Sfsu Management Discussion Answer all the questions based on book, each question have to summary about 10 sentences. I will email you the book. Must A!! We

Sfsu Management Discussion Answer all the questions based on book, each question have to summary about 10 sentences. I will email you the book. Must A!! Week 1
1.1. Describe 7 main steps in the scientific decision making. Illustrate the steps using a specific management problem (for a example,
choice of inputs for a PC with minimal costs).
Week 2
2.1. Define a linear programming model and its components. Discuss which 3 properties a planning problem needs to meet to be modeled
as a LP.
2.2. Define feasible set and its corner points. Explain how one can find the combinations of the decision variables corresponding to the
corner points. How one can use the corner points to find the optimal solution?
2.3. Define special situations: unbounded feasible set, infeasibility, alternate solutions, redundant constraints. Discuss what do these
situations imply for the manager’s planning problem. When do you need and how do you escape this situations?
Week 3
3.1 Provide an example of a manufacturing or employee staffing application discussed in the class (based on chapter 3). State the business
problem; name the decision variables; define the objective function; explain sources of the linear constraints in this application.
3.2 Provide an example of a marketing or transportation application discussed in the class (based on chapter 3). State the business
problem; name the decision variables; define the objective function; explain sources of the linear constraints in this application.
3.3 Provide an example of a finance or blending application discussed in the class (based on chapter 3). State the business problem; name
the decision variables; define the objective function; explain sources of the linear constraints in this application.
3.4 Provide an example of a multi-period application discussed in the class (based on chapter 3). State the business problem; name the
decision variables; define the objective function; explain sources of the linear constraints in this application.

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