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.
- Higher Secondary (+2 Stage) Examination 2025Set ANNUAL5 marksQ.Solve the following linear programming problem graphically: Find the maximum and minimum value of Z, where Z=x+2y, subject to the constraints: x+2y≥100, 2x−y≤0, 2x+y≤200, x≥0, y≥0.
›Reveal solutionSolution
Plot the feasible region from the four constraints, find its corner points, then evaluate Z=x+2y at each corner — the maximum/minimum of a linear objective over a bounded polygon always occurs at a corner.
Constraints: x+2y≥100, 2x−y≤0 (i.e. y≥2x), 2x+y≤200, x≥0, y≥0.
Finding the corner points by intersecting the boundary lines in pairs:
- x=0 with x+2y=100: gives (0,50).
- x+2y=100 with y=2x: substituting, x+4x=100⇒x=20,y=40: gives (20,40).
- y=2x with 2x+y=200: substituting, 2x+2x=200⇒x=50,y=100: gives (50,100).
- 2x+y=200 with x=0: gives (0,200).
(One can check each of these four points satisfies all the constraints simultaneously, and that they are the actual vertices of the bounded feasible region — a quadrilateral.)
Evaluating Z=x+2y at each corner:
Corner Z=x+2y (0,50) 0+100=100 (20,40) 20+80=100 (50,100) 50+200=250 (0,200) 0+400=400 The maximum value is Z=400 at (0,200).
The minimum value is Z=100, and it occurs at both (0,50) and (20,40) — because these two corners both lie on the line x+2y=100 (one of the boundary constraints), Z is constant and equal to 100 along the entire edge joining them, giving infinitely many optimal solutions on that segment.
✓Final answerMaximum Z=400 at (0,200); Minimum Z=100, attained at every point of the line segment joining (0,50) and (20,40).
- Higher Secondary (+2 Stage) Examination 2023Set ANNUAL5 marksQ.A businessman plans to sell two types of special computers: a desktop model and a portable model, whose current prices are Rs 25,000 and Rs 40,000 respectively. He estimates that the total monthly demand for computers will not exceed 250 units. If the profit is Rs 4,500 on each desktop model and Rs 5,000 on each portable model, and he does not want to invest more than Rs 70,00,000 (seventy lakh rupees) in total, determine, by the graphical method, how many units of each type of computer the businessman should stock to obtain the maximum profit. What is the maximum profit?
›Reveal solutionSolution
This is a linear programming problem: set up the demand and investment constraints, plot the feasible region, and evaluate the profit at each corner point — the maximum occurs at (200,50) giving a profit of Rs 11,50,000.
Let x = number of desktop models, y = number of portable models to stock.
Objective (maximize profit): Z=4500x+5000y
Constraints:
- Demand: x+y≤250
- Investment: 25000x+40000y≤70,00,000, which simplifies (dividing by 5000) to 5x+8y≤1400
- Non-negativity: x≥0, y≥0
Corner points of the feasible region:
- (0,0)
- x-intercept of demand line: (250,0) — check investment: 5(250)=1250≤1400 ✓, so this is a genuine corner.
- y-intercept of investment line: 8y=1400⇒y=175, giving (0,175) — check demand: 175≤250 ✓ (this is the binding constraint here, since the demand line would allow y=250 but investment caps it at 175).
- Intersection of x+y=250 and 5x+8y=1400: from the first, x=250−y; substitute: 5(250−y)+8y=1400⇒1250+3y=1400⇒y=50, x=200. Point: (200,50).
Evaluate Z=4500x+5000y at each corner:
Point Z (0,0) 0 (250,0) 4500(250)=11,25,000 (200,50) 4500(200)+5000(50)=9,00,000+2,50,000=11,50,000 (0,175) 5000(175)=8,75,000 The maximum value of Z is Rs 11,50,000, attained at (x,y)=(200,50).
✓Final answerThe businessman should stock 200 desktop models and 50 portable models, giving a maximum profit of Rs 11,50,000.
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