Nowadays, the internationalization of supply chains makes the management of operation affairs face a great challenge. On the other hand, vague parameters have challenged decision-makers to drive decision-making. To cope with these challenges, this study tries to model a green SCM (GSCM) model which considers fuzzy parameters. The objective function of our model is to minimize total fuzzy cost including fuzzy establishment costs of the plants and distribution centers, fuzzy transportation costs among the suppliers, facilities, and customers, fuzzy hiring cost of the transportation facilities, and miscellaneous fuzzy environmental impact costs. The developed model also includes facilities location constraints, material flow constraints, open transportation routing from plants to customers and from distribution centers to customers. Also, determining alternative products for customers has not been addressed in the literature. Therefore, this paper tries to focus on the mentioned complex problem and develop a comprehensive model. Because of the level of complexity of the developed model, two empowered meta-heuristic approaches, named fuzzy hybrid genetic algorithm (FHGA) and fuzzy hybrid biogeography-based optimization algorithm (FHBBO), are implemented to solve the NP-hard developed problem. According to the best of our knowledge, the proposed FHGA is not addressed in the literature in this way. For instance, most of the fuzzy algorithms either are not hybrid or get out of the fuzzy environment in one of their complex evolution processes. However, our fuzzy hybrid algorithms follow a fuzzy environment from beginning test initialization to calculating the objective function and presenting the convergence plots and none of our parameters are defuzzied in all steps of these processes. Besides, miscellaneous Figures, illustrations, and tables support the explanations of results. تفاصيل المقالة

Undoubtedly, rating bank branches is one of the essential tool managers use to promote branches. In this study, a multi-criteria problem applied in banking has been addressed. In this research, a framework for ranking 20 branches of Tose’e Ta'avon bank in Iran (Khuzestan province) using decision-making methods has been considered as a case study. Essential criteria are selected through experts and research literature. Then, according to the uncertainty in some indicators and the elimination of defects related to the investigation at a certain point, the data is determined in the form of interval values. The weighting of the criteria using experts' opinions, interval Shannon entropy, and the linear combination of the two, and considering the final matrix extracted from three 4-month intervals (geometric mean of 3 matrices) using three approaches, namely PROMETHEE II, EDAS, and TOPSIS with interval values, the ranking of bank branches has been used for a case study. Then, benchmark tests are used to validate the methods to provide a fairer ranking. Finally, the managers can see the actual position of the branches in identify throughout the year and use it to improve the bank's performance. تفاصيل المقالة