Q.Determine the maximum value of Z=11x+7y subject to the constraints: 2x+y≤6, x≤2, x≥0, y≥0.
Concept understanding — Linear Programming Graphical Method
The Graphical Method for Linear Programming
When a linear programming problem has just two decision variables, x and y, you can solve it by drawing a picture. This is the graphical method, and it is the technique the CBSE Class-12 course expects you to use.
The idea
Each constraint is a linear inequality such as 2x+3y≤100. On the xy-plane its boundary is a straight line, and the inequality picks one side of that line (a half-plane). The points that satisfy all the constraints at once form a single region — the feasible region. Your job is to find the point inside this region that makes the objective function Z=ax+by largest or smallest.
The step-by-step procedure
- Draw each constraint line. Replace every inequality by an equation and plot the line, usually by finding where it meets the axes.
- Shade the correct side. Test a simple point (often the origin (0,0)) in the inequality. If it holds, the origin's side is the wanted half-plane; if not, take the other side. Always include the non-negativity conditions x≥0, y≥0, which keep you in the first quadrant.
- Identify the feasible region. It is the overlap of all the shaded half-planes — the region satisfying every constraint together.
- Find the corner (vertex) points. These are the points where the boundary lines cross. Read them off the graph or solve the two relevant lines simultaneously.
- Evaluate Z at every corner and pick the largest value (for a maximum) or the smallest (for a minimum).
The whole method rests on the Corner-Point Theorem: if an optimum exists, it occurs at a vertex of the feasible region. So you never test interior points — only the corners.
Bounded vs unbounded
If the feasible region is a closed polygon (bounded), both the maximum and minimum are guaranteed and are found among the corners. If the region stretches to infinity (unbounded), a maximum or minimum may fail to exist — you then check whether Z can be pushed indefinitely large or small in the open direction before concluding.
The bottom line
Graph the constraints, find the feasible region, list its corner points, and compare Z=ax+by at each. The best corner is your optimal solution — a clean, visual route to the answer for any two-variable LP problem.
The graphical method for solving linear programming problems is the entire method taught in the NCERT Class 12 Linear Programming chapter, and "linear programming graphical method examples class 12" is one of the most searched topics ahead of CBSE board exams. This corner-point approach is also occasionally tested in JEE Main and select state CET papers involving optimization.
Maximise Z=11x+7y over 2x+y≤6, x≤2, x≥0, y≥0 by testing the corner points.
Corners of the feasible region:
- (0,0)
- (2,0) — from x=2, y=0
- (2,2) — from x=2 and 2x+y=6
- (0,6) — from x=0 and 2x+y=6
Values of Z=11x+7y:
- (0,0):0
- (2,0):22
- (2,2):22+14=36
- (0,6):0+42=42
The largest is 42.
Maximum Z=42, attained at (0,6).
Testing the four corners of the feasible region, Z=11x+7y is largest at (0,6), where Z=42.
Set-up
We maximise Z=11x+7y subject to
2x+y≤6,x≤2,x≥0, y≥0.
By the corner-point theorem the maximum of a linear objective over a bounded region occurs at a vertex, so we only need the vertices.
Step 1 — Find the corner points
The boundary lines are x=0, y=0, x=2 and 2x+y=6 (intercepts (3,0),(0,6)).
- x=0,y=0⇒(0,0).
- x=2,y=0⇒(2,0).
- x=2 in 2x+y=6⇒4+y=6⇒y=2⇒(2,2).
- x=0 in 2x+y=6⇒y=6⇒(0,6).
Do not use (3,0): although 2x+y=6 meets the x-axis there, x=3 breaks x≤2, so (3,0) is outside the feasible region. The line x=2 cuts it off.
So the feasible region is the quadrilateral (0,0),(2,0),(2,2),(0,6).
Step 2 — Evaluate Z=11x+7y
| Vertex | Z=11x+7y |
|---|---|
| (0,0) | 0 |
| (2,0) | 22 |
| (2,2) | 22+14=36 |
| (0,6) | 0+42=42 |
Step 3 — Pick the best
The values are 0,22,36,42; the maximum is 42, at (0,6). Check (0,6): 2(0)+6=6≤6 and 0≤2 — feasible.
The maximum value is Z=42, attained at (0,6).
Method: Corner-Point Method when One Constraint Cuts Off a Vertex
Use this for a bounded maximisation where a simple bound (like x≤k) trims the region, so some "obvious" intersection points are actually infeasible.
Steps
Step 1: Plot all boundaries, including the cutting bound.
Draw each constraint line and the non-negativity axes. A vertical/horizontal bound such as x≤k or y≤k slices across a sloping line.
Step 2: Find candidate intersections — then keep only feasible ones.
Solve each relevant pair of lines. Crucially, an axis-intercept of a sloping line (e.g. where 2x+y=6 meets the x-axis) may lie outside the region because it violates the cutting bound. Discard any intersection that breaks any constraint.
Step 3: Evaluate Z=ax+by at the surviving corners.
Tabulate Z at each genuine vertex of the trimmed polygon and select the optimum.
The most common slip here is using the full line's intercept as a corner. Always re-check each candidate against every constraint before treating it as a vertex — the cutting bound is exactly what makes some intercepts invalid.
Common Mistakes
Mistake 1: Using (3,0) as a corner.
Why it's wrong: the line 2x+y=6 meets the x-axis at (3,0), but x=3 violates x≤2, so (3,0) is outside the feasible region. Correct approach: the bound x≤2 cuts the region — the true corner on that edge is (2,2), from x=2 in 2x+y=6.
Mistake 2: Not testing every candidate against all constraints.
Why it's wrong: an intersection of two lines can still break a third constraint and so not be a real vertex. Correct approach: check each candidate corner against 2x+y≤6, x≤2, x≥0, y≥0 before evaluating Z=11x+7y.
- GUJCET 2025Set 031 markMCQQ.The maximum value of z=5x+3y subject to constraints 3x+5y≤15, x≥0, y≥0 is : (A) 10 (B) 25 (C) 0 (D) 9
›Reveal solutionSolution
Feasible region is the triangle bounded by 3x+5y≤15, x,y≥0.
Corner points: (0,0), (5,0), (0,3).
- (0,0)→0
- (5,0)→25
- (0,3)→9
Maximum is 25 at (5,0).
✓Final answer(B) 25
ANSWER: (B)
- GUJCET 2021Set 151 markMCQQ.Minimise : Z=2x+3y, subject to constraints 2x+4y≤12, x+y≤3, x≥0 and y≥0. (A) 12 (B) 9 (C) 0 (D) 6
›Reveal solutionSolution
With all constraints of <= type and x,y>=0, the origin is feasible and minimises Z = 2x+3y.
Concept. For Z=2x+3y with x,y≥0, the smallest value comes from the corner nearest the origin.
Solution. (0,0) satisfies 2x+4y≤12 and x+y≤3, giving Z=0, which is the minimum.
✓Final answer(C) 0
ANSWER: (C)
- GUJCET 2020Set 071 markMCQQ.The maximum value of Z=3x+4y subject to constraints x+y≤4, x≥0, y≥0 is ________. (A) 16 (B) 12 (C) 0 (D) not possible
›Reveal solutionSolution
Evaluate Z at the corners of the feasible region; the max is at (0,4).
Concept: Feasible region: x+y≤4, x≥0, y≥0 — a triangle with corners (0,0),(4,0),(0,4).
Z(0,0)=0,Z(4,0)=12,Z(0,4)=16.
Maximum Z=16 at (0,4).
✓Final answer(A) 16
ANSWER: (A)
- GUJCET 2023Set 091 markMCQQ.Minimise objective function z=3x+2y subject to the constraints : x+y≥8, x+y≤5, x≥0, y≥0 is : (A) 15 (B) 6 (C) 24 (D) No feasible region and hence no feasible solution
›Reveal solutionSolution
Incompatible constraints leave no feasible region.
Concept: The constraints require x+y≥8 and simultaneously x+y≤5. No point can satisfy x+y≥8 and x+y≤5 at once, so the feasible region is empty and there is no feasible solution.
✓Final answer(D) No feasible region and hence no feasible solution
ANSWER: (D)
- GUJCET 2024Set 131 markMCQQ.Minimise objective function z=7x+3y subject to the constraints : x+y≤5,x+y≥10,x≥0,y≥0 is : (A) No feasible region and hence no feasible solution (B) 15 (C) 70 (D) 35
›Reveal solutionSolution
The constraints x+y≤5 and x+y≥10 are contradictory.
Reasoning. No point can satisfy x+y≤5 and x+y≥10 simultaneously (a sum cannot be both ≤5 and ≥10). Hence the feasible region is empty and the LPP has no feasible solution.
✓Final answerOption (A) No feasible region and hence no feasible solution
ANSWER: (A)
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