Question

What are the essential characteristics of a linear programming model?

Answer 1

All linear programming problems must have following five characteristics:

(a) Objective function:

(b) Constraints:

(c) Non-negativity:

(d) Linearity:

(e) Finiteness:

Answer 2

Answer:

Linear programming may be used to find the best solution to a given issue with certain restrictions. A real-life situation is transformed into a mathematical model here. It is made up of an objective function, linear inequalities, and constraints.

The following are the linear programming components:

• Decision variables indicate quantities that must be determined.
• The objective function depicts how the choice factors will affect the cost or value that must be optimized.
• Constraints describe how the choice variables use limited resources.
• The relationships indicated by the constraints and the goal function are quantified by data.

The following are the drawbacks of linear programming:

• It only happens when the variables are linear.
• It does not take into account variable changes.
• It is unable to solve the nonlinear function.
• In the graphical technique, it is impossible to answer a problem with more than two variables.

Linear programming has the following characteristics: objective function, constraints, non-negativity, linearity, and finiteness.

(a) Primary function:

There must be a clearly defined objective that can be expressed quantitatively. In most business situations, the goal is to maximize profit or minimize costs.

(b) Constraints:

All resource restrictions (limitations) should be properly laid out in mathematical language.

(c) Lack of negativity:

Variables must be either zero or positive and not negative. In the event of manufacturing, for example, the management can decide on any particular product number in positive or minimal zero, not negative.

(c) Linearity:

The variables' relationships must be linear. Linear denotes a proportionate connection between two or more variables, implying that the number of variables should be as few as possible.

(e) Finiteness:

The number of inputs and outputs must be restricted. It is impossible to compute a plausible solution when there are infinite factors.

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