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.
- JKBOSE Class 12 Annual Regular Examination 2022Set SZ2 marksQ.Solve the following L.P.P. graphically : Maximise : Z=3x+4y. Subject to constraints : x+y≤4, x≥0, y≥0.
›Reveal solutionSolution
The feasible region is a triangle with corners (0,0),(4,0),(0,4); evaluating Z=3x+4y at each shows the maximum is 16 at (0,4).
Constraints: x+y≤4, x≥0, y≥0.
This describes the triangular region in the first quadrant bounded by the line x+y=4 and the axes, with corner (vertex) points:
(0,0),(4,0),(0,4).
By the Fundamental Theorem of Linear Programming, the maximum of a linear objective function over a bounded feasible region occurs at a corner point. Evaluate Z=3x+4y at each:
Corner Z=3x+4y (0,0) 0 (4,0) 12 (0,4) 16 The largest value is 16, at (0,4).
✓Final answerMaximum value of Z is 16, attained at (x,y)=(0,4).
- JKBOSE Class 12 Annual Regular Examination 2020Set SZ2 marksQ.Shade the feasible region of L.P.P. x+3y≥3, x+y≥2, x,y≥0.
›Reveal solutionSolution
Plot both boundary lines, test the origin to pick the correct side, and shade the intersection with the first quadrant.
This question asks for a hand-drawn shaded graph, which cannot be rendered here — the region is described numerically instead.
Constraints: x+3y≥3, x+y≥2, x,y≥0.
x+3y=3 passes through (3,0) and (0,1). x+y=2 passes through (2,0) and (0,2).
Testing the origin (0,0) in each: 0≥3 is false and 0≥2 is false, so the origin is NOT in the feasible region — shade the side of each line away from the origin.
Intersection of the two lines: from x+y=2, x=2−y; substitute into x+3y=3: (2−y)+3y=3⇒2y=1⇒y=21, x=23.
The feasible region is unbounded, lying above/right of both lines in the first quadrant, with vertices (0,2) (on the y-axis, where x+y=2 dominates), (23,21) (intersection of the two lines), and (3,0) (on the x-axis, where x+3y=3 dominates) — extending outward to infinity beyond (0,2) along the y-axis side and beyond (3,0) along the x-axis side.
✓Final answerFeasible region: unbounded, above/right of both lines, first quadrant, with corner points (0,2), (23,21), (3,0).
- JKBOSE Class 12 Annual Regular Examination 2018Set WZ2 marksQ.Minimize Z=3x+2y subject to the constraints x+y≥8, x,y≥0.
›Reveal solutionSolution
The feasible region's corners on the boundary line x+y=8 are (8,0) and (0,8); comparing Z=3x+2y at each (and checking the unbounded direction) gives the minimum at (0,8).
Minimize Z=3x+2y subject to x+y≥8, x,y≥0.
Corner points: the boundary line x+y=8 meets the axes at (8,0) and (0,8); the feasible region is everything on or above this line in the first quadrant (unbounded).
Z(8,0)=24,Z(0,8)=16
Parametrizing the boundary as x=t, y=8−t for t∈[0,8]: Z=3t+2(8−t)=t+16, which is smallest at t=0, i.e. at (0,8), giving Z=16.
Moving further into the interior of the feasible region (away from the boundary line, e.g. increasing x or y beyond the line) only increases Z since both coefficients 3 and 2 are positive. So the minimum occurs on the boundary at (0,8).
✓Final answerMinimum value of Z=3x+2y is 16, attained at x=0, y=8.
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