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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

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.

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

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

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

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.

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

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.

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

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

Example All-Integer LP

- Partial Spreadsheet Showing Problem Data

Example All-Integer LP

- Partial Spreadsheet Showing Formulas

Example All-Integer LP

- Partial Spreadsheet Showing Optimal Solution

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

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.

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

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.

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

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

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

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

Example Metropolitan Microwaves

- Partial Spreadsheet Showing Problem Data

Example Metropolitan Microwaves

- Partial Spreadsheet Showing Example Formulas

Example Metropolitan Microwaves

- Solver Parameters Dialog Box

Example Metropolitan Microwaves

- Solver Options Dialog Box

Example Metropolitan Microwaves

- Integer Options Dialog Box

Example Metropolitan Microwaves

- Optimal Solution

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

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

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).

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

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

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

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

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.

End of Chapter 8

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