In the literature, most existing EPQ models for imperfect processes do not consider statistical quality control methods such as acceptance sampling plans, although, these methods are widely employed in industry. The hypothesis of 100% inspection of all produced items is far from being used in practice because of its high cost. In this paper, we addressed the problem of joint determination of the optimal lot sizing and production-inventory control policy for unreliable and imperfect manufacturing systems where quality control is performed using a single acceptance sampling plan. The problem was formulated with a stochastic dynamic programming model in which the lot sizing and the production rate are considered as decision variables. Due to the high stochastic level of the model and the complexity of the system dynamics, it was shown that the optimization problem is intractable either analytically or numerically. However, we proposed a heuristic feedback control policy based on a combination of a modified HPP and batch processing control policy. The efficiency of the control policy was illustrated using a simulation-based experimental approach. The main advantage of this approach is that provides the possibility of establishing a realistic representation of the stochastic and dynamic behaviour of the system using a combined discrete-continuous simulation model, and optimizing the control policy parameters. A thorough sensitivity analysis was performed and some interesting behaviours were observed, which underscores the strong and complex interaction between the production and quality aspects in real manufacturing systems. Possible extensions of this work can be envisaged when quality control is performed using double or multiple acceptance sampling plans. As well, further research could be carried out to investigate the joint optimization of the production control policy and sampling plan parameters.