Production planning is a fundamental role in any manufacturing operation. The problem is to decide what type of, and how much, product should be produced in future time periods. The decisions should be based on many factors, including period machine capacity, profit margins, holding costs, etc. The primary factor, however, is the estimate of demand for a manufacturer's products in upcoming periods. Traditional production planning algorithms assume future demand forecasts will describe the actual demand realizations perfectly and use these forecasts to solve the problem deterministically. Unfortunately, this assumption is usually invalid. Our focus is to solve the production planning problem by including in our models the randomness that exists in our estimates for future demands. We solve the problem with three methods. One method approaches the true optimal solution at a speed and implement ability cost. Two other methods are proposed which attempt to achieve results similar to the first method with the benefits of being faster and easier to implement.
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