Spring 2018 IE 102. Operations Research and Mathematical Programming Part 2
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1 Spring 2018 IE 102 Operations Research and Mathematical Programming Part 2
2 Graphical Solution of 2-variable LP Problems Consider an example max x x 2 s.t. x 1 + x 2 6 (1) - x 1 + 2x 2 8 (2) x 1, x 2 0 (3), (4)
3 x 2 (3) -x 1 + 2x 2 = 8 (1) (2) (0,6) (0,4) (4/3,14/3) (4) (4) (-8,0) (0,0) (6,0) x 1 (2) (3) (1) x 1 + x 2 = 6
4 x 2 (3) -x 1 + 2x 2 = 8 (1) (2) (0,6) (0,4) (4/3,14/3) Feasible Region (4) (4) (-8,0) (0,0) (6,0) x 1 (2) (3) (1) x 1 + x 2 = 6
5 x 2 (3) -x 1 + 2x 2 = 8 (1) (2) (0,6) (0,4) (4/3,14/3) (4) (4) (-8,0) (0,0) (6,0) x 1 (2) (3) (1) x 1 + x 2 = 6
6 x 2 (0,6) (0,4) (4/3,14/3) -x 1 + 2x 2 = 8 Recall the objective function: max x 1 + 3x 2 (-8,0) (0,0) (6,0) x 1 x 1 + 3x 2 = 6 x 1 + 3x 2 = 3 x 1 + x 2 = 6 x 1 + 3x 2 = 0
7 Graphical Solution of 2-variable LP Problems A line on which all points have the same z-value (z = x 1 + 3x 2 ) is called : Isoprofit line for maximization problems, Isocost line for minimization problems.
8 Graphical Solution of 2-variable LP Problems Consider the coefficients of x 1 and x 2 in the objective function and let c=[1 3].
9 x 2 (0,6) (0,4) (4/3,14/3) -x 1 + 2x 2 = 8 objective function: max x 1 + 3x 2 c=[1 3] (-8,0) (0,0) (6,0) x 1 x 1 + 3x 2 = 6 x 1 + 3x 2 = 3 x 1 + x 2 = 6 x 1 + 3x 2 = 0
10 Graphical Solution of 2-variable LP Problems Note that as move the isoprofit lines in the direction of c, the total profit increases for this max problem!
11 Graphical Solution of 2-variable LP Problems For a maximization problem, the vector c, given by the coefficients of x 1 and x 2 in the objective function, is called the improving direction. On the other hand, -c is the improving direction for minimization problems!
12 Graphical Solution of 2-variable LP Problems To solve an LP problem in the two variables, we should try to push isoprofit (isocost) lines in the improving direction as much as possible while still staying in the feasible region.
13 x 2 (0,6) (0,4) (0,3) (0,2) (1,0) (4/3,14/3) c=[1 3] -x 1 + 2x 2 = 8 Objective : max x 1 + 3x 2 (-8,0) (0,0) (3,0) (6,0) (9,0) x 1 x 1 + 3x 2 = 9 x 1 + 3x 2 = 6 x 1 + 3x 2 = 3 x 1 + x 2 = 6 x 1 + 3x 2 = 0
14 x 2 (0,6) (0,4) (0,3) (4/3,14/3) c=[1 3] c=[1 3] -x 1 + 2x 2 = 8 Objective : max x 1 + 3x 2 (-8,0) (0,0) (6,0) (9,0) x 1 x 1 + 3x 2 = 9 x 1 + x 2 = 6
15 x 2 (0,6) (0,4) (0,3) (4/3,14/3) c=[1 3] c=[1 3] -x 1 + 2x 2 = 8 Objective : max x 1 + 3x 2 (-8,0) (0,0) (6,0) (9,0) x 1 (12,0) x 1 + x 3x x = 12 2 = 9 x 1 + x 2 = 6
16 x 2 (0,6) (0,4) c=[1,3] (4/3,14/3) -x 1 + 2x 2 = 8 Objective : max x 1 + 3x 2 x 1 +3x 2 = 46/3 (-8,0) (0,0) (6,0) x 1 x 1 + x 2 = 6
17 x 2 -x 1 + 2x 2 = 8 (0,6) (0,4) (4/3,14/3) Objective : max x 1 + 3x 2 c=[1,3] (-8,0) (0,0) (6,0) Can we improve (6,0)? x 1 x 1 + x 2 = 6
18 x 2 (0,6) Can we improve (0,4)? (0,4) (4/3,14/3) c=[1 3] -x 1 + 2x 2 = 8 Objective : max x 1 + 3x 2 (-8,0) (0,0) (6,0) x 1 x 1 + x 2 = 6
19 x 2 -x 1 + 2x 2 = 8 (0,6) (0,4) Objective : max x 1 + 3x 2 (4/3,14/3) c=[1 3] Can we improve (4/3,16/3)? No! We stop here (-8,0) (0,0) (6,0) x 1 x 1 + x 2 = 6
20 x 2 (0,6) (0,4) c=[1 3] (4/3,14/3) -x 1 + 2x 2 = 8 Objective : max x 1 + 3x 2 (4/3,14/3) is the OPTIMAL solution (-8,0) (0,0) (6,0) x 1 x 1 + x 2 = 6
21 Graphical Solution of 2-variable LP Problems Point (4/3, 14/3) is the last point in the feasible region when we move in the improving direction. So, if we move in the same direction any further, there is no feasible point. Therefore, point z*= (4/3, 14/3) is the maximizing point. The optimal value of the LP problem is 46/3.
22 Graphical Solution of 2-variable LP Problems: We may graphically solve an LP with two decision variables as follows: Step 1: Graph the feasible region. Step 2: Draw an isoprofit line (for max problem) or an isocost line (for min problem). Step 3: Move parallel to the isoprofit/isocost line in the improving direction. The last point in the feasible region that contacts an isoprofit/isocost line is an optimal solution to the LP.
23 More examples 23
24 Graphical Solution of 2-variable LP Problems Let us modify the objective function while keeping the constraints unchanged: Ex 1.a) max x x 2 s.t. x 1 + x 2 6 (1) - x 1 + 2x 2 8 (2) x 1, x 2 0 (3), (4) Ex 1.b) max x 1-2 x 2 s.t. x 1 + x 2 6 (1) - x 1 + 2x 2 8 (2) x 1, x 2 0 (3), (4)
25 Graphical Solution of 2-variable LP Problems Does feasible region change?
26 x 2 (3) -x 1 + 2x 2 = 8 (1) (2) (0,6) (0,4) (4/3,14/3) Feasible Region (4) (4) (-8,0) (0,0) (6,0) x 1 (2) (3) (1) x 1 + x 2 = 6
27 Graphical Solution of 2-variable LP Problems What about the improving direction?
28 x 2 (3) -x 1 + 2x 2 = 8 (1) (2) (0,6) (0,4) (4/3,14/3) x 1-2x 2 = 0 Feasible Region (4) (4) (-8,0) (2) (0,0) c=[1-2] (6,0) x 1 (3) (1) x 1 + x 2 = 6
29 x 2 (3) -x 1 + 2x 2 = 8 (1) (2) (0,6) (0,4) (4/3,14/3) Feasible Region (6,0) is the optimal solution! (4) (4) (-8,0) (2) (0,0) c=[1-2] (6,0) x 1 (3) (1) x 1 + x 2 = 6
30 Graphical Solution of 2-variable LP Problems Now let us consider: Ex 1.c) max -x 1 + x 2 s.t. x 1 + x 2 6 (1) - x 1 + 2x 2 8 (2) x 1, x 2 0 (3), (4)
31 x 2 (3) -x 1 + 2x 2 = 8 (1) (2) (0,6) (0,4) (4/3,14/3) c=[-1 1] (4) (4) (-8,0) (0,0) (6,0) x 1 (2) (3) (1) x 1 + x 2 = 6
32 Graphical Solution of 2-variable LP Problems Now, let us consider: Ex 1.c) max -x 1 + x 2 s.t. x 1 + x 2 6 (1) Question: Is it possible for (5,1) be the optimal? - x 1 + 2x 2 8 (2) x 1, x 2 0 (3), (4)
33 x 2 (1) (3) (2) -x 1 + 2x 2 = 8 (0,6) (0,4) (4/3,14/3) -x 1 + x 2 = -4 (-8,0) (2) (4) c=[-1 1] (0,0) improving direction! (5,1) (6,0) (4) x 1 (3) (1) x 1 + x 2 = 6
34 x 2 (1) (3) (2) -x 1 + 2x 2 = 8 (0,6) (0,4) (4/3,14/3) -x 1 + x 2 = 0 -x 1 + x 2 = -4 (3,3) (-8,0) (2) (4) c=[-1 1] (0,0) improving direction! (5,1) (6,0) (4) x 1 (3) (1) x 1 + x 2 = 6
35 x 2 (3) -x 1 + x 2 = 4 -x 1 + x 2 = 10/3 -x 1 + 2x 2 = 8 (1) (2) Point (0,4) is the optimal solution! (0,6) (0,4) (4/3,14/3) -x 1 + x 2 = 0 -x 1 + x 2 = -4 (3,3) c=[-1 1] (4) (5,1) (4) (-8,0) (0,0) (6,0) x 1 (2) (3) (1) x 1 + x 2 = 6
36 Graphical Solution of 2-variable LP Problems Now, let us consider: Ex 1.d) max -x 1 - x 2 s.t. x 1 + x 2 6 (1) Question: Is it possible for (0,4) be the optimal? - x 1 + 2x 2 8 (2) x 1, x 2 0 (3), (4)
37 x 2 -x 1 - x 2 = 0 -x 1 - x 2 = -4 (1) (0,6) (0,4) (3) (4/3,14/3) (2) -x 1 + 2x 2 = 8 (-8,0) (2) Point (0,0) is the (4) optimal solution! (0,0) c=[-1-1] (6,0) (4) x 1 (3) (1) x 1 + x 2 = 6
38 Another Example
39 Graphical Solution of 2-variable LP Problems Ex 2) A company wishes to increase the demand for its product through advertising. Each minute of radio ad costs $1 and each minute of TV ad costs $2. Each minute of radio ad increases the daily demand by 2 units and each minute of TV ad by 7 units. The company would wish to place at least 9 minutes of daily ad in total. It wishes to increase daily demand by at least 28 units. How can the company meet its advertising requirementsat minimum total cost?
40 Graphical Solution of 2-variable LP Problems x 1 : # of minutes of radio ads purchased x 2 : # of minutes of TV ads purchased
41 Graphical Solution of 2-variable LP Problems x 1 : # of minutes of radio ads purchased x 2 : # of minutes of TV ads purchased min x x 2 s.t. 2x 1 + 7x 2 28 (1) x 1 + x 2 9 (2) x 1, x 2 0 (3), (4)
42 x 2 (2) (3) (1) (0,9) (0,4) (4) (0,0) (9,0) (14,0) (4) (1) x 1 (2) 2x 1 +7x 2 =28 (3) x 1 + x 2 = 9
43 x 2 (2) (3) Objective : min x 1 + 2x 2 (1) (0,9) c=[1 2] total cost x 1 + 2x 2 INCREASES in this direction! -c=[-1-2] -c=[-1-2] is the improving direction (0,4) (7,2) x 1 +2x 2 =24 (4) (4) (0,0) (9,0) (14,0) (1) x 1 (2) 2x 1 +7x 2 =28 (3) x 1 + x 2 = 9
44 x 2 (2) (3) Objective : min x 1 + 2x 2 (1) (0,9) (4) (0,4) (0,0) -c=[-1-2] (7,2) x x 1 +2x 2 = x 2 =18 (4) (9,0) (14,0) (1) x 1 2x 1 +7x 2 =28 (2) (3) x 1 + x 2 = 9
45 x 2 (2) (3) Objective : min x 1 + 2x 2 (1) (0,9) (4) (0,4) (0,0) (3) (7,2) (9,0) (14,0) (7,2) is the optimal solution and optimal value is 11 x 1 +2x 2 =18 (2) (4) (1) x 1 2x 1 +7x 2 =28 x x 1 + x 2 = 9 x 1 +2x 2 = x 2 =11
46 Graphical Solution of 2-variable LP Problems: We may graphically solve an LP with two decision variables as follows: Step 1: Graph the feasible region. Step 2: Draw an isoprofit line (for max problem) or an isocost line (for min problem). Step 3: Move parallel to the isoprofit/isocost line in the improving direction. The last point in the feasible region that contacts an isoprofit/isocost line is an optimal solution to the LP.
47 More challenging modelling examples 47
48 Trim Loss Problem Certain products are manufactured in wide rolls. Wide rolls are later cut into rolls of smaller strips to meet specific demand. Examples: Paper, photographic film, sheet metal, textile fabric. 48
49 Another Example 100 of this shape 7 of this shape Available stock 22 of this shape and size How to cut depends on the demand 49
50 Example: Wrapping Paper Paper rolls are manufactured as 60 inch (wideness). Demand is for 10-inch, 20-inch, 25-inch and 30-inch rolls. customer 10-inch 20-inch 25-inch 30-inch X Y Z
51 Examples: 6 10-inch 3 20-inch 2 30-inch Possible patterns? 4 10-inch inch... What do you think our concern will be? Waste remains of the original roll Total number of original rolls used 51
52 A simpler example to work-out Wider rolls with 48-inch Demand is for 21-inch and 25-inch Patterns 25-inch 21-inch Waste Pattern A Pattern B Demand: inch rolls, inch rolls 52
53 Example continued Decision variables: x : the number rolls cut into Pattern A y : the number rolls cut into Pattern B Constraints: We want to satisfy demand y 20; 2x + y 50 x 0, y 0 Objective function: Minimize waste where waste = w = 6x + 2y 25-inch 21-inch Waste Pattern A Pattern B
54 Example continued 54
55 Example continued Solution? Corner solution: (0,50), w = 100 What is the problem with the solution? Total produced: inch inch Demand? inch inch You need to be able to store the additional 25-inch rolls and use later. Any additional cost even if you can? 55
56 56
57 You are going on a trip. Your Suitcase has a capacity of b kg s. You have n different items that you can pack into your suitcase 1,..., n. Item i has a weight of a i and gives you a utility of c i. How should you pack your suitcase to maximize total utility? Knapsack problem
58 Knapsack problem
59 Knapsack problem
60 Knapsack problem This is also an example of an IP problem since x i can only take one of the two integer values 0 and 1.
61 KAHOOT
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