ENGINEERING OPTIMIZATION Methods and Applications

1. ENGINEERING OPTIMIZATION Methods and Applications A. Ravindran, K. M. Ragsdell, G. V. Reklaitis Book Review

2. Chapter 4: Linear Programming Part 1: Abu (Sayeem) Reaz Part 2: Rui (Richard) Wang Review Session June 25, 2010

3. Finding the optimum of any given world – how cool is that?!

4. Outline of Part 1 • Formulations • Graphical Solutions • Standard Form • Computer Solutions • Sensitivity Analysis • Applications • Duality Theory

5. Outline of Part 1 • Formulations • Graphical Solutions • Standard Form • Computer Solutions • Sensitivity Analysis • Applications • Duality Theory

6. What is an LP? • An LP has • An objective to find the best value for a system • A set of design variables that represents the system • A list of requirements that draws constraints the design variables The constraints of the system can be expressed as linear equations or inequalities and the objective function is a linear function of the design variables

7. Types Linear Program (LP): all variables are real Integer Linear Program (ILP): all variables are integer Mixed Integer Linear Program (MILP): variables are a mix of integer and real number Binary Linear Program (BLP): all variables are binary

8. Formulation • Formulation is the construction of LP models of real problems: • To identify the design/decision variables • Express the constraints of the problem as linear equations or inequalities • Write the objective function to be maximized or minimized as a linear function

9. The Wisdom of Linear Programming “Model building is not a science; it is primarily an art that is developed mainly by experience”

10. Example 4.1 • Two grades of inspectors for a quality control inspection • At least 1800 pieces to be inspected per 8-hr day • Grade 1 inspectors: 25 inspections/hour, accuracy = 98%, wage=$4/hour • Grade 2 inspectors: 15 inspections/hour, accuracy= 95%, wage=$3/hour • Penalty=$2/error • Position for 8 “Grade 1” and 10 “Grade 2” inspectors Let’s get experienced!!

11. Final Formulation for Example 4.1

12. Example 4.2

13. Nonlinearity “During each period, up to 50,000 MWh of electricity can be sold at $20.00/MWh, and excess power above 50,000 MWh can only be sold for $14.00/MW” Piecewise  Linear in the regions (0, 50000) and (50000, ∞)

14. Let’s Formulate

15. Final Formulation for Example 4.2

16. Outline of Part 1 • Formulations • Graphical Solutions • Standard Form • Computer Solutions • Sensitivity Analysis • Applications • Duality Theory

17. Definitions • Feasible Solution: all possible values of decision variables that satisfy the constraints • Feasible Region: the set of all feasible solutions • Optimal Solution: The best feasible solution • Optimal Value: The value of the objective function corresponding to an optimal solution

18. Graphical Solution: Example 4.3 • A straight line if the value of Z is fixed a priori • Changing the value of Z  another straight line parallel to itself • Search optimal solution  value of Z such that the line passes though one or more points in the feasible region

19. Graphical Solution: Example 4.4 • All points on line BC are optimal solutions

20. Realizations • Unique Optimal Solution: only one optimal value (Example 4.1) • Alternative/Multiple Optimal Solution: more than one feasible solution (Example 4.2) • Unbounded Optimum: it is possible to find better feasible solutions improving the objective values continuously (e.g., Example 2 without ) Property: If there exists an optimum solution to a linear programming problem, then at least one of the corner points of the feasible region will always qualify to be an optimal solution!

21. Outline of Part 1 • Formulations • Graphical Solutions • Standard Form • Computer Solutions • Sensitivity Analysis • Applications • Duality Theory

22. Standard Form (Equation Form)

23. Standard Form (Matrix Form) (A is the coefficient matrix, x is the decision vector, b is the requirement vector, and c is the profit (cost) vector)

24. Handling Inequalities Slack Using Equalities Surplus Using Bounds

25. Unrestricted Variables In some situations, it may become necessary to introduce a variable that can assume both positive and negative values!

26. Conversion: Example 4.5

27. Conversion: Example 4.5

28. Recap

29. Outline of Part 1 • Formulations • Graphical Solutions • Standard Form • Computer Solutions • Sensitivity Analysis • Applications • Duality Theory

30. Computer Codes • For small/simple LPs: • Microsoft Excel • For High-End LP: • OSL from IBM • ILOG CPLEX • OB1 in XMP Software • Modeling Language: • GAMS (General Algebraic Modeling System) • AMPL (A Mathematical Programming Language) • Internet • http: / /www.ece.northwestern.edu/otc

31. Outline of Part 1 • Formulations • Graphical Solutions • Standard Form • Computer Solutions • Sensitivity Analysis • Applications • Duality Theory

32. Sensitivity Analysis • Variation in the values of the data coefficients changes the LP problem, which may in turn affect the optimal solution. • The study of how the optimal solution will change with changes in the input (data) coefficients is known as sensitivity analysis or post-optimality analysis. • Why? • Some parameters may be controllable  better optimal value • Data coefficients from statistical estimation  identify the one that effects the objective value most  obtain better estimates

33. Example 4.9 100 hr of labor, 600 lb of material, and 300hr of administration per day

34. Solution A. Felt, ‘‘LINDO: API: Software Review,’’ OR/MS Today, vol. 29, pp. 58–60, Dec. 2002.

35. Outline of Part 1 • Formulations • Graphical Solutions • Standard Form • Computer Solutions • Sensitivity Analysis • Applications • Duality Theory

36. Applications of LP For any optimization problem in linear form with feasible solution time!

37. Outline of Part 1 • Formulations • Graphical Solutions • Standard Form • Computer Solutions • Sensitivity Analysis • Applications • Duality Theory (Additional Topic)

38. Duality of LP Every linear programming problem has an associated linear program called its dual such that a solution to the original linear program also gives a solution to its dual Solve one, get one free!!

39. Reversed Constraint constants Objective coefficients Columns into constraints and constraints into columns Find a Dual: Example 4.10

40. Find a Dual: Example 4.10

41. Some Tricks • • “Binarization” • If • • OR • • AND • • Finding Range • • Finding the value of a variable http://networks.cs.ucdavis.edu/ppt/group_meeting_22may2009.pdf

42. Binarization • x is positive real, z is binary, M is a large number • For a single variable • • For a set of variable

43. If • Both x and y are binary • If two variables share the same value • • If y = 0, then x = 0 • • If y = 1, then x = 1 • If they may have different values • • If y = 1, then x = 1 • • Otherwise x can take either 1 or 0

44. OR • A, x, y, and z are binary • • M is a large number • • If any of x,y,z are 1 then A is 1 • • If all of x,y,z are 0 then A is 0

45. AND • x, y, and z are binary • • If any of x,y are 0 then z is 0 • • If all of x,y are 1 then z is 1

46. Range • x and y are integers, z is binary • We want to find out if x falls within a range defined by y • • If x >= y, z is true • • If x <= y, z is true

47. Finding a Value • A,B,C are binary • • If x = y, Cy is true x takes the value of y if both the ranges are true

48. Thank You! Now Part 2 begins….