Anylogic simulation modeling (1)

1. Analytical methods and simulation modeling

1.1 Analytical model

That is, write a formula directly, but the direct formula (analytic method) is limited. The formula is suitable for expressing the dependence between static variables, while other systems with dynamic behavior are not suitable, such as having the following characteristics:

1. Non-linear behavior
2. Memory
3. Non-intuitive effects between variables
4. Time-dependent and causal-dependent
5. Some other uncertainties and a lot of parameters

2.2 Simulation and modeling

Simulation model: mainly describes the rule set of how the current state of the system transitions to the next state. These rules come in many forms: difference equations, state diagrams, process flowcharts, and scheduling schemes.
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2. Three methods of modern simulation modeling

Discrete event modeling (DE), agent-based modeling (AB), system dynamics model (SD).

1. System dynamics model: suitable for higher levels of abstraction and decision-making modeling, which emphasizes causal feedback , that is, states will affect each other before.
2. Discrete event modeling: supports abstract levels of middle and lower layers, emphasizing interlocking procedures
3. Agent modeling: suitable for the mid-sampling level, that is, it can realize the details of the bottom abstraction, and can also achieve higher abstraction levels such as companies and governments, which emphasizes interaction

3. Discrete event modeling

The discrete event model can be represented as a process flow diagram, where each module represents various operations. The main operations include: various time delays, resource services, road selection, separation and combination, etc. The entity competes for resources and causes a time delay, so the entity will have a queue .

Entities can represent customers, patients, calls, paper or electronic documents, parts, products, tasks, etc.
Resources represent staff, doctors, operators, workers, servers, processors, etc.

The service time and entity arrival time are taken from a random probability distribution, so that the discrete event model has a certain randomness.

Typical discrete event model outputs include:

1. Resource utilization rate (employee full load rate)
2. The residence time of the entity in all or part of the system
3. waiting time
4. Queue length
5. System throughput
6. bottleneck
7. Entity construction and processing overhead

3.1 Several basic concepts

1. Resources: Resource Pool
2. Resource extraction: Seize
3. Delay: Delay
4. Resource release: Release
5. Service: Service. I.e. the set of 2, 3, 4
6. Waiting: Queue

3.2 Time operation mechanism

Time there will be a list in chronological, handle events will be as follows:
Step.1: programs to deal with the closest event
Step.2: the simulation time to a time event
Step.3: update the system state variables
Step. 4: Insert future events in time sequence