This study is aimed to develop a novel intuitionistic fuzzy P-graph with Gaussian membership function to help decision-makers deal with complex process network systems.
Two fuzzy P-graph case studies of the cogeneration system were selected, and relevant data were collected, including the structure and flow sequence of the system, and the rate of material and product transitions between the operating units. Gaussian function membership was set according to the restriction of fuzzy upper and lower bounds. Then the α-cut was used to obtain different upper and lower bound restrictions of each membership degree. After finding the optimal and suboptimal solutions for different membership degrees, the results of non-membership and hesitation were calculated.
The proposed method will help the decision maker consider the risk and provide more feasible solutions to choose the optimal and suboptimal solutions based on their own or through experience. The proposed model in this study has more flexibility in operation and decision making.
This study is the first to propose a novel intuitive fuzzy P-graph and demonstrates the effectiveness and flexibility of the method by two case studies of the cogeneration system. However, the addition of hesitation can increase the error tolerance of the system. Even for the solutions with a high degree of membership, optimal and suboptimal solutions still exist for the decision maker to select. Since decision makers expect the higher achievement of the target requirements; thus, it is important to have more feasible solutions with a high degree of membership.
