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Hands-on Tutorial on Optimization F. Eberle, R. Hoeksma, and N. Megow September 24, 2018 Modeling What is a model? What is a model? In general A representation of a real thing. What is a model? In general A representation of a real thing.


  1. Hands-on Tutorial on Optimization F. Eberle, R. Hoeksma, and N. Megow September 24, 2018 Modeling

  2. What is a model?

  3. What is a model? In general A representation of a real thing.

  4. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch

  5. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original)

  6. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original) ◮ Model of an insect (often larger than the original)

  7. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original) ◮ Model of an insect (often larger than the original) ◮ Model of a human skeleton (same size as the original)

  8. Example The real thing Models

  9. Example The real thing Models

  10. Example The real thing Models

  11. Example The real thing Models

  12. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original) ◮ Model of an insect (larger than the original) ◮ Model of a human skeleton (same size as the original) ◮ Abstract models ◮ Description of a real thing (situation)

  13. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original) ◮ Model of an insect (larger than the original) ◮ Model of a human skeleton (same size as the original) ◮ Abstract models ◮ Description of a real thing (situation) ◮ All information necessary for some purpose

  14. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original) ◮ Model of an insect (larger than the original) ◮ Model of a human skeleton (same size as the original) ◮ Abstract models ◮ Description of a real thing (situation) ◮ All information necessary for some purpose Example: Small World factory produces globes and maps

  15. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original) ◮ Model of an insect (larger than the original) ◮ Model of a human skeleton (same size as the original) ◮ Abstract models ◮ Description of a real thing (situation) ◮ All information necessary for some purpose Example: Small World factory produces globes and maps ◮ List of materials: wood, metal, plastics, screws, paper, and glue

  16. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original) ◮ Model of an insect (larger than the original) ◮ Model of a human skeleton (same size as the original) ◮ Abstract models ◮ Description of a real thing (situation) ◮ All information necessary for some purpose Example: Small World factory produces globes and maps ◮ List of materials: wood, metal, plastics, screws, paper, and glue ◮ Prices, supply, demand

  17. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original) ◮ Model of an insect (larger than the original) ◮ Model of a human skeleton (same size as the original) ◮ Abstract models ◮ Description of a real thing (situation) ◮ All information necessary for some purpose Example: Small World factory produces globes and maps ◮ List of materials: wood, metal, plastics, screws, paper, and glue ◮ Prices, supply, demand ◮ Print patterns

  18. What is a model? In general A representation of a real thing. ◮ Concrete models - models that you can touch ◮ Model car (smaller than the original) ◮ Model of an insect (larger than the original) ◮ Model of a human skeleton (same size as the original) ◮ Abstract models ◮ Description of a real thing (situation) ◮ All information necessary for some purpose Example: Small World factory produces globes and maps ◮ List of materials: wood, metal, plastics, screws, paper, and glue ◮ Prices, supply, demand ◮ Print patterns ◮ Assembly procedures

  19. Why use models? Gain information by: ◮ examining concrete models

  20. Why use models? Gain information by: ◮ examining concrete models ◮ getting an overview of important properties

  21. Why use models? Gain information by: ◮ examining concrete models ◮ getting an overview of important properties ◮ constructing concrete/abstract models (think about them)

  22. Why use models? Gain information by: ◮ examining concrete models ◮ getting an overview of important properties ◮ constructing concrete/abstract models (think about them) ◮ analyzing abstract/mathematical models

  23. Why use models? Gain information by: ◮ examining concrete models ◮ getting an overview of important properties ◮ constructing concrete/abstract models (think about them) ◮ analyzing abstract/mathematical models Use the gained information to:

  24. Why use models? Gain information by: ◮ examining concrete models ◮ getting an overview of important properties ◮ constructing concrete/abstract models (think about them) ◮ analyzing abstract/mathematical models Use the gained information to: ◮ Cut costs

  25. Why use models? Gain information by: ◮ examining concrete models ◮ getting an overview of important properties ◮ constructing concrete/abstract models (think about them) ◮ analyzing abstract/mathematical models Use the gained information to: ◮ Cut costs ◮ Increase productivity

  26. Why use models? Gain information by: ◮ examining concrete models ◮ getting an overview of important properties ◮ constructing concrete/abstract models (think about them) ◮ analyzing abstract/mathematical models Use the gained information to: ◮ Cut costs ◮ Increase productivity ◮ Make predictions

  27. Why use models? Gain information by: ◮ examining concrete models ◮ getting an overview of important properties ◮ constructing concrete/abstract models (think about them) ◮ analyzing abstract/mathematical models Use the gained information to: ◮ Cut costs ◮ Increase productivity ◮ Make predictions ◮ Automate decision processes

  28. Modeling Real world situation

  29. Modeling Real world situation Formulation Model

  30. Modeling Real world situation Formulation Model Analysis/solver Mathematical solution

  31. Modeling Real world situation Formulation Real solution Model Interpretation Analysis/solver Mathematical solution

  32. Modeling Real world situation Evalutation Formulation Real solution Model Interpretation Analysis/solver Mathematical solution

  33. Mathematical models Formulation ◮ Mathematical language ◮ Parameters: known data ◮ Variables: unknown (decisions) ◮ Operators: ◮ × , + , − , = , � = , <, ≥ , and , or , � , � Formulation should fit the available solution methods ◮ Mathematical theory ◮ Algorithms

  34. Types of mathematical models Many types of mathematical models, e.g.: ◮ linear vs. non-linear ◮ stochastic vs. deterministic ◮ continuous vs. discrete ◮ dynamic vs. static

  35. Types of mathematical models Many types of mathematical models, e.g.: ◮ linear vs. non-linear ◮ stochastic vs. deterministic ◮ continuous vs. discrete ◮ dynamic vs. static In this course: ◮ (integer) linear programming models (ILP/LP) ◮ mathematically assisted decision making ◮ optimization according to objectives

  36. Linear programming Question → (linear) objective function Data → parameters Possible decisions → variables Conditions that have to be met → (linear) constraints

  37. Linear programming Question → (linear) objective function Data → parameters Possible decisions → variables Conditions that have to be met → (linear) constraints Structure of an (I)LP min / max [linear objective function] s.t. [linear constraints]

  38. Linear programming Question → (linear) objective function Data → parameters Possible decisions → variables Conditions that have to be met → (linear) constraints Example min x + 2 y x + 1 s.t. 2 y ≤ 7 x ≥ y x ≥ 0 y ≥ 0

  39. Which expressions work, which do not? ◮ Variables: x , y ◮ Parameters: a , b , c Linear:

  40. Which expressions work, which do not? ◮ Variables: x , y ◮ Parameters: a , b , c Linear: x + y = 9 ax − by ≤ c a b x = y ( x − 1) b ≤ a (1 − y )

  41. Which expressions work, which do not? ◮ Variables: x , y ◮ Parameters: a , b , c Linear: x + y = 9 ax − by ≤ c a b x = y ( x − 1) b ≤ a (1 − y ) Non-linear: x a + y 2 ≥ 9 xy − ay ≤ c e x ≥ y

  42. Example - Potato chips factory Client owns a potato chips factory

  43. Example - Potato chips factory Client owns a potato chips factory Two types of chips ◮ Plain chips ◮ Mexican ribble chips

  44. Example - Potato chips factory Client owns a potato chips factory Two types of chips ◮ Plain chips ◮ Mexican ribble chips Three processes: ◮ Slicing ◮ Frying ◮ Packaging

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