Exam 14: Optimization Models
Exam 1: Introduction to Data Analysis and Decision Making30 Questions
Exam 2: Describing the Distribution of a Single Variable66 Questions
Exam 3: Finding Relationships Among Variables46 Questions
Exam 4: Probability and Probability Distributions56 Questions
Exam 5: Normal, Binomial, Poisson, and Exponential Distributions56 Questions
Exam 6: Decision Making Under Uncertainty54 Questions
Exam 7: Sampling and Sampling Distributions77 Questions
Exam 8: Confidence Interval Estimation53 Questions
Exam 9: Hypothesis Testing63 Questions
Exam 10: Regression Analysis: Estimating Relationships79 Questions
Exam 11: Regression Analysis: Statistical Inference69 Questions
Exam 12: Time Series Analysis and Forecasting75 Questions
Exam 13: Introduction to Optimization Modeling70 Questions
Exam 14: Optimization Models63 Questions
Exam 15: Introduction to Simulation Modeling64 Questions
Exam 16: Simulation Models56 Questions
Exam 17: Data Mining18 Questions
Exam 18: Importing Data Into Excel18 Questions
Exam 19: Analysis of Variance and Experimental Design19 Questions
Exam 20: Statistical Process Control19 Questions
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Multiple optimal solutions are quite common in linear programming models.
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In blending problems,if a quality constraint involves a quotient,then the problem will be nonlinear.
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In aggregate planning models,we can model backlogging of demand by allowing a month's inventory to be negative.
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Transshipment points are locations where goods neither originate nor end up,but goods are allowed to enter such points to be shipped out to their eventual destinations.
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When we solve a linear programming problem with Solver,we cannot guarantee that the solution obtained is an optimal solution.
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In a minimum cost network flow model,the flow balance constraint for each supply node takes the form
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A minimum cost network flow model (MCNFM)has the following advantage relative to the special case of a simple transportation model:
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Which of the following does not represent a broad class of applications of linear programming models?
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In a set-covering model,each member of a given set (set 1)must be "covered" by an acceptable member of another set (set 2).The objective of such problems is to minimize the number of elements in set 2 that are needed to cover all the elements in set 1.
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Any integer programming problem involving 0-1 variables with only one constraint is called a knapsack problem.
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The flows in a general minimum cost network flow model (MCNFM)do all necessarily have to be from "left to right";that is,from supply points to demand points.
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In an optimized network flow model (MCNFM),all the available capacity will be used.
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Transportation and transshipment problems are both considered special cases of a class of linear programming problems called
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If all the supplies and demands for a transportation model are integers,then the optimal Solver solution may or may not have integer-valued shipments.
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Any integer program involving 0 - 1 variables with constraint(s)is called a knapsack problem.
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In a network representation of a transportation problem,the nodes generally represent:
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The binary variables in the fixed cost models correspond to:
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The constraints in a blending problem can be specified in a valid way and still lead to which of the following problems?
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In a typical minimum cost network flow model,the nodes indicate
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