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@@ -43,7 +43,7 @@ Constrained and unconstrained optimization. Linear Programming: formulation, geo
 Optimization and simulation are two key areas in Operations Research, used for problem-solving and decision-making, but they approach these tasks differently[4][5]. Simulation creates a virtual model to analyze a system's behavior under various conditions, allowing for experimentation by varying parameters[1]. Optimization, on the other hand, employs mathematical algorithms to identify the best configuration of these parameters, aiming to maximize or minimize a specific objective, such as reducing costs or increasing efficiency.
 
 **[Sim-Opt]() (Simulation-Optimization)**
-Sim-opt combines simulation and optimization techniques to provide a comprehensive and dynamic understanding of a system[1]. This approach integrates real-world uncertainties with the search for ideal solutions[1]. By simulating the impact of each parameter and comparing it to an ideal scenario, sim-opt helps identify the factors that most influence a system's performance, leading to more strategic decisions.
+Sim-opt combines simulation and optimization techniques to provide a comprehensive and dynamic understanding of a system. This approach integrates real-world uncertainties with the search for ideal solutions[1]. By simulating the impact of each parameter and comparing it to an ideal scenario, sim-opt helps identify the factors that most influence a system's performance, leading to more strategic decisions.
 
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@@ -79,7 +79,7 @@ Sim-opt combines simulation and optimization techniques to provide a comprehensi
 
 The [key difference]() between sim-opt and other analytical tools is its [ability to model the complexity and dynamics of real-world systems](), including data uncertainty and variability. This allows for the creation of more robust and adaptable plans.
 
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 # I- Example of a [Optimization Problem]()