Transcription of Chapter Four: Linear Programming: Modeling Examples
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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 (maximization) mix (maximization) mi
x4 = 1.464 cups of oat bran x5 = .065 eggs x8 = 1.033 cups of milk x10 = 4 slices of wheat toast Z = $0.828 Further limiting the servings of the four hot and cold cereals to two cups, x1 + x2 + x3 + x4 ≤ 2, results in the following solution: x3 = 2 cups of oatmeal x6 = .750 slices of bacon x8 = 2 cups of milk x9 = .115 cups of orange juice ...
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