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Linear programming is a mathematical technique that is applied to tackle the problem of allocating limited resources among competing activities. This optimization technique assists decision-makers in figuring out how best to allocate resources to meet certain goals, including cost reduction or profit maximization, while abiding by a set of restrictions pertaining to capacity, availability, or requirements of the resources. Finding the optimal resource allocation among competing activities is made possible by linear programming, which formulates the problem with an objective function, decision variables, and linear constraints. This method is frequently used to maximize resource use and raise overall efficiency in a variety of disciplines, including supply chain management, economics, and operations research.
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A mathematical technique used to solve the problem of allocating limited resources among competing activities is Linear Programming (LP). It optimizes a linear objective function subject to linear constraints, effectively determining the best allocation of resources to maximize or minimize a specific outcome.
The mathematical technique used to solve the problem of allocating limited resources among competing activities is called **Linear Programming (LP)**. LP optimizes a linear objective function while adhering to a set of linear constraints that represent the limitations or requirements of the resources. It is widely applied in various fields such as operations research, logistics, finance, and manufacturing to find the most efficient allocation of resources.
One mathematical technique used to solve the problem of allocating limited resources among competing activities is Linear Programming (LP). LP involves formulating a mathematical model that represents the decision variables, objective function, and constraints associated with the allocation problem. The objective function quantifies the goal, often maximization of profit or minimization of costs, while the constraints represent the limitations of the resources. Solutions are typically found using methods like the Simplex algorithm or interior-point methods, allowing decision-makers to identify the optimal allocation of resources that yields the best possible outcome within the given constraints.
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