input: 
  def solve_linear_programming_problem(decision_variables, objective_function, constraints):
      """
      Solve a linear programming problem using the Simplex Method. This method involves finding
      the maximum or minimum value of a linear function subject to linear constraints. The method
      uses simultaneous equations to define a polytope that contains the solution and 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.
      
      Example:
      decision_variables = ['x1', 'x2']
      objective_function = {'x1': 2, 'x2': 3}
      constraints = [{'x1': 1, 'x2': 2, 'rhs': 10}, {'x1': 3, 'x2': 1, 'rhs': 15}]
      
      solve_linear_programming_problem(decision_variables, objective_function, constraints)
      # Output: {'x1': 5, 'x2': 2.5, 'value': 17.5}
      """
  
output:     from scipy.optimize import linprog
  
      # Convert objective function to a list of coefficients
      c = [objective_function[var] for var in decision_variables]
  
      # Convert constraints to a list of lists of coefficients and a list of right-hand sides
      A = [[constraint[var] for var in decision_variables] for constraint in constraints]
      b = [constraint['rhs'] for constraint in constraints]
  
      # Solve the linear programming problem
      result = linprog(c, A_ub=A, b_ub=b, method='simplex')
  
      # Extract the solution
      solution = {var: result.x[i] for i, var in enumerate(decision_variables)}
      solution['value'] = result.fun
  
      return solution