<p>Unexpected and Scheduled maintenance activities in production scheduling are prominent issues in environmentally responsible production systems that led to real-time scheduling and effective sustainable manufacturing systems. This study considers multi-objective optimization for stochastic flexible job shop scheduling problem undergoing reliability-based preventive maintenance, random machine breakdown and sequence-dependent setup time. Due date tightness factor (<i>Ɗ</i>), and routing flexibility (Ʀ), percentage of machine failure (ƥ), reliability-centered preventive maintenance (ℜ), and mean time to repair for random machine breakdown (Ƶ), are five input factors taken into consideration. Four performance measurements (P_<i>ms</i>), i.e., total energy consumption cost, maximum flow time, total setup time and maximum tardiness, are used to assess system performance. A simulation-optimization is carried out utilizing TOPSIS and desirability approach. ANOVA indicates that model is significant with reasonable accuracy. The results show that input parameters, i.e., ℜ, ƥ, Ƶ, <i>Ɗ</i>, and Ʀ, significantly impact multi-response performance coefficient (MRPC). Further, <i>Ɗ</i> and Ʀ have emerged as major factors affecting MRPC. The combined TOPSIS-DESIRABILITY technique estimates the best system conditions for a considered sustainable manufacturing system. Validation results illustrate that the model can be used for future prediction as error is less than 1%. Considering energy aspect in stochastic flexible job shop undergoing simultaneously preventive maintenance and machine failure in dynamic job environment shows the novelty of the work.</p> Graphical abstract <p></p>