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. Linear programming model is widely used in resource allocation, production planning, transportation scheduling and other issues.
2. Nonlinear programming model: Nonlinear programming is an extension of linear programming, which is used to maximize or minimize a nonlinear objective function under a set of nonlinear constraints. Nonlinear programming model is often used in engineering design, economic analysis and other fields.
3. Integer programming model: Integer programming is a special linear programming, in which variables can only take integer values. Integer programming model is often used in personnel scheduling, vehicle scheduling and other issues.
4. Dynamic programming model: Dynamic programming is an optimization method that decomposes a complex problem into a series of subproblems and uses the solutions of the subproblems to solve the original problem. Dynamic programming model is often used in the shortest path, maximum flow and other issues.
5. Stochastic process model: Stochastic process is a mathematical tool to describe the law of stochastic phenomena changing with time. Stochastic process model is often used in queuing theory, signal processing and other fields.
6. Markov chain model: Markov chain is a random process with "no aftereffect", that is, the future state is only related to the current state and has nothing to do with the past state. Markov chain model is often used in financial market analysis, weather forecast and other issues.