1. 1 1 Slide Chapter 1 & Lecture 1

2. 2 2 Slide Body of Knowledge n Management science Is an approach to decision making based on the scientific method Is an approach to decision making based on the scientific method Makes extensive use of quantitative analysis Makes extensive use of quantitative analysis n The body of knowledge involving quantitative approaches to decision making is also referred to as Operations research Operations research Decision science Decision science n It had its early roots in World War II and is flourishing in business and industry with the aid of computers

3. 3 3 Slide n 7 Steps of Problem Solving (First 5 steps are the process of decision making) Define the problem. Define the problem. Identify the set of alternative solutions. Identify the set of alternative solutions. Determine the criteria for evaluating alternatives. Determine the criteria for evaluating alternatives. Evaluate the alternatives. Evaluate the alternatives. Choose an alternative (make a decision). Choose an alternative (make a decision). --------------------------------------------------------------------- --------------------------------------------------------------------- Implement the chosen alternative. Implement the chosen alternative. Evaluate the results. Evaluate the results. Problem Solving and Decision Making

4. 4 4 Slide Quantitative Analysis n Quantitative Analysis Process Model Development Model Development Data Preparation Data Preparation Model Solution Model Solution Report Generation Report Generation

5. 5 5 Slide Model Development n Models are representations of real objects or situations n Three forms of models are: Iconic models - physical replicas (scalar representations) of real objects Iconic models - physical replicas (scalar representations) of real objects Analog models - physical in form, but do not physically resemble the object being modeled Analog models - physical in form, but do not physically resemble the object being modeled Mathematical models - represent real world problems through a system of mathematical formulas and expressions based on key assumptions, estimates, or statistical analyses Mathematical models - represent real world problems through a system of mathematical formulas and expressions based on key assumptions, estimates, or statistical analyses

6. 6 6 Slide Advantages of Models n Generally, experimenting with models (compared to experimenting with the real situation): requires less time requires less time is less expensive is less expensive involves less risk involves less risk

7. 7 7 Slide Mathematical Models n Relate decision variables (controllable inputs) with fixed or variable parameters (uncontrollable inputs). n Frequently seek to maximize or minimize some objective function subject to constraints. n Are said to be stochastic if any of the uncontrollable inputs is subject to variation, otherwise are said to be deterministic. n Generally, stochastic models are more difficult to analyze. n The values of the decision variables that provide the mathematically-best output are referred to as the optimal solution for the model.

8. 8 8 Slide Transforming Model Inputs into Output Uncontrollable Inputs (Environmental Factors) Uncontrollable Inputs (Environmental Factors) ControllableInputs(DecisionVariables)ControllableInputs(DecisionVariables) Output(ProjectedResults)Output(ProjectedResults) MathematicalModelMathematicalModel

9. 9 9 Slide Example: Iron Works, Inc. Iron Works, Inc. (IWI) manufactures two products made from steel and just received this month's allocation of b pounds of steel. It takes a 1 pounds of steel to make a unit of product 1 and it takes a 2 pounds of steel to make a unit of product 2. Let x 1 and x 2 denote this month's production level of product 1 and product 2, respectively. Denote by p 1 and p 2 the unit profits for products 1 and 2, respectively. The manufacturer has a contract calling for at least m units of product 1 this month. The firm's facilities are such that at most u units of product 2 may be produced monthly.

10. 10 Slide Example: Iron Works, Inc. n Mathematical Model The total monthly profit = The total monthly profit = (profit per unit of product 1) x (monthly production of product 1) x (monthly production of product 1) + (profit per unit of product 2) + (profit per unit of product 2) x (monthly production of product 2) x (monthly production of product 2) = p 1 x 1 + p 2 x 2 = p 1 x 1 + p 2 x 2 We want to maximize total monthly profit: Max p 1 x 1 + p 2 x 2

11. 11 Slide Example: Iron Works, Inc. n Mathematical Model (continued) The total amount of steel used during monthly production equals: The total amount of steel used during monthly production equals: (steel required per unit of product 1) (steel required per unit of product 1) x (monthly production of product 1) x (monthly production of product 1) + (steel required per unit of product 2) + (steel required per unit of product 2) x (monthly production of product 2) x (monthly production of product 2) = a 1 x 1 + a 2 x 2 = a 1 x 1 + a 2 x 2 This quantity must be less than or equal to the allocated b pounds of steel: This quantity must be less than or equal to the allocated b pounds of steel: a 1 x 1 + a 2 x 2 < b a 1 x 1 + a 2 x 2 < b

12. 12 Slide Example: Iron Works, Inc. n Mathematical Model (continued) The monthly production level of product 1 must be greater than or equal to m : The monthly production level of product 1 must be greater than or equal to m : x 1 > m x 1 > m The monthly production level of product 2 must be less than or equal to u : The monthly production level of product 2 must be less than or equal to u : x 2 < u x 2 < u However, the production level for product 2 cannot be negative: However, the production level for product 2 cannot be negative: x 2 > 0 x 2 > 0

13. 13 Slide Example: Iron Works, Inc. n Mathematical Model Summary Max p 1 x 1 + p 2 x 2 Max p 1 x 1 + p 2 x 2 s.t. a 1 x 1 + a 2 x 2 < b s.t. a 1 x 1 + a 2 x 2 < b x 1 > m x 1 > m x 2 < u x 2 < u x 2 > 0 x 2 > 0 Objectiv e Function Subject to Constraint s

14. 14 Slide Example: Iron Works, Inc. n Question: Suppose b = 2000, a 1 = 2, a 2 = 3, m = 60, u = 720, p 1 = 100, p 2 = 200. Rewrite the model with these specific values for the uncontrollable inputs.

15. 15 Slide Example: Iron Works, Inc. n Answer: Substituting, the model is: Max 100 x 1 + 200 x 2 Max 100 x 1 + 200 x 2 s.t. 2 x 1 + 3 x 2 < 2000 s.t. 2 x 1 + 3 x 2 < 2000 x 1 > 60 x 1 > 60 x 2 < 720 x 2 < 720 x 2 > 0 x 2 > 0

16. 16 Slide Example: Iron Works, Inc. n Question: The optimal solution to the current model is x 1 = 60 and x 2 = 626 2/3. If the product were engines, explain why this is not a true optimal solution for the "real-life" problem. n Answer: One cannot produce and sell 2/3 of an engine. Thus the problem is further restricted by the fact that both x 1 and x 2 must be integers. They could remain fractions if it is assumed these fractions are work in progress to be completed the next month.

17. 17 Slide Example: Iron Works, Inc. Uncontrollable Inputs $100 profit per unit Prod. 1 $200 profit per unit Prod. 2 2 lbs. steel per unit Prod. 1 3 lbs. Steel per unit Prod. 2 2000 lbs. steel allocated 60 units minimum Prod. 1 720 units maximum Prod. 2 0 units minimum Prod. 2 $100 profit per unit Prod. 1 $200 profit per unit Prod. 2 2 lbs. steel per unit Prod. 1 3 lbs. Steel per unit Prod. 2 2000 lbs. steel allocated 60 units minimum Prod. 1 720 units maximum Prod. 2 0 units minimum Prod. 2 60 units Prod. 1 60 units Prod. 1 626.67 units Prod. 2 60 units Prod. 1 60 units Prod. 1 626.67 units Prod. 2 Controllable Inputs Profit = $131,333.33 Steel Used = 2000 Profit = $131,333.33 Steel Used = 2000 OutputOutput Mathematical Model Max 100(60) + 200(626.67) s.t. 2(60) + 3(626.67) < 2000 60 > 60 60 > 60 626.67 < 720 626.67 < 720 626.67 > 0 626.67 > 0 Max 100(60) + 200(626.67) s.t. 2(60) + 3(626.67) < 2000 60 > 60 60 > 60 626.67 < 720 626.67 < 720 626.67 > 0 626.67 > 0

18. 18 Slide Management Science Techniques n Linear Programming n Integer Linear Programming n Network Models n PERT/CPM n Inventory models n Queuing Models n Simulation n Decision Analysis n Goal Programming n Analytic Hierarchy Process n Forecasting n Markov-Process Models n Dynamic Programming

19. 19 Slide End of Chapter 1