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The purpose of this paper is to clarify how enablers of quality management can secure the satisfaction and loyalty of patients through increasing service quality in hospitals.

The current study conducted a review of the existing literature to identify operational dimensions of the research variables. As a result of the review, 17 dimensions were identified; five European foundation for quality management enablers, five SERVQUAL service quality dimensions, four patients’ satisfaction elements and three patients’ loyalty components. To evaluate interrelationships among these 17 research dimensions, decision-making trial and evaluation laboratory technique was applied using experts’ opinions.

Several key relations were found among research dimensions. Research findings could provide a scientific insight for hospital managers to understand how they could increase the level of patients’ satisfaction and loyalty through high-quality services provided by quality management enablers.

This study has been conducted based on the expert’s opinions from private hospitals located in Tehran and Alborz provinces, Iran. Although the results could be useful for hospital managers in different places and could provide them a valuable insight and knowledge, findings are limited to Iranian private hospitals.

It is taken for granted that patients’ satisfaction and loyalty could increase as a result of high-quality medical and treatment services in hospitals. On the other hand, offering excellent services meeting all the needs and expectations of customers could be consequence product of quality management enablers. Even though satisfaction and loyalty of customers, i.e. patients, is the primary target of quality management, there is little research in the literature as to how enablers of quality management can secure the satisfaction and loyalty through increasing service quality in hospitals. The gap is more critical because the specialized dimensions of four research variables have not been previously integrated into a coherent framework and interrelationships among them have not been studied and clarified in detail. The current study attempts to bridge this gap.

In this study, the authors consider the key decisions in the design of the green closed-loop supply chain (CSLC) network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, in this paper is the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.

In this study, the author consider the key decisions in the design of the green CLSC network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.

The results indicate that the results obtained from the colonial competition algorithm have higher quality than the genetic algorithm. This quality of results includes relative percentage deviation and computational time of the algorithm and it is shown that the computational time of the colonial competition algorithm is significantly lower than the computational time of the genetic algorithm. Furthermore, the limit test and sensitivity analysis results show that the proposed model has sufficient accuracy.

Solid modeling of the green supply chain of the closed loop using the solid optimized method by Bertsimas and Sim. Development of models that considered environmental impacts to the closed loop supply chain. Considering the impact of the technology type in the manufacture of products and the recycling of waste that will reduce emissions of environmental pollutants. Another innovation of the model is the multi-cycle modeling of the closed loop of supply chain by considering the uncertainty and the fixed and variable cost of transport.