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In this study, a unique adaptive neuro fuzzy inference system for optimization of decision making process in natural gas transmission unit is presented. To do this, macro-ergonomics and integrated resilience engineering factors are considered as outputs to assess operators' performance and decision styles. Evaluation of decision-making styles of control room operators would help managers adjust job specification with human characteristics. In this regard, a pertinent standard questionnaire is designed to collect required data. Operators' decision styles are identified by standard questionnaire and, then, their efficiency values are calculated by considering macro-ergonomics factors through a unique adaptive neuro-fuzzy inference system (ANFIS). Moreover, fuzzy data envelopment analysis (FDEA) model is applied to validate the obtained results. Analysis of variance is used to investigate the results of ANFIS. The results show that the best decision style is flexible DM style wherein information is pertinently used as needed and there are multiple focuses for making decisions. In addition, the results reveal that information flow, safety, system efficiency, redesign, preparedness, and learning have the lowest efficiency values amongst macro-ergonomics and integrated resilience engineering factors and require more attention. Then, DM speed and violation of regulations obtain the best results in the gas transmission unit. This is the first study that introduces a unique intelligent adaptive

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This paper presents a simulation–optimization model for tandem G/G/K queuing systems with infinite capacity considering reneging and server breakdowns. In this problem, the entities that enter each queue after a certain amount of waiting time renege from the queue. Moreover, each of the servers may break down based on the failure rates and will be repaired immediately according to the repair rates. Owing to the complexity of these systems, the OptQuest approach that combines the scatter search (SS) and tabu search (TS) algorithms is employed to determine the optimal number of servers in each queue according to the capacity of resources as well as budget constraints. The main objective of this optimization is to minimize the total time in the system by keeping the average waiting time in each queue below the allowable waiting time. Finally, 20 distinct samples of tandem G/G/K queuing systems are used to evaluate the validity of the proposed simulation model and the optimization algorithm. To the best of our knowledge, this is the first study that both simulates and optimizes tandem G/G/K queuing systems by simultaneously considering reneging and server breakdowns such that budget constraints are satisfied.