Q.Case Study 2: The month of September is celebrated as the Rashtriya Poshan Maah across the country. Following a healthy and well-balanced diet is crucial in order to supply the body with the proper nutrients it needs. A balanced diet also keeps us mentally fit and promotes improved level of energy. A dietician wishes to minimize the cost of a diet involving two types of foods, food ( kg) and food ( kg) which are available at the rate of /kg and /kg respectively. The feasible region satisfying the constraints is shown in Figure-2. [The feasible region (unbounded) is bounded by the corner points , , , , formed by the lines , , and , with , .] On the basis of the above information, answer the following questions:
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Start your 14-day free trial to unlock the full solution →The feasible region is defined by the constraints , , , , , . The minimum cost of is achieved when kg of food X and kg of food Y are used.
The problem asks us to work with a Linear Programming Problem (LPP) in the context of diet planning. We need to identify the constraints that define the feasible region and then find the minimum cost using the given objective function. This is a classic application of the graphical method for solving LPPs.
Concept: Linear Programming Graphical Method
Linear Programming involves optimizing (maximizing or minimizing) a linear objective function subject to a set of linear inequalities called constraints. The graphical method is suitable for problems with two decision variables (here, and ).
- Formulate the LPP: Define the objective function and all constraints.
- Graph the Constraints: Each inequality represents a half-plane. To graph an inequality, first plot the corresponding equality (a straight line). Then, choose a test point (like the origin if it's not on the line) to determine which side of the line satisfies the inequality.
- Identify the Feasible Region: This is the region in the graph that satisfies all the constraints simultaneously. It's the intersection of all the half-planes. For diet problems, and are always included, restricting the region to the first quadrant.
- Find Corner Points: The vertices (corner points) of the feasible region are the points where the boundary lines intersect.
- Evaluate Objective Function: Calculate the value of the objective function at each corner point.
- Determine Optimal Solution:
- For maximization, the maximum value of the objective function among the corner points is the optimal solution.
- For minimization, the minimum value of the objective function among the corner points is the optimal solution.
- Unbounded Regions: If the feasible region is unbounded, especially for minimization problems, an additional check is required. Even if a minimum value is found at a corner point, it's possible that the objective function can take even smaller values within the unbounded region. To verify, draw the line (where is the minimum value found). If the open half-plane has no point in common with the feasible region, then is indeed the minimum value. If there are common points, then no minimum exists. The problem statement here explicitly asks us to assume that a minimum cost is possible.
Part (i): Identify and write all the constraints
The problem provides the lines that form the boundary of the feasible region and lists its corner points. Since this is a minimization problem (minimizing cost), the feasible region will typically be an unbounded region extending away from the origin, meaning the inequalities will be of the "greater than or equal to" type.
The given lines are:
The corner points of the feasible region are , , , .
We also have the non-negativity constraints and , which are standard for quantities like food in kg.
To determine the direction of the inequalities, we can pick a test point that is clearly within the feasible region (e.g., a point far from the origin like ) and substitute its coordinates into the equations of the lines. The inequality that holds true for this point will be the correct constraint.
Let's use the test point :
- For : . Since , the constraint is .
- For : . Since , the constraint is .
- For : . Since , the constraint is .
- For : . Since , the constraint is .
Combining these with the non-negativity constraints, we get all the constraints.
The constraints which determine the given feasible region are:
Part (ii): Find the values of and at which cost is minimum, and the minimum cost
The objective function to minimize is the cost . …
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