to be 7.83 years that, in comparison with the deterministic LP, is 2.21 years higher; and (4) the resulting drop in profit was found practically solved by increasing the resource availability by the same percentage of underachievement of profit.
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