1 00:00:04,500 --> 00:00:06,380 So we saw in the previous video how 2 00:00:06,380 --> 00:00:11,240 we can solve linear optimization problems in LibreOffice. 3 00:00:11,240 --> 00:00:13,680 Let's now try to get some intuition for what's 4 00:00:13,680 --> 00:00:16,770 going on by visualizing our problem. 5 00:00:16,770 --> 00:00:20,230 Since we only have two decisions, R and D, 6 00:00:20,230 --> 00:00:23,570 we can visualize our constraints in two dimensions. 7 00:00:23,570 --> 00:00:28,620 We'll plot D on the x-axis, and R on the y-axis. 8 00:00:28,620 --> 00:00:31,370 We first have non-negativity constraints, 9 00:00:31,370 --> 00:00:36,090 so R and D are both greater than zero. 10 00:00:36,090 --> 00:00:40,530 We can plot the capacity constraint, R + D less than 11 00:00:40,530 --> 00:00:45,330 or equal to 166, which is shown as the red line here. 12 00:00:45,330 --> 00:00:48,260 Our solution has to be to the left of this line 13 00:00:48,260 --> 00:00:51,530 according to this constraint. 14 00:00:51,530 --> 00:00:54,460 Now, let's add in our demand constraints. 15 00:00:54,460 --> 00:00:57,960 The regular seats should be less than the demand of 100, which 16 00:00:57,960 --> 00:01:02,250 requires the solution to be below this blue line. 17 00:01:02,250 --> 00:01:06,180 And the discount seats should be less than the demand of 150, 18 00:01:06,180 --> 00:01:08,140 which requires the solution to be 19 00:01:08,140 --> 00:01:11,220 to the left of this green line. 20 00:01:11,220 --> 00:01:13,660 Taken together, our constraints define 21 00:01:13,660 --> 00:01:16,820 what we call our "feasible space" or the space 22 00:01:16,820 --> 00:01:19,170 of all possible values that our decisions can 23 00:01:19,170 --> 00:01:22,620 take according to our constraints. 24 00:01:22,620 --> 00:01:26,430 To find the optimal solution now in our feasible space, 25 00:01:26,430 --> 00:01:33,990 we have to use the objective, 617*R + 238*D. 26 00:01:33,990 --> 00:01:38,410 We can plot this objective in our feasible space. 27 00:01:38,410 --> 00:01:40,050 So to know how many seats we should 28 00:01:40,050 --> 00:01:42,560 sell to achieve a certain revenue, 29 00:01:42,560 --> 00:01:45,430 we can see different values of this line. 30 00:01:45,430 --> 00:01:48,800 So to achieve a revenue of $20,000, 31 00:01:48,800 --> 00:01:51,729 our solution has to be somewhere on this line in our feasible 32 00:01:51,729 --> 00:01:53,120 space. 33 00:01:53,120 --> 00:01:56,440 To achieve a revenue of $40,000, our solution 34 00:01:56,440 --> 00:01:59,940 has to be somewhere on this line in our feasible space. 35 00:01:59,940 --> 00:02:03,110 And to achieve a revenue of $60,000, 36 00:02:03,110 --> 00:02:06,300 our solution has to be somewhere on this line in our feasible 37 00:02:06,300 --> 00:02:08,120 space. 38 00:02:08,120 --> 00:02:11,690 Since the revenue is increasing as we move this line up 39 00:02:11,690 --> 00:02:14,620 and our goal is to maximize the revenue, 40 00:02:14,620 --> 00:02:17,000 our optimal solution will be where 41 00:02:17,000 --> 00:02:20,000 this line can't go any further and still 42 00:02:20,000 --> 00:02:21,970 be in our feasible space. 43 00:02:21,970 --> 00:02:25,100 So our optimal solution is at this point 44 00:02:25,100 --> 00:02:30,430 with a revenue of $77,408. 45 00:02:30,430 --> 00:02:32,730 As we can see here, the solution is 46 00:02:32,730 --> 00:02:35,850 dependent on how the feasible space was defined. 47 00:02:35,850 --> 00:02:37,940 In the next video, we'll see what 48 00:02:37,940 --> 00:02:41,720 happens if our capacity and demands change.