Q.Solve the following linear programming problem graphically: Minimise Z=200x+500y subject to the constraints: x+2y≥10, 3x+4y≤24, 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.
Minimise Z=200x+500y subject to x+2y≥10, 3x+4y≤24, x,y≥0.
The feasible region is the triangle with corners:
- x+2y=10 ∩ x=0: (0,5)
- 3x+4y=24 ∩ x=0: (0,6)
- x+2y=10 ∩ 3x+4y=24: (4,3)
Evaluate Z: Z(0,5)=2500, Z(0,6)=3000, Z(4,3)=800+1500=2300.
(The axis points (10,0) and (8,0) are not feasible: e.g. (8,0) fails x+2y≥10 since 8<10.)
Minimum value Z=2300, at (4, 3).
The feasible region is a triangle with vertices (0,5),(0,6),(4,3); the minimum of Z=200x+500y is 2300 at (4,3).
Set up
Minimise Z=200x+500y subject to
x+2y≥10,3x+4y≤24,x,y≥0.
The region must lie above x+2y=10 and below 3x+4y=24, in the first quadrant.
Plot the boundary lines
- x+2y=10 passes through (10,0) and (0,5).
- 3x+4y=24 passes through (8,0) and (0,6).
Find the feasible corner points
- On the y-axis (x=0): the two constraints give 2y≥10 (so y≥5) and 4y≤24 (so y≤6). This gives the vertices (0,5) and (0,6).
- Intersection of the two lines: from x+2y=10, multiply by 2: 2x+4y=20. Subtract from 3x+4y=24: x=4, then 2y=6⇒y=3 → (4,3).
- The x-axis gives no feasible point: y=0 needs x≥10 (first constraint) and x≤8 (second) at once, which is impossible. So (8,0) and (10,0) are both outside the region.
Hence the feasible region is the triangle (0,5),(0,6),(4,3).
Evaluate Z at the corners
| Corner | Z=200x+500y |
|---|---|
| (0,5) | 2500 |
| (0,6) | 3000 |
| (4,3) | 800+1500=2300 |
The smallest value is 2300 at (4,3). The region is bounded, so this is the true minimum.
Minimum value Z=2300, at (4, 3).
Method: Corner-Point (Graphical) Method for a Minimum
Use this to minimise a linear objective Z=ax+by of two variables subject to a mix of ≥ and ≤ linear constraints. The mechanics are identical to the maximisation case — only the final selection changes.
Steps
Step 1: Plot each constraint line.
Turn each inequality into an equation and draw it from its intercepts, keeping x≥0, y≥0.
Step 2: Shade each half-plane and take the overlap.
Test the origin in each inequality. Note that a ≥ constraint (e.g. x+2y≥10) typically keeps the side away from the origin, while a ≤ constraint keeps the side containing the origin. The feasible region is where all kept half-planes overlap.
Step 3: Locate the feasible corner points.
Solve intersecting boundary lines pairwise. Crucially, discard any intersection that violates another constraint — for a mixed ≥/≤ system an axis intercept often lies outside the region (e.g. it satisfies one constraint but not the other).
Step 4: Evaluate Z at each valid corner and take the smallest.
Zmin=mincorners(ax+by)
Step 5: Confirm the region is bounded.
If the feasible region is a closed polygon, the smallest corner value is the true minimum. (If it were unbounded, you would additionally have to check whether Z can be driven still lower — see the unbounded-region method.) A bounded region guarantees the corner minimum is genuine.
Common Mistakes
Mistake 1: Treating the axis intercepts (10,0) and (8,0) as feasible corners.
Why it's wrong: (8,0) fails x+2y≥10 since 8<10, and (10,0) fails 3x+4y≤24 since 30>24. Neither lies in the region. Correct approach: check every candidate corner against all constraints before evaluating Z.
Mistake 2: Shading x+2y≥10 toward the origin.
Why it's wrong: the origin gives 0≥10, which is false, so the ≥ constraint keeps the side away from the origin. Shading toward it inverts the whole region. Correct approach: for a ≥ constraint that fails the origin test, keep the far side.
Mistake 3: Miscomputing the intersection (4,3).
Why it's wrong: solving x+2y=10 and 3x+4y=24 needs elimination — double the first to 2x+4y=20, subtract to get x=4, then y=3. A sign slip here changes the minimum. Correct approach: substitute the found point back into both equations to verify.
Mistake 4: Evaluating Z=200x+500y with the coefficients swapped.
Why it's wrong: writing 500x+200y gives the wrong values and can flip which corner is smallest. Correct approach: keep each variable with its own coefficient — Z(4,3)=200(4)+500(3)=2300.
- 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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