In the growing and turbulent market of the current world both in national and in international relations, the need for reviewing and assessment of the resiliency of suppliers as one of the new concepts in supply chain management has been prioritized. In addition, globalization, increasing the regulations of governmental and non-governmental organizations, customers’ request and pressure regarding environmental issues has led organizations to evaluate the measures necessary to implement green supply chain management, to improve their environmental and economic performance. The subject of selecting and evaluating suppliers on green supply chain with resilience capability first requires the identification of the supplier evaluation criteria so that it considers the resiliency of the supply chain concurrent with the concepts of green supply chain. Secondly, right tools should be used for the decision. Considering all the criteria and indicators in conditions of uncertainty encourages the development and application of methods, such as fuzzy theory and grey systems theory. In this research, a comprehensive model for evaluating green resilience supply chain network is provided. In order to apply both quantitative and qualitative criteria, the possibility of making the target based criteria dimensionless along the cost or benefit criteria, the use of experts’ opinions in the allocation of weights to the criteria and indicators using grey numbers, grey relational analysis are developed and improved. In the end, the implementation of the proposed model is explained step by step in a case study and the future conclusions and recommendations are suggested.

A neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system using approximate techniques of neural networks. Both neural network and fuzzy system have common features. These can solve problems that there is no mathematical model for them. Adaptive Neuro-Fuzzy Inference System (ANFIS) is an adaptive network that uses supervised learning on learning algorithm. Selecting optimization method in training, to achieve effective results with ANFIS is very important. Heuristics and metaheuristics algorithms attempt to find the best solution out of all possible solutions of an optimization problem. ANFIS training can be based on non-derivative algorithms. Heuristics and metaheuristics are non-derivative algorithms that can lead to better performance in ANFIS training. Most heuristic and metaheuristic algorithms are taken from the behavior of biological systems or physical systems in nature. The newly released Emperor Penguins Colony (EPC) algorithm is a population-based and nature-inspired metaheuristic algorithm. This algorithm has many potential for solving various problems. In this paper an optimized ANFIS based on newly EPC algorithm is proposed. The optimized ANFIS is compared with other non-derivative algorithms on benchmark datasets. Eventually, the proposed algorithm is used to solve the classical inverted pendulum problem. The results show that the proposed ANFIS based on EPC algorithm has less error and better performance than other state-of-the-art algorithms in both training and testing phase.