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184 CHAPTER 4 LINEAR PROGRAMMING APPLICATIONS

ADJUSTABLE CELLS
Final Reduced Objective Allowable Allowable
Name Value Cost Coefficient Increase Decrease
--- ---- ------ ---------- -------- --------
GAQ 33 0 178 1E+30 174
GSQ 44 0 268 1E+30 85
GVQ 22 0 228 85 4
GAY 16 0 380 1E+30 376
GSY 6 0 456 1E+30 273
GVY 11 0 560 1E+30 332
EAQ 26 0 199 1E+30 129
ESQ 36 0 249 55 70
EVQ 39 0 349 1E+30 55
EAY 15 0 385 1E+30 315
ESY 7 0 444 1E+30 195
EVY 9 0 580 1E+30 286
ASQ 31 0 179 70 55
ASY 8 0 380 1E+30 201
AVQ 41 0 224 4 85
AVY 10 0 582 1E+30 358

CONSTRAINTS
Final Shadow Constraint Allowable Allowable
Name Value Price R.H. Side Increase Decrease
--- ---- ----- --------- -------- --------
1 132 4 132 23 5
2 132 70 132 20 33
3 132 224 132 5 41
4 132 179 132 33 31
5 33 174 33 5 23
6 44 85 44 5 23
7 22 0 45 1E+30 23
8 16 376 16 5 16
9 6 273 6 5 6
10 11 332 11 22 11
11 26 129 26 33 20
12 36 0 56 1E+30 20
13 39 55 39 33 5
14 15 315 15 33 15
15 7 195 7 36 7
16 9 286 9 33 5
17 31 0 64 1E+30 33
18 8 201 8 31 8
19 41 0 46 1E+30 5
20 10 358 10 41 5

MBA programmes. We want to compare the performance of the different schools
against each other. It is not immediately obvious why LP is relevant in this type of
situation. After all, it does not look as if we will be trying to optimize anything.
However, let us look at the information shown in Table 4.17 and see how LP can be
applied. The data relate to four hospitals that provide health care in a particular

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