1. Linear programming model: Linear programming is an optimization technique used to maximize or minimize a linear objective function under a set of linear constraints. It is widely used in production planning, resource allocation and transportation.
2. Nonlinear programming model: Nonlinear programming is an extension of linear programming, which is used to solve optimization problems under nonlinear constraints. It is widely used in engineering design, economic analysis and financial fields.
3. Integer programming model: Integer programming is a special linear programming, in which variables can only take integer values. It is widely used in personnel scheduling, vehicle routing and task assignment.
4. Dynamic programming model: Dynamic programming is an optimization technique, which is used to solve the decision-making problem with overlapping sub-problems and optimal substructure properties. It is widely used in project management, resource allocation and path planning.
5. Stochastic process model: Stochastic process is a mathematical model used to describe stochastic phenomena that change with time. It is widely used in financial market analysis, communication system design and biostatistics.
6. Statistical regression model: Statistical regression is a statistical analysis method used to establish the quantitative relationship between dependent variables and independent variables. It is widely used in the fields of economics, medicine and environmental science.
7. Time series model: Time series is a collection of data arranged in time sequence, which is used to analyze and predict trends and patterns that change with time. It is widely used in meteorology, economics and financial market analysis.