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23 Cards in this Set
- Front
- Back
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refers both to the goal of finding the best values of the decision variables to a set of procedures that accomplish that goal.
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optimization
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the procedures that accomplish the goal of optimization
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algorithms
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What must be decided?
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decision variables
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Our decision criterion
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objective functions
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What restrictions limit our choice of decision variables?
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constraints
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Objective and all constraints are linear functions of the
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decision variables of linear optimization
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Techniques for solving linear models are
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more powerful
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The objective function or a constraint are nonlinear functions of the decision variables
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Nonlinear optimization or nonlinear programming
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algortith uses the ----- which is the slope or derivate
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gradient
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The highest peak (lowest valley) is the
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global optimum
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Objective and all constraints are linear functions of the decision variables.
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Linear optimization or linear programming
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What are properties of a linear function?
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additivity, proportionality, divisibility
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that the contribution from one decision gets added to the contributions of other decisions
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additive
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the contribution from any given decision grows in proportion to the value of the corresponding decision variable.
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proportional
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we mean that a fcational decision variable is meaninful
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divisible
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what are solutions to linear problesm
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feasible solutions, optimal solutions, the simplex method
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a solution that satisfies all constraints
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feasible solution
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must satisfy all constraints, and its objective function must equal the best value that can be achieved.
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optimal solution
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which method guarantees that it will find a global optimum, and in that sense makes it completely reliable.
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the simplex method
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Maximize objective (e.g., profit) subject to LT constraints on capacity
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allocation models
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Minimize objective (e.g., cost) subject to GT constraints on required coverage
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covering models
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Mix materials with different properties to find best blend
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blending models
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Describe patterns of flow in a connected system
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network models
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