input: 
  def linear_programming_using_simplex(decision_variables, objective_function, constraints):
      """
      Explains the approach for solving a linear programming problem using the Simplex Method in Python.
  
      The Simplex Method is used to find the maximum or minimum value of a linear objective function, 
      subject to a set of linear constraints. The method involves the following steps:
      1. Define the problem in terms of decision variables, objective function, and constraints.
      2. Convert the problem into standard form by introducing slack variables, if needed.
      3. Create a tableau containing the coefficients of the decision variables and the right-hand sides 
         of the constraints.
      4. Set up the starting solution in the tableau.
      5. Iteratively use the Simplex algorithm to move from one vertex to another in the polytope defined 
         by the constraints, aiming to improve the objective function value until an optimum solution is 
         reached.
      6. Determine the optimum solution by inspecting the values in the tableau.
  
      Example:
      For linear_programming_using_simplex(['x1', 'x2'], {'x1': 3, 'x2': 4}, {'x1': '<= 2', 'x2': '>= 1'})
      Output: 'The optimal solution is achieved by setting x1 = 2 and x2 = 1, with a maximum objective function value of 10.'
      """
  
output:     from scipy.optimize import linprog
  
      # Convert objective function to minimization form
      c = [-objective_function[var] for var in decision_variables]
  
      # Convert constraints to standard form
      A = []
      b = []
      for var, constraint in constraints.items():
          if constraint.startswith('<='):
              A.append([1 if v == var else 0 for v in decision_variables])
              b.append(float(constraint.split()[1]))
          elif constraint.startswith('>='):
              A.append([-1 if v == var else 0 for v in decision_variables])
              b.append(-float(constraint.split()[1]))
  
      # Solve the linear programming problem using the Simplex Method
      result = linprog(c, A_ub=A, b_ub=b, method='simplex')
  
      # Extract the optimal solution
      optimal_solution = {decision_variables[i]: result.x[i] for i in range(len(decision_variables))}
      max_objective_value = -result.fun
  
      return f"The optimal solution is achieved by setting {', '.join([f'{var} = {val:.2f}' for var, val in optimal_solution.items()])}, with a maximum objective function value of {max_objective_value:.2f}."