TAXONOMY OF DECISION MODELING A. MODELS: 1. DETERMINISTIC vs PROBABILISTIC 2. LINEAR vs NONLINEAR 3. UNCONSTRAINED vs CONSTRAINED 4. CONTINUOUS vs DISCRETE 5. SINGLE OBJECTIVE vs MULTIPLE OBJECTIVES B. ANALYSIS: 1. SOLUTION vs SIMULATION SOLULTION METHODS: a. TOTAL ENUMERATION b. ALGEBRAIC c. CALCULUS d. ALGORITHM e. HEURISTIC C. RESULTS: 1. OPTIMIZATION vs DESCRIPTIVE 2. GENERAL CASE vs SPECIFIC CASE
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TAXONOMY OF DECISION MODELING (With Descriptions) A. MODELS: 1. DETERMINISTIC vs PROBABILISTIC Sometimes called “certainty” vs “uncertainty.” In deterministic models, parameter values are assumed to be known values, i.e., constants. In probabilistic models, parameter values are not known with certainty and are therefore represented by probability distributions. 2. LINEAR vs NONLINEAR Linear models are characterized by linear functional relationships, i.e., y = 2x “Linear” implies both “proportionality” and “Additivity.” Nonlinear models are characterized by nonlinear functional relationships, i.e., y = 3x2 or y = 5/x, or y = 7x1x2 3. UNCONSTRAINED vs CONSTRAINED Unconstrained models have no restrictions on the values of the solution variables. Constrained models have certain restrictions on the values of the solution variables, i.e., x ≤ 40, or x1 + 2x2 ≤ 300. 4. CONTINUOUS vs DISCRETE Sometimes referred to as “real” variables vs. “integer” variables. Continuous models contain continuous functional relationships and allow fractional solutions. Discrete models may require that the solution values be integer. 5. SINGLE vs MULTIPLE OBJECTIVES Many business decisions are made based on a single criteria such as profit or cost. However, in other situations, we may need to consider several criteria at once such as profit, cost, labor relations, pollution control, company image, etc. B. ANALYSIS: 1. SOLUTION vs SIMULATION Solution typically means “mathematical” manipulation of the model. A solution method is usually a mathematical manipulation that yields...
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