Linear Programming Problem — NCERT Solutions
CBSE · Class 12 · Applied Mathematics
NCERT Solutions for Linear Programming Problem, CBSE Class 12 Applied Mathematics: 13 textbook questions solved step by step.
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1To maintain his health, a person must fulfill certain minimum daily requirements for several kinds of nutrients. Assuming that there are only three kinds of nutrients — calcium, protein and calories — and the person's diet consists of only two food items I and II, whose price and nutrient contents are shown in the table below:
| Nutrients | Food I (per lb) | Food II (per lb) | Minimum daily requirement |
|---|---|---|---|
| Calcium | 10 | 4 | 20 |
| Protein | 5 | 5 | 20 |
| Calories | 2 | 6 | 13 |
| Price (in Rs.) | 0.60 | 1.00 | |
What combination of two food items will satisfy the daily requirement and entail the least cost? Formulate this problem as a LPP.Show solution
Given:
Let = quantity (in lbs) of Food I purchased per day, and = quantity (in lbs) of Food II purchased per day.
Objective Function (Cost to be minimized):
Constraints:
- Calcium requirement:
- Protein requirement:
- Calorie requirement:
- Non-negativity:
LPP Formulation:
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2Vitamins A and B are found in two different foods F1 and F2. One unit of food F1 contains 2 units of Vitamin A and 3 units of Vitamin B; one unit of food F2 contains 4 units of Vitamin A and 2 units of Vitamin B. One unit of food F1 and F2 cost ₹5 and ₹2.5 respectively. The minimum daily requirements for a person of Vitamin A and B are 40 and 50 units respectively. Find the optimum mixture of food F1 and F2 at minimum cost which meets the daily minimum requirement of Vitamin A and B. Formulate this problem as an LPP.Show solution
Given:
Let = number of units of food F1 consumed per day, and = number of units of food F2 consumed per day.
Objective Function (Cost to be minimized):
Constraints:
- Vitamin A requirement:
- Vitamin B requirement:
- Non-negativity:
LPP Formulation:
(Note: The answer key lists the first constraint as , which appears to be a misprint; the correct constraint derived from the data is .)
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3A brick manufacturer has two depots, A and B with stocks of 30,000 and 20,000 bricks respectively. He receives orders from three builders P, Q and R for 15,000, 20,000 and 15,000 bricks respectively. The cost (in Rs.) of transporting 1,000 bricks to the builders from the depots are given below:
| From \ To | P | Q | R |
|---|---|---|---|
| A | 40 | 20 | 30 |
| B | 20 | 60 | 40 |
How should the manufacturer fulfil the orders so as to keep the cost of transportation minimum? Formulate the above problem as a linear programming problem.Show solution
Given:
Let = number of thousands of bricks transported from depot A to builder P, and = number of thousands of bricks transported from depot A to builder Q.
Since total order of P = 15 (thousand), Q = 20 (thousand), R = 15 (thousand):
- Bricks sent from A to R = (thousands)
- Bricks sent from B to P = (thousands)
- Bricks sent from B to Q = (thousands)
- Bricks sent from B to R = (thousands)
Total Transportation Cost (in Rs.):
Objective Function:
Constraints:
- Stock at A:
- Supply to P:
- Supply to Q:
- Non-negative supply from B to R:
- Non-negativity:
LPP Formulation:
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4A Cooperative Society of farmers has 50 hectares of land to grow two crops X and Y. The profit from crops X and Y per hectare are estimated as ₹10,500 and ₹9,000 respectively. To control weeds, a liquid herbicide has to be used for crops X and Y at the rate of 20 litres and 10 litres per hectare. Further, no more than 800 litres of herbicide should be used in order to protect fish and wildlife using a pond which collects drainage from this land. How much land should be allocated to each crop so as to maximize the total profit of the society? Formulate the above problem as a linear programming problem.Show solution
Given:
Let = number of hectares allocated to crop X, and = number of hectares allocated to crop Y.
Objective Function (Profit to be maximized):
Constraints:
- Total land:
- Herbicide limit: , i.e.,
- Non-negativity:
LPP Formulation:
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5A company has two grades of inspectors, I and II, to undertake quality control inspection. At least 1,500 pieces must be inspected in an 8-hour day. Grade I inspector can check 20 pieces in an hour with an accuracy of 96%. Grade II inspector checks 14 pieces an hour with an accuracy of 92%. Wages of Grade I Inspector are Rs. 5 per hour while those of Grade II Inspector are Rs. 4 per hour. Any error made by an inspector costs Rs. 3 to the company. There are, in all, 10 Grade I inspectors and 15 Grade II inspectors in the company. Find the optimal assignment of inspectors that minimizes the daily inspection cost. Formulate the above problem as a linear programming problem.Show solution
Given:
Let = number of Grade I inspectors assigned, and = number of Grade II inspectors assigned.
Cost Calculation per inspector per 8-hour day:
- Grade I inspector wages per day:
- Grade I pieces checked per day: pieces; error rate = ; errors = ; error cost =
- Total cost per Grade I inspector per day:
- Grade II inspector wages per day:
- Grade II pieces checked per day: pieces; error rate = ; errors = ; error cost =
- Total cost per Grade II inspector per day:
Objective Function (Daily cost to be minimized):
Constraints:
- Minimum pieces inspected:
- Availability of Grade I:
- Availability of Grade II:
- Non-negativity:
LPP Formulation:
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6(i)Solve the following Linear Programming Problem graphically:
Maximize
Subject to the constraints:
,
,
,
and Show solution
Step 1: Convert inequalities to equations and find intercepts.
- Line : → passes through and
- Line : (vertical line)
- Line : → passes through and
Step 2: Find corner points of the feasible region.
- Intersection of and :
- Intersection of and :
- Intersection of and :
Subtracting: →
- Intersection of and : →
- Intersection of and :
Step 3: Evaluate at each corner point.
| Corner Point | |
|---|---|
Step 4: Conclusion.
The maximum value of is at .
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6(ii)Minimize
Subject to the constraints:
,
and Show solution
Step 1: Find the boundary line and feasible region.
Line: passes through and .
The feasible region is above this line (since ) with .
Step 2: Find corner points.
- Intersection of and : →
- Intersection of and : →
The feasible region is unbounded (extends to infinity).
Step 3: Evaluate at corner points.
| Corner Point | |
|---|---|
Step 4: Check for unboundedness.
As (with ), . However, the minimum must occur at a corner point if the feasible region is bounded in the direction of minimization. Since the region is unbounded and can decrease without bound along the -axis, we check the corner point .
At , .
Conclusion:
The minimum value of is at .
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Subject to the constraints:
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Subject to the constraints:
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Subject to the constraints:
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Minimize
Subject to the constraints:
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Subject to the constraints:
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Subject to the constraints:
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