This study presents an integrated approach for analyzing the impact of macro-ergonomics factors in healthcare supply chain (HCSC) by data envelopment analysis (DEA). The case of this study is the supply chain (SC) of a real hospital. Thus, healthcare standards and macro-ergonomics factors are considered to be modeled by the mathematical programming approach. Over 28 subsidiary SC divisions with parallel missions and objectives are evaluated by analyzing inputs and outputs through DEA. Each division in this HCSC is considered as decision making unit (DMU). This approach can analyze the impact of macro-ergonomics factors on supply chain management (SCM) in healthcare sector. Also, this method ranks the relevant performance efficiencies of each HCSC. In this study by using proposed method, the most effective macro-ergonomics factor on HCSC is identified as "teamwork" issue. Also, this study would help managers to identify the areas of weaknesses in their SCM system and set improvement target plan for the related SCM system in healthcare industry. To the best of our knowledge, this is the first study for macro-ergonomics optimization of HCSC.

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

No abstract