Intuitionistic fuzzy multi-stage multi-objective fixed-charge solid transportation problem in a green supply chain

This research mainly focuses on presenting an innovative study of a multi-stage multi-objective fixed-charge solid transportation problem (MMFSTP) with a green supply chain network system under an intuitionistic fuzzy environment. The most controversial issue in recent years is that greenhouse gas emissions such as carbon dioxide, methane, etc. induce air pollution and global warming, thus motivating us to formulate the proposed research. In real-world situations the parameters of MMFSTP via a green supply chain network system usually have unknown quantities, and thus we assume trapezoidal intuitionistic fuzzy numbers to accommodate them and then employ the expected value operator to convert intuitionistic fuzzy MMFSTP into deterministic MMFSTP. Next, the methodologies are constructed to solve the deterministic MMFSTP by weighted Tchebycheff metrics programming and min-max goal programming, which provide Pareto-optimal solutions. A comparison is then drawn between the Pareto-optimal solutions that are extracted from the programming, and thereafter a procedure is performed to analyze the sensitivity analysis of the target values in the min-max goal programming. Finally, we incorporate an application example connected with a real-life industrial problem to display the feasibility and potentiality of the proposed model. Conclusions about the findings and future study directions are also offered.

Automated summary

This research focuses on addressing the multi-stage multi-objective fixed-charge solid transportation problem with a green supply chain network system in an intuitionistic fuzzy environment. The study utilizes trapezoidal intuitionistic fuzzy numbers and the expected value operator to convert the problem into a deterministic one, and then solves it using weighted Tchebycheff metrics programming and min-max goal programming to provide Pareto-optimal solutions.