Q.A firm has to transport 1200 packages using large vans which can carry 200 packages each and small vans which can take 80 packages each. The cost for engaging each large van is Rs 400 and each small van is Rs 200. Not more than Rs 3000 is to be spent on the job and the number of large vans cannot exceed the number of small vans. Formulate this problem as a LPP given that the objective is to minimise cost.
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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 …
Concept: Optimization Word Problem (Linear Programming Problem)
Let x = number of large vans, y = number of small vans.
Step 1 – Objective function
Minimise cost: Z=400x+200y
Step 2 – Constraints
- Package capacity: 200x+80y≥1200 (at least 1200 packages)
- Budget: 400x+200y≤3000
- Large vans ≤ small vans: x≤y …
This is a linear programming problem where we minimise cost under constraints on capacity, budget, and van count. The LPP formulation is: minimise Z=400x+200y subject to 200x+80y≥1200, 400x+200y≤3000, x≤y, x≥0, y≥0, with x and y integers.
The core of any optimisation word problem is translating real-world conditions into mathematical relationships. Here, we have two types of vans — large and small — each with a carrying capacity and a cost. The goal is to move exactly 1200 packages at minimum cost, but we are limited by a budget of Rs 3000 and a rule that the number of large vans cannot exceed the number of small vans.
Let’s break it down step by step.
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Define the decision variables
Let x = number of large vans used, and y = number of small vans used.
These are the quantities we can choose. Since we cannot hire a fraction of a van, x and y are non-negative integers — but in a standard LPP formulation, we first write them as ≥0 and note the integer condition separately if needed.
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Objective function: minimise cost
Each large van costs Rs 400, each small van costs Rs 200.
Total cost Z=400x+200y.
We want to minimise Z.
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Constraint 1: Capacity
Large van carries 200 packages, small van carries 80.
Total packages carried = 200x+80y.
This must be at least 1200 (we can carry more, but not less):
200x+80y≥1200
- Constraint 2: Budget Total cost cannot exceed Rs 3000:
400x+200y≤3000
- Constraint 3: Large vans ≤ small vans The number of large vans cannot exceed the number of small vans:
x≤y
- Non-negativity constraints You cannot hire a negative number of vans: x≥0,y≥0 …
Method: Formulating a Cost-Minimisation LPP
Use this to set up a word problem whose goal is to minimise a cost subject to a requirement to be met, a budget cap, and a comparison condition.
Steps
Step 1: Define the decision variables.
Let x and y be the counts of the two options chosen (e.g. two van types). Note they are non-negative (and often whole numbers).
Step 2: Translate each condition into an inequality — mind the direction.
- A quantity that must be met or exceeded (packages to transport) gives ≥: (cap. of x)x+(cap. of y)y≥required.
- A cap (budget) gives ≤: (cost x)x+(cost y)y≤budget. …
Common Mistakes
Mistake 1: Writing the capacity condition as ≤ or =.
Why it's wrong: the firm must move at least 1200 packages, so 200x+80y≥1200; using ≤ or = wrongly caps or fixes the load. Correct approach: a minimum requirement is a ≥ constraint, a budget cap is ≤ (400x+200y≤3000).
Mistake 2: Reversing the "large ≤ small" condition. …
- AHSEC Higher Secondary (HS) Final Examination 2026Set ANNUAL6 marksQ.Solve the following linear programming problem graphically : Minimize Z=10(x−7y+190) subject to the constraints x+y≤8, x≤5, y≤5, x+y≥4, x≥0, y≥0.
›Reveal solutionSolution
Main: min Z=1550 at (0,5). OR: Z=−50x+20y is unbounded below on the feasible region, so no minimum exists.
Main part. Constraints: x+y≤8, x≤5, y≤5, x+y≥4, x,y≥0. This is a bounded region with corner points (0,4),(0,5),(3,5),(5,3),(5,0),(4,0). Evaluate Z=10(x−7y+190):
- (0,4): 10(0−28+190)=1620
- (0,5): 10(0−35+190)=1550
- (3,5): 10(3−35+190)=1580
- (5,3): 10(5−21+190)=1740
- (5,0): 10(5−0+190)=1950
- (4,0): 10(4−0+190)=1940
The minimum is Z=1550 at (0,5).
…
- AHSEC Higher Secondary (HS) Final Examination 2025Set ANNUAL6 marksQ.Solve graphically the following linear programming problem: Maximize and minimize Z=3x+5y subject to the constraints 2x+3y≤36, x+y≤15, y≥3, x≥0. OR Determine graphically the minimum value of the objective function Z=3x+9y subject to the constraints x+3y≤60, x+y≥10, x≤y, x≥0, y≥0.
›Reveal solutionSolution
Main: plot the constraints, find corner points of the feasible region, evaluate Z at each — max and min occur at corner points (corner-point theorem). OR: same method for a different feasible region and objective.
Main: Maximize/minimize Z=3x+5y subject to 2x+3y≤36, x+y≤15, y≥3, x≥0 (with y≥0 implicit).
Find the corner points of the feasible region by intersecting boundary lines:
- x=0 and y=3: (0,3)
- x=0 and 2x+3y=36: y=12, point (0,12)
- y=3 and x+y=15: x=12, point (12,3) [note: y=3 meeting 2x+3y=36 gives x=13.5, but that violates x+y≤15, so it is not a feasible vertex]
- x+y=15 and 2x+3y=36: substituting x=15−y gives 2(15−y)+3y=36⟹y=6,x=9, point (9,6)
Feasible region vertices: (0,3),(0,12),(9,6),(12,3).
Evaluate Z=3x+5y at each:
- (0,3): Z=15
- (0,12): Z=60
- (9,6): Z=27+30=57
- (12,3): Z=36+15=51
By the corner-point theorem, the maximum and minimum of Z over the feasible region occur at vertices: maximum Z=60 at (0,12), minimum Z=15 at (0,3).
OR: Minimize Z=3x+9y subject to x+3y≤60, x+y≥10, x≤y, x≥0,y≥0.
Corner points of the feasible region:
- x+y=10 and x=y: x=y=5, point (5,5)
- x+y=10 and x=0: point (0,10)
- x+3y=60 and x=0: point (0,20) …
- AHSEC Higher Secondary (HS) Final Examination 2024Set ANNUAL6 marksQ.Solve graphically the following linear programming problem: Maximize and minimize Z=x+2y subject to x+2y≥100, 2x−y≤0, 2x+y≤200, x,y≥0. OR A merchant plans to sell two types of personal computers—a desktop model and a portable model that will cost Rs. 25,000 and Rs. 40,000 respectively. He estimates that the total monthly demand of computers will not exceed 250 units. Determine the number of units of each type of computers which the merchant should stock to get maximum profit if he does not want to invest more than Rs. 70 lakhs and if his profit on the desktop model is Rs. 4,500 and on portable model is Rs. 5,000.
›Reveal solutionSolution
Graph the constraints, find the feasible-region corner points, and evaluate the objective at each; the OR part is a max-profit LPP solved the same way.
Main question: Constraints: x+2y≥100, 2x−y≤0 (i.e. y≥2x), 2x+y≤200, x,y≥0.
Find the corner points of the feasible region by pairwise intersection of the boundary lines (keeping only points satisfying all constraints):
- x+2y=100 meets the y-axis (x=0) at (0,50) — check: y≥2x (50≥0 ✓), 2x+y=50≤200 ✓.
- x+2y=100 meets y=2x: substituting, x+4x=100⇒x=20,y=40, point (20,40).
- y=2x meets 2x+y=200: 2x+2x=200⇒x=50,y=100, point (50,100).
- 2x+y=200 meets the y-axis at (0,200) — check: x+2y=400≥100 ✓, y≥2x (200≥0 ✓).
(The point (100,0), where x+2y=100 meets the x-axis, is rejected since it violates y≥2x.)
So the feasible region is the quadrilateral with vertices (0,50),(20,40),(50,100),(0,200).
Evaluate Z=x+2y:
Point Z=x+2y (0,50) 100 (20,40) 100 (50,100) 250 (0,200) 400 Since (0,50) and (20,40) both lie on the line x+2y=100 and give the same Z=100, the minimum Z=100 is attained at every point of the segment joining them (multiple optimal solutions). The maximum Z=400 occurs uniquely at (0,200).
OR: Let x = number of desktops, y = number of portables.
Budget: 25000x+40000y≤70,00,000⇒5x+8y≤1400 (dividing by 5000).
Demand: x+y≤250. Also x,y≥0.
Maximize profit P=4500x+5000y.
Corner points:
- (0,0)
- (250,0) (demand line meets x-axis; budget 5(250)=1250≤1400, feasible) …
- AHSEC Higher Secondary (HS) Final Examination 2023Set ANNUAL6 marksQ.Solve graphically the following linear programming problem. Maximize and minimize Z=−x+2y subject to the constraints x≥2, x+y≥5, x+2y≥6, y≥0. OR A manufacturer makes two types of toys A and B. Three machines are needed for this purpose and the time (in minutes) required for each toy on the machines is given below: Machine I / II / III — Toy A: 12, 18, 6; Toy B: 6, 0, 9. Each machine is available for a maximum of 6 hours per day. If the profit on each toy of type A is Rs. 7.50 and that on each toy of type B is Rs. 5, show that 15 toys of type A and 30 toys of type B should be manufactured in a day to get maximum profit.
›Reveal solutionSolution
Plot the corner points of the unbounded feasible region and test, via the half-plane method, whether Z is bounded in either direction — here it is unbounded both ways.
Constraints: x≥2, x+y≥5, x+2y≥6, y≥0. Since all inequalities are "≥" (plus y≥0), the feasible region lies above/right of these boundary lines and is unbounded.
Corner points (intersections of the boundary lines, checked for feasibility):
- x=2 and x+y=5: gives (2,3). (Check x+2y=2+6=8≥6 ✓.)
- x+y=5 and x+2y=6: subtracting gives y=1, x=4, i.e. (4,1). (Check x≥2 ✓.)
- x+2y=6 and y=0: gives (6,0). (Check x+y=6≥5 ✓, x≥2 ✓.)
The region is bounded by these three segments but extends unboundedly: upward along x=2 (for y≥3) and rightward along y=0 (for x≥6).
Evaluate Z=−x+2y at the corners:
Z(2,3)=−2+6=4,Z(4,1)=−4+2=−2,Z(6,0)=−6+0=−6.
Testing for a maximum: Consider the open half-plane −x+2y>4. The point (2,100) lies in the feasible region (satisfies x≥2, x+y=102≥5, x+2y=202≥6, y≥0) and gives Z=−2+200=198>4. Since this half-plane intersects the feasible region, Z can be made arbitrarily large along x=2 as y→∞ — so Z has no maximum value.
Testing for a minimum: Consider the open half-plane −x+2y<−6. The point (100,0) is feasible (satisfies all constraints) and gives Z=−100<−6. Since this half-plane also intersects the feasible region, Z can be made arbitrarily small (large negative) along y=0 as x→∞ — so Z has no minimum value either.
Hence, over this unbounded feasible region, Z=−x+2y is unbounded in both directions: neither a maximum nor a minimum exists.
OR: Toys A,B; machine-minute constraints (converting 6 hours =360 minutes each): …
- AHSEC Higher Secondary (HS) Final Examination 2022Set ANNUAL6 marksQ.Minimize Z=3x+5y subject to x+3y≥3, x+y≥2, x,y≥0. OR Minimise and Maximise Z=5x+10y subject to x+2y≤120, x+y≥60, x−2y≥0, x,y≥0.
›Reveal solutionSolution
Evaluating Z=3x+5y at the feasible region's corner points gives the minimum 7 at (3/2,1/2). (OR: the classic two-constraint LPP has minimum 300 at (60,0) and maximum 600 along the whole edge from (120,0) to (60,30).)
Minimize Z=3x+5y subject to x+3y≥3, x+y≥2, x,y≥0
Find the corner points of the feasible region (intersection of the boundary lines with each other and the axes, keeping only feasible points):
- On y=0: need x≥3 (from x+3y≥3) and x≥2 (from x+y≥2) — the binding one is x=3, giving corner (3,0).
- On x=0: need y≥1 and y≥2 — binding is y=2, giving corner (0,2).
- Intersection of x+3y=3 and x+y=2: subtracting, 2y=1⇒y=21, then x=23 — corner (23,21).
The feasible region is unbounded, with corners (3,0), (23,21), (0,2) (and extending outward).
Evaluate Z=3x+5y:
Z(3,0)=9,Z(23,21)=29+25=7,Z(0,2)=10.
Since both coefficients of Z are positive and the region extends only outward (away from the origin), Z cannot go below the smallest corner value; the open half-plane 3x+5y<7 has no point in common with the feasible region. So the minimum is Z=7 at (23,21).
OR: Minimise and Maximise Z=5x+10y subject to x+2y≤120, x+y≥60, x−2y≥0, x,y≥0
Finding all feasible corner points (checking each pairwise intersection against all constraints):
- x+2y=120 and x=2y: y=30,x=60 — (60,30), feasible.
- x+2y=120 and y=0: (120,0), feasible.
- x+y=60 and x=2y: y=20,x=40 — (40,20), feasible. …
- AHSEC Higher Secondary (HS) Final Examination 2020Set ANNUAL6 marksQ.Solve graphically the following linear programming problem: Maximize or Minimize Z=x+2y subject to constraints x+2y≥100, 2x−y≤0, 2x+y≤200, x≥0, y≥0. OR Maximize Z=1000x+600y subject to constraints x+y≤200, x≥20, y≥4x, x≥0, y≥0.
›Reveal solutionSolution
Plot the feasible region from the constraints, find its corner points, then evaluate the objective function at each corner.
Max/Min Z=x+2y s.t. x+2y≥100, 2x−y≤0, 2x+y≤200, x,y≥0
Rewrite 2x−y≤0 as y≥2x.
Corner points (solving pairs of boundary lines and checking they satisfy all constraints):
- x+2y=100 and y=2x: x+4x=100⇒x=20,y=40 → (20,40)
- y=2x and 2x+y=200: 2x+2x=200⇒x=50,y=100 → (50,100)
- x=0 with x+2y=100: y=50 → (0,50) (since at x=0, need y≥50 from constraint 1)
- x=0 with 2x+y=200: y=200 → (0,200) (upper bound at x=0)
These four points (0,50),(20,40),(50,100),(0,200) form the (bounded) feasible region.
Evaluate Z=x+2y:
- (0,50): Z=100
- (20,40): Z=100
- (50,100): Z=250
- (0,200): Z=400
Minimum Z=100 (attained all along the edge joining (0,50) and (20,40), since that edge lies exactly on x+2y=100). Maximum Z=400 at (0,200).
OR: Maximize Z=1000x+600y s.t. x+y≤200, x≥20, y≥4x, x,y≥0
…
- AHSEC Higher Secondary (HS) Final Examination 2019Set ANNUAL6 marksQ.Solve the linear programming problem graphically. Maximize z=20x+15y, subject to the conditions 2x+y≤200, x+y≤150 and x≥0, y≥0. OR Maximize and minimize z=5x+2y, subject to the conditions x−2y≤2, 3x+2y≤12, −3x+2y≤3 and x≥0, y≥0.
›Reveal solutionSolution
In each LPP, plot the constraint lines, find the vertices of the feasible region, and evaluate the objective at each vertex — the optimum occurs at a vertex (corner-point method).
Main question. Maximize z=20x+15y subject to 2x+y≤200, x+y≤150, x,y≥0.
Find the corner points of the feasible region.
- Intersection of 2x+y=200 and x+y=150: subtracting, x=50, so y=100. Point (50,100).
- y=0: 2x+y=200⇒x=100 (binding, since x+y≤150 allows x up to 150, so 200-line is tighter) — vertex (100,0).
- x=0: x+y=150⇒y=150 (binding, since 2x+y≤200 allows y up to 200) — vertex (0,150).
- Origin (0,0).
Evaluate z=20x+15y:
Vertex z (0,0) 0 (100,0) 2000 (50,100) 1000+1500=2500 (0,150) 2250 Maximum is z=2500 at (50,100).
OR question. Maximize and minimize z=5x+2y subject to x−2y≤2, 3x+2y≤12, −3x+2y≤3, x,y≥0.
Find the vertices.
- x=0: constraints give y≤6 (from 3x+2y≤12) and y≤1.5 (from −3x+2y≤3); tightest is y≤1.5 — vertex (0,1.5); also (0,0).
- y=0: constraints give x≤2 (from x−2y≤2) and x≤4 (from 3x+2y≤12); tightest is x≤2 — vertex (2,0).
- Intersection of x−2y=2 and 3x+2y=12: adding, 4x=14⇒x=3.5, y=2x−2=0.75. Vertex (3.5, 0.75) — check −3(3.5)+2(0.75)=−9≤3 ✓ feasible. …
- AHSEC Higher Secondary (HS) Final Examination 2018Set ANNUAL6 marksQ.Solve the Linear Programming Problem graphically: Maximize and Minimize z=6x+3y subject to 4x+y≥80, x+5y≥115, 3x+2y≤150, x≥0,y≥0.
›Reveal solutionSolution
The feasible region is a triangle with corners (2,72),(15,20),(40,15); evaluating z=6x+3y gives min 150 and max 285.
Maximize/minimize z=6x+3y subject to 4x+y≥80, x+5y≥115, 3x+2y≤150, x,y≥0.
Find the corner points by intersecting the boundary lines:
- 4x+y=80 and x+5y=115: solving gives (15,20).
- 4x+y=80 and 3x+2y=150: solving gives (2,72).
- x+5y=115 and 3x+2y=150: solving gives (40,15).
Each of these satisfies all constraints, so the feasible region is the triangle with vertices (2,72), (15,20), (40,15).
Evaluate z=6x+3y at each corner:
- (2,72):z=12+216=228
- (15,20):z=90+60=150
- (40,15):z=240+45=285
So the minimum is 150 at (15,20) and the maximum is 285 at (40,15).
…
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