Chapter 1 b625aswc01.pptx - Introduction to Decision...

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Introduction to Decision Science
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Overview Everyday specific businesses decide how much to sell of various types of products they make, how much labor and capital to employ, how many units to ship by train and how many by plane. There are other decisions as well. In this course we will focus mainly on those decisions that have a quantitative solution. We will spend time applying mathematical and statistical techniques to aid us in finding a solution. Sometimes Microsoft Excel will be utilized in seeking solutions to problems that folks in a business setting face on a day to day basis.
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Overview Think about your experiences with your career, club, perhaps a church you have belonged to, or some other group. Sometimes after a while with the group you notice a problem or a situation that could be improved. You may have articulated your ideas to others and may have tried to make the improvement. In decision science, just like in your own life, there is a logical way to solve problems. In the decision science area the logic is to follow the scientific method as outlined in figure 1.1 in the book. Note that decision making is part of a larger process of problem solving.
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Models Often we will build a model of the decision process. There are some items in that process for which we have no control over and must merely take as a given piece of information in our work. These items are sometimes called uncontrollable inputs or parameters . Other items will be under our control and we have to determine appropriate usage for these items. These are called controllable inputs or decision variables . In our model we may have an objective function and some constraints . The objective function is a mathematical statement of what we want to achieve and is typically a combination of uncontrollable inputs and controllable inputs. Constraints place limits on the values that can be taken by the controllable inputs.
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Types of Models A deterministic model would be used in a situation where the uncontrollable inputs are known and cannot vary. A stochastic or probabilistic model would be used when any uncontrollable inputs are uncertain and subject to variability. At this stage of our work I think it best to have these definitions and look later to actual examples.
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Let’s consider a relatively simple problem.
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