Chapter Four: Linear Programming: Modeling Examples

40 PROBLEM SUMMARY1. Product mix example2. Diet example3. Investment example4. Marketing example5. Transportation example6. Blend mix (maximization) analysis (4 7) (minimization) mix (minimization) mix (maximization) mix (maximization) mix (maximization) mix (minimization) (maximization) mix (maximization) mix (minimization) distribution (maximization) allocation (maximization) (minimization), sensitivity (maximization) (minimization) scheduling (minimization) busing (minimization) analysis (4 24) mixture (minimization) scheduling (maximization) mixture (maximization) poly mix (maximization) mix (maximization) mix (minimization), sensitivity analysisChapter Four: Linear Programming: Modeling (maximization) borrowing (minimization) production scheduling(minimization) (maximization), sensitivity (minimization), sensitivity (minimization) (minimization) line scheduling (maximization) flow (minimization) admissions (maximization) (maximization) loss (minimization) investment (maximization) sales and inventory (maximization) production and inventory(minimization) assignment (maximization) envelopment envelopment flow (maximization) workforce planning (minimization) solution (4 43) scheduling (maximization), storage (maximization) scheduling (maximiz)

x9 = .115 cups of orange juice x10 = 4 slices of wheat toast Z = $0.925 3. It would have no effect; the entire $70,000 would be invested anyway. Since the upper limit of the sensitivity range for the investment amount is “unlimited,” an increase of $10,000 will not affect the shadow price, which is $0.074. Thus, the total increase $740).

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