65) How are linear programming models vulnerable when used in complex situation? Answer: These models have the ability to be vulnerable when used in very complex situations for a number of reasons. One reason focuses on the possibility that not all parameters can be known or understood. another concern is that the standard characteristics of a linear programming calculation may not hold in more dynamic, real-world environments. Additionally, in more complex environments all actors may not be wholly rational and economic issues Diff: 2 Page Ref: 338 66) Provide some examples where a sensitivity analysis may be used Answer: Sensitivity analyses are used for Revising models to eliminate too-large sensitivities Adding details about sensitive variables or scenarios Obtaining better estimates of sensitive external variables Altering a real-world system to reduce actual sensitivities Accepting and using the sensitive(and hence vulnerable) real world, leading to the continuous and close monitoring of actual results Diff: 3 Page Ref: 347 67) List and describe the most common approaches for treating uncertainty Answer: There are two common approaches to dealing with uncertainty The first is the optimistic approach and the second is the pessimistic approach. The optimistic approach assumes that the outcomes for all alternatives will be the best possible and then the best of each of those may be selected Under the pessimistic approach the worst possible outcome is assumed for each alternative and then the best of the worst are selected Diff: 2 Page Ref: 350-351 68)Why is the Monte Carlo simulation popular for solving business problems? Answer: The Monte Carlo simulation is a probabilistic simulation. It is designed around a model of the decision problem, but the problem does not consider the uncertainty of any of the variables. This allows for a huge number of simulations to be run with random changes within each of the variables. In this way, the model may be solved hundreds or thousands of times before it is completed. These results can then be analyzed for either the dependent or erformance variables using statistical distributions. This demonstrates a number of possible solutions, as well as providing information about the manner in which variables will respond under different levels of uncertainty Diff: 3 Page Ref: 357 Copyright C 2018 Pearson Education, Inc10 Copyright © 2018 Pearson Education, Inc. 65) How are linear programming models vulnerable when used in complex situation? Answer: These models have the ability to be vulnerable when used in very complex situations for a number of reasons. One reason focuses on the possibility that not all parameters can be known or understood. Another concern is that the standard characteristics of a linear programming calculation may not hold in more dynamic, real-world environments. Additionally, in more complex environments all actors may not be wholly rational and economic issues. Diff: 2 Page Ref: 338 66) Provide some examples where a sensitivity analysis may be used. Answer: Sensitivity analyses are used for: • Revising models to eliminate too-large sensitivities • Adding details about sensitive variables or scenarios • Obtaining better estimates of sensitive external variables • Altering a real-world system to reduce actual sensitivities • Accepting and using the sensitive (and hence vulnerable) real world, leading to the continuous and close monitoring of actual results Diff: 3 Page Ref: 347 67) List and describe the most common approaches for treating uncertainty. Answer: There are two common approaches to dealing with uncertainty. The first is the optimistic approach and the second is the pessimistic approach. The optimistic approach assumes that the outcomes for all alternatives will be the best possible and then the best of each of those may be selected. Under the pessimistic approach the worst possible outcome is assumed for each alternative and then the best of the worst are selected. Diff: 2 Page Ref: 350-351 68) Why is the Monte Carlo simulation popular for solving business problems? Answer: The Monte Carlo simulation is a probabilistic simulation. It is designed around a model of the decision problem, but the problem does not consider the uncertainty of any of the variables. This allows for a huge number of simulations to be run with random changes within each of the variables. In this way, the model may be solved hundreds or thousands of times before it is completed. These results can then be analyzed for either the dependent or performance variables using statistical distributions. This demonstrates a number of possible solutions, as well as providing information about the manner in which variables will respond under different levels of uncertainty. Diff: 3 Page Ref: 357