input: def solve_linear_programming_using_simplex(problem_description: str) -> str:
      """
      Explain the approach for solving a linear programming problem using the Simplex method in Python based on the given problem description.
      The Simplex Method is an algorithm for solving a linear programming problem, which involves finding the maximum or minimum value of a linear function. This method uses simultaneous equations to define a polytope that contains the solution to the linear programming problem. It then finds the optimal solution in this polytope by iteratively making moves from a vertex to another in the polytope that gives an increase in the message value. 
      
      To solve a linear programming problem using the Simplex Method in Python, first, define the problem in terms of the decision variables, objective function, and constraints. Next, convert the problem into standard form by introducing slack variables, if needed. Then, create a tableau containing the coefficients of the decision variables and the right-hand sides of the constraints, and set up the starting solution in the tableau. Now, iteratively use the Simplex algorithm to move from a vertex to the next until an optimum solution is reached. Determine the optimum solution by inspecting the values in the tableau.
      
      Example:
      for solve_linear_programming_using_simplex("find the max of 5x1 + 6x2 under constraints x1 + 2x2 <= 5 and 3x1 + x2 <= 10") 
      == "The Simplex Method involves first defining the problem as max(5x1 + 6x2) under constraints x1 + 2x2 <= 5 and 3x1 + x2 <= 10. Convert to standard form by introducing slack variables: maximize Z = 5x1 + 6x2 subject to x1 + 2x2 + s1 = 5, 3x1 + x2 + s2 = 10, where s1, s2 >= 0. Create a tableau with coefficients of variables. Iterate with the Simplex algorithm until optimum point is found in the tableau."
      """
output:     # Extract the objective function and constraints from the problem description
      import re
      match = re.match(r"find the (max|min) of (.*) under constraints (.*)", problem_description)
      if not match:
          return "Invalid problem description format"
      
      objective_type = match.group(1)
      objective_function = match.group(2)
      constraints = match.group(3).split(" and ")
      
      # Convert the problem into standard form by introducing slack variables
      slack_constraints = []
      for constraint in constraints:
          if "<=" in constraint:
              slack_constraints.append(constraint.replace("<=", "+ s") + " = " + constraint.split("<=")[1])
          elif ">=" in constraint:
              slack_constraints.append(constraint.replace(">=", "- s") + " = " + constraint.split(">=")[1])
          else:
              return "Constraints must be in the form of <= or >="
      
      # Create the tableau description
      tableau_description = f"The Simplex Method involves first defining the problem as {objective_type}({objective_function}) under constraints {', '.join(constraints)}. Convert to standard form by introducing slack variables: maximize Z = {objective_function} subject to {', '.join(slack_constraints)}, where s1, s2 >= 0. Create a tableau with coefficients of variables. Iterate with the Simplex algorithm until optimum point is found in the tableau."
      
      return tableau_description