Chapter 8 Integer Linear Programming - PowerPoint PPT Presentation

Loading...

PPT – Chapter 8 Integer Linear Programming PowerPoint presentation | free to download - id: 12fd68-ODg4M



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Chapter 8 Integer Linear Programming

Description:

Types of Integer Linear Programming Models. Graphical and Computer Solutions for ... Bullfighter's outfit X 20 20 18 21. 31. Slide 2005 Thomson/South-Western ... – PowerPoint PPT presentation

Number of Views:196
Avg rating:3.0/5.0
Slides: 38
Provided by: bloch8
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Chapter 8 Integer Linear Programming


1
(No Transcript)
2
Chapter 8Integer Linear Programming
  • Types of Integer Linear Programming Models
  • Graphical and Computer Solutions for an
    All-Integer Linear Program
  • Applications Involving 0-1 Variables
  • Modeling Flexibility Provided by 0-1 Variables

3
Types of Integer Programming Models
  • An LP in which all the variables are restricted
    to be integers is called an all-integer linear
    program (ILP).
  • The LP that results from dropping the integer
    requirements is called the LP Relaxation of the
    ILP.
  • If only a subset of the variables are restricted
    to be integers, the problem is called a
    mixed-integer linear program (MILP).
  • Binary variables are variables whose values are
    restricted to be 0 or 1. If all variables are
    restricted to be 0 or 1, the problem is called a
    0-1 or binary integer linear program.

4
Example All-Integer LP
  • Consider the following all-integer linear
    program
  • Max 3x1 2x2
  • s.t. 3x1 x2 lt
    9
  • x1 3x2
    lt 7
  • -x1 x2
    lt 1
  • x1, x2 gt 0 and
    integer

5
Example All-Integer LP
  • LP Relaxation
  • Solving the problem as a linear program
    ignoring the integer constraints, the optimal
    solution to the linear program gives fractional
    values for both x1 and x2. From the graph on the
    next slide, we see that the optimal solution to
    the linear program is
  • x1 2.5, x2 1.5, z 10.5

6
Example All-Integer LP
  • LP Relaxation

x2
5
-x1 x2 lt 1
3x1 x2 lt 9
4
Max 3x1 2x2
3
LP Optimal (2.5, 1.5)
2
x1 3x2 lt 7
1
x1
1 2 3 4 5
6 7
7
Example All-Integer LP
  • Rounding Up
  • If we round up the fractional solution (x1
    2.5, x2 1.5) to the LP relaxation problem, we
    get x1 3 and x2 2. From the graph on the next
    slide, we see that this point lies outside the
    feasible region, making this solution
    infeasible.

8
Example All-Integer LP
  • Rounded Up Solution

x2
5
-x1 x2 lt 1
3x1 x2 lt 9
4
Max 3x1 2x2
3
ILP Infeasible (3, 2)
2
LP Optimal (2.5, 1.5)
x1 3x2 lt 7
1
x1
1 2 3 4 5
6 7
9
Example All-Integer LP
  • Rounding Down
  • By rounding the optimal solution down to x1
    2, x2 1, we see that this solution indeed is an
    integer solution within the feasible region, and
    substituting in the objective function, it gives
    z 8.
  • We have found a feasible all-integer solution,
    but have we found the OPTIMAL all-integer
    solution?
  • ---------------------
  • The answer is NO! The optimal solution is x1
    3 and x2 0 giving z 9, as evidenced in the
    next two slides.

10
Example All-Integer LP
  • Complete Enumeration of Feasible ILP Solutions
  • There are eight feasible integer solutions to
    this problem
  • x1 x2 z
  • 1. 0 0 0
  • 2. 1 0 3
  • 3. 2 0 6
  • 4. 3 0 9
    optimal solution
  • 5. 0 1 2
  • 6. 1 1 5
  • 7. 2 1 8
  • 8. 1 2 7

11
Example All-Integer LP
x2
-x1 x2 lt 1
5
3x1 x2 lt 9
4
Max 3x1 2x2
3
ILP Optimal (3, 0)
2
x1 3x2 lt 7
1
x1
1 2 3 4 5
6 7
12
Example All-Integer LP
  • Partial Spreadsheet Showing Problem Data

13
Example All-Integer LP
  • Partial Spreadsheet Showing Formulas

14
Example All-Integer LP
  • Partial Spreadsheet Showing Optimal Solution

15
Special 0-1 Constraints
  • When xi and xj represent binary variables
    designating whether projects i and j have been
    completed, the following special constraints may
    be formulated
  • At most k out of n projects will be completed
  • ?xj lt k
  • j
  • Project j is conditional on project i
  • xj - xi lt 0
  • Project i is a corequisite for project j
  • xj - xi 0
  • Projects i and j are mutually exclusive
  • xi xj lt 1

16
Example Metropolitan Microwaves
  • Metropolitan Microwaves, Inc. is planning to
  • expand its operations into other
  • electronic appliances. The company
  • has identified seven new product lines
  • it can carry. Relevant information
  • about each line follows on the next slide.

17
Example Metropolitan Microwaves
  • Initial Floor Space Exp. Rate
  • Product Line Invest. (Sq.Ft.)
    of Return
  • 1. TV/VCRs 6,000 125 8.1
  • 2. Color TVs 12,000
    150 9.0
  • 3. Projection TVs 20,000 200
    11.0
  • 4. VCRs 14,000
    40 10.2
  • 5. DVD Players 15,000 40
    10.5
  • 6. Video Games 2,000
    20 14.1
  • 7. Home Computers 32,000 100
    13.2

18
Example Metropolitan Microwaves
  • Metropolitan has decided that they should not
    stock projection TVs unless they stock either
    TV/VCRs or color TVs. Also, they will not stock
    both VCRs and DVD players, and they will stock
    video games if they stock color TVs. Finally,
    the company wishes to introduce at least three
    new product lines.
  • If the company has 45,000 to invest and 420
    sq. ft. of floor space available, formulate an
    integer linear program for Metropolitan to
    maximize its overall expected return.

19
Example Metropolitan Microwaves
  • Define the Decision Variables
  • xj 1 if product line j is introduced
  • 0 otherwise.
  • where
  • Product line 1 TV/VCRs
  • Product line 2 Color TVs
  • Product line 3 Projection TVs
  • Product line 4 VCRs
  • Product line 5 DVD Players
  • Product line 6 Video Games
  • Product line 7 Home Computers

20
Example Metropolitan Microwaves
  • Define the Decision Variables
  • xj 1 if product line j is introduced
  • 0 otherwise.
  • Define the Objective Function
  • Maximize total expected return
  • Max .081(6000)x1 .09(12000)x2
    .11(20000)x3
  • .102(14000)x4 .105(15000)x5
    .141(2000)x6
  • .132(32000)x7

21
Example Metropolitan Microwaves
  • Define the Constraints
  • 1) Money
  • 6x1 12x2 20x3 14x4 15x5 2x6
    32x7 lt 45
  • 2) Space
  • 125x1 150x2 200x3 40x4 40x5
    20x6 100x7 lt 420
  • 3) Stock projection TVs only if
  • stock TV/VCRs or color TVs
  • x1 x2 gt x3 or x1 x2 - x3 gt 0

22
Example Metropolitan Microwaves
  • Define the Constraints (continued)
  • 4) Do not stock both VCRs and DVD players
  • x4 x5 lt 1
  • 5) Stock video games if they stock color
    TV's
  • x2 - x6 gt 0
  • 6) Introduce at least 3 new lines
  • x1 x2 x3 x4 x5 x6 x7 gt 3
  • 7) Variables are 0 or 1
  • xj 0 or 1 for j 1, , , 7

23
Example Metropolitan Microwaves
  • Partial Spreadsheet Showing Problem Data

24
Example Metropolitan Microwaves
  • Partial Spreadsheet Showing Example Formulas

25
Example Metropolitan Microwaves
  • Solver Parameters Dialog Box

26
Example Metropolitan Microwaves
  • Solver Options Dialog Box

27
Example Metropolitan Microwaves
  • Integer Options Dialog Box

28
Example Metropolitan Microwaves
  • Optimal Solution

29
Example Metropolitan Microwaves
  • Optimal Solution
  • Introduce
  • TV/VCRs, Projection TVs, and DVD Players
  • Do Not Introduce
  • Color TVs, VCRs, Video Games, and Home
    Computers
  • Total Expected Return
  • 4,261

30
Example Tinas Tailoring
  • Tina's Tailoring has five idle tailors and four
    custom garments to make. The estimated time (in
    hours) it would take each tailor to make each
    garment is shown in the next slide. (An 'X' in
    the table indicates an unacceptable
    tailor-garment assignment.)

  • Tailor
  • Garment 1 2
    3 4 5
  • Wedding gown 19 23 20 21 18
  • Clown costume 11 14 X 12
    10
  • Admiral's uniform 12 8 11 X
    9
  • Bullfighter's outfit X 20 20
    18 21

31
Example Tinas Tailoring
  • Formulate an integer program for determining
  • the tailor-garment assignments that minimize
  • the total estimated time spent making the four
  • garments. No tailor is to be assigned more than
    one
  • garment and each garment is to be worked on by
    only
  • one tailor.
  • --------------------
  • This problem can be formulated as a 0-1 integer
  • program. The LP solution to this problem will
  • automatically be integer (0-1).

32
Example Tinas Tailoring
  • Define the decision variables
  • xij 1 if garment i is assigned to tailor j
  • 0 otherwise.
  • Number of decision variables
  • (number of garments)(number of tailors)
  • - (number of unacceptable assignments)
  • 4(5) - 3 17

33
Example Tinas Tailoring
  • Define the objective function
  • Minimize total time spent making garments
  • Min 19x11 23x12 20x13 21x14 18x15
    11x21
  • 14x22 12x24 10x25 12x31 8x32
    11x33
  • 9x35 20x42 20x43 18x44 21x45

34
Example Tinas Tailoring
  • Define the Constraints
  • Exactly one tailor per garment
  • 1) x11 x12 x13 x14 x15 1
  • 2) x21 x22 x24 x25 1
  • 3) x31 x32 x33 x35 1
  • 4) x42 x43 x44 x45 1

35
Example Tinas Tailoring
  • Define the Constraints (continued)
  • No more than one garment per tailor
  • 5) x11 x21 x31 lt 1
  • 6) x12 x22 x32 x42 lt 1
  • 7) x13 x33 x43 lt 1
  • 8) x14 x24 x44 lt 1
  • 9) x15 x25 x35 x45 lt 1
  • Nonnegativity xij gt 0 for i 1, . . ,4 and
    j 1, . . ,5

36
Cautionary Note About Sensitivity Analysis
  • Sensitivity analysis often is more crucial for
    ILP problems than for LP problems.
  • A small change in a constraint coefficient can
    cause a relatively large change in the optimal
    solution.
  • Recommendation Resolve the ILP problem several
    times with slight variations in the coefficients
    before choosing the best solution for
    implementation.

37
End of Chapter 8
About PowerShow.com