Utilizing lexicographic max product of picture fuzzy graph in human trafficking

Graph structures are an essential tool for solving combinatorial problems in computer science and computational intelligence. With an emphasis on signed graphs, picture-fuzzy graphs, and graphs with colored or labeled edges, this study explores the properties of picture-fuzzy graph topologies. Within these frameworks, it presents key ideas such as the lexicographic-max product, vertex degree, and total degree. The use of picture-fuzzy graphs' lexicographic-max product to tackle intricate problems like human trafficking is a key component of this study. The study illustrates how this strategy can improve decision-making processes in such crucial areas by utilizing the special qualities of picture-fuzzy graphs. The study is supported by informative numerical examples that show how useful these ideas are in real-world situations. In addition, the study offers a thorough algorithmic foundation for applying the lexicographic-max product in practical situations, especially those involving human trafficking. The goal of this framework is to provide a workable approach for applying picture-fuzzy graph structures to enhance decision-making and tackle important societal issues.