Lagrangian Relaxation Lower Bound exceeds the Upper bound and the Optimal solution

I'm trying to minimize an MIP model employing a Lagrangian relaxation approach. However, I've encountered an issue where, in certain instances, the lower bound (resulting from the Lagrangian sub-problems) surpasses both the upper bound (resulting from the Master problem) and the optimal solution of the original problem. Consequently, no Lagrangian bound is obtained, as the lower bound continues to outpace the upper bound, while the upper bound steadily decrease. I tried to enhance the accuracy of the models, the issue is still there. The MIP model is attached, I relaxed the second constraint which gives me two subproblems one in x (binary) and z (binary) and one in y (continuous). I use a random multiplier to solve the subproblems, then I use their solutions to add cuts to the master problem and update the Lagrangian multipliers and solve the subproblems again and go on like that. The lower bound is obtained by adding the optimal solutions of the subproblems while the upper bound is obtained by solving the master problem.