Optimization models.

    Review Questions

1. Briefly discuss the evolution of the MS/OR field.

MS comes after and originated from OR, it began as a team of mathematicians, engineers and scientists for the study of some strategies and tactics related to the defense of their countries during wars. The scientist realized that many approaches and techniques applied during the war could be applied to some industry problems. After this discovery, some ideas and concepts began to appear in the industry. Since that the MS/OR has grown thanks to some developments in technology and some academic research.

1. How is model building related to management science?

Model building is a way to solve problems, by mental, scale or mathematical models. So the managers are able to solve problems and they take the task of problem solving trough the construction of models.

1. Differentiate between a descriptive and a normative model. Give examples of each.

Descriptive model is for predicting the behavior of the systems, but thay do not have the capacity to identify the best action course. An example of this could be that regression models indicates a relationship between a dependent and independent variable but it does not indicates the value for selecting the independent variables.

1. What basic set of elements exists in any normative model? Comment on each of these elements.

Decision variables and parameters: Decision variables are the unknown quantities that determine the solution of the model and parameters are the factors that describes the rate of consumption of a resource used in the production of a unit of the decision variable.

Constrains: They limit decision variables to the tolerable values.

Objective function: Is the effectiveness of a model as a function of the decision variables.

1. What are the subclassifications of normative and descriptive models?

Deterministic and stochastic

Linear and nonlinear

Static and dynamic

1. Differentiate between a deterministic model and a stochastic model.

Deterministic models have parameters that are known, whereas the stochastic models have unknown parameters.

1. Differentiate between a linear and nonlinear model.

Linear model has a functional relationship are such that the dependent variable is proportional to the sum of independent variables and the nonlinear models apply non proportional equations.

1. Differentiate between a static and a dynamic model.

Dynamic model refers to runtime model of the system and that keeps changing with reference to time whereas static model is the system not during runtime and are at equilibrium of in a steady state.

1. When are simulation models employed in management science?

Simulation models employed when we do not required closed mathematical function for relate variables, the simulation is more than a process ore modeling or experimentation process, its used for describe and analyze a problem.

1. What solution processes exist in the MS/OR field? Briefly explain or comment on each.

Heuristics: Are important in numerical methods, they give the starting point and deliver one or several solutions for a model.

Algorithms: Set of procedures followed in order that deliver the best solution.

Simulations: Emulates problems behavior under a given set of condition.

1. Is it necessary to develop an algorithm for every problem that is addressed in an MS/OR study?

Is necessary when we have an open problem whit known or unknown solution in which case have an algorithm, while an algorithm is developed for a model, it is applicable only in solving problem that has the specific characteristics of the model.

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