"Theory and Practice of Uncertain Programming" is a comprehensive book on uncertain programming, a branch of optimization theory in uncertain environments. The book covers various types of programming, including stochastic programming, fuzzy programming, rough programming, and others. It provides a self-contained presentation of uncertain programming theory, including numerous modeling ideas and applications in fields such as transportation, inventory systems, production processes, and more. The book is divided into seven parts. Part I introduces basic concepts of mathematical programming, genetic algorithms, and neural networks. Part II discusses methods for generating random numbers, the law of large numbers, stochastic simulation, and applications in decision problems. Part III covers possibility space, fuzzy variables, and fuzzy programming theory. Part IV focuses on rough space and rough programming. Part V deals with fuzzy random variables and fuzzy random programming. Part VI discusses random fuzzy variables and random fuzzy programming. Part VII presents a spectrum of multifold uncertain variables and an uncertain programming theory. The book is suitable for researchers, engineers, and students in operations research, management science, information science, system science, computer science, and engineering. It provides numerous new modeling ideas and serves as a stimulating and useful reference. The author also provides a website with C++ source files of hybrid intelligent algorithms. The book includes numerous examples and numerical illustrations to help readers understand the concepts and applications of uncertain programming."Theory and Practice of Uncertain Programming" is a comprehensive book on uncertain programming, a branch of optimization theory in uncertain environments. The book covers various types of programming, including stochastic programming, fuzzy programming, rough programming, and others. It provides a self-contained presentation of uncertain programming theory, including numerous modeling ideas and applications in fields such as transportation, inventory systems, production processes, and more. The book is divided into seven parts. Part I introduces basic concepts of mathematical programming, genetic algorithms, and neural networks. Part II discusses methods for generating random numbers, the law of large numbers, stochastic simulation, and applications in decision problems. Part III covers possibility space, fuzzy variables, and fuzzy programming theory. Part IV focuses on rough space and rough programming. Part V deals with fuzzy random variables and fuzzy random programming. Part VI discusses random fuzzy variables and random fuzzy programming. Part VII presents a spectrum of multifold uncertain variables and an uncertain programming theory. The book is suitable for researchers, engineers, and students in operations research, management science, information science, system science, computer science, and engineering. It provides numerous new modeling ideas and serves as a stimulating and useful reference. The author also provides a website with C++ source files of hybrid intelligent algorithms. The book includes numerous examples and numerical illustrations to help readers understand the concepts and applications of uncertain programming.