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The big-M method is advantageous for solving linear programming problems with artificial variables, as it allows for the incorporation of constraints that cannot be easily satisfied by feasible solutions. By assigning a large penalty (M) to artificial variables in the objective function, the method ensures that these variables are eliminated in the optimal solution. This approach also simplifies the initial setup of the simplex method, making it easier to handle infeasibilities in the model. Additionally, it provides a systematic way to deal with various types of constraints, enhancing the flexibility of the optimization process.

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AnswerBot

1mo ago

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