(1) Increase the impact of new equipment on the company or enterprise; ② Design the quality of new production line;
③ Compare the advantages and disadvantages of various design schemes and so on.
Logistics simulation is essential to reduce the whole logistics investment cost. This paper focuses on the analysis and description of the application status and development trend of logistics simulation software in China.
Core technology of logistics system simulation
Logistics system simulation is a typical discrete event system simulation, and its core is clock advance and event scheduling mechanism. Discrete event system refers to a system in which the state changes discretely at some random time points. This behavior that causes state changes is called "event", so this system is driven by events; Moreover, "events" often occur at random time points, also known as random events, so discrete event systems generally have random characteristics; The state variables of the system often change discretely. 1, analog clock
Analog clock is used to indicate the change of analog time. In discrete event system simulation, because the change of system state is discontinuous, the system state will not change until two adjacent events occur, so the simulation clock can span these "inactive" periods. From one event to the next. Because simulation is essentially a dynamic description of the system state in a certain time series. Therefore, the analog clock is generally the main independent variable of simulation. There are three analog clock advancing methods: event scheduling method, fixed incremental advancing method and dominant clock advancing method.
It should be pointed out that the simulation clock shows the time spent in system simulation, not the time when the computer runs the simulation model. Therefore, the simulation time is proportional to the real time. For such a complex electromechanical system as logistics system, the simulation time can be much shorter than the real time. The real system actually runs for a few days and months, and it only takes a few minutes to simulate it with a computer.
2. Event scheduling method
Event scheduling method is an event-oriented method, which defines events and processes a series of events in chronological order. Record the change of system state caused by each event, and complete the simulation of the whole dynamic process of the system. Because all events are predetermined and the state changes occur at a certain predetermined moment, this method is suitable for systems with certain activity duration.
In the event scheduling method, the analog clock is advanced according to the next time step method. By establishing an event table, the scheduled events are put into the event table in the order of time occurrence. Analog clocks always advance to the earliest moment. Then, the change of system state when the event occurs is processed, and the statistical calculation required by users is carried out. In this way, the analog clock continuously advances from one event occurrence time to the next earliest event time, leading to the end of the simulation.
3. Generation of random numbers and random variables
In the logistics system, the arrival of the workpiece, the arrival of the transport vehicle and the transport time are generally random. When simulating a system affected by random factors, we must first establish a random variable model. That is, determine the random variables of the system and determine the distribution types and parameters of these random variables. For a random variable whose distribution type is known or can be determined empirically, only its parameters need to be determined.
After establishing the random variable model, it is necessary to generate a series of sampling values of random variables with different distributions in the computer to simulate various random phenomena in the system. The practical method to generate the sampling value of random variables is usually to generate a continuous and evenly distributed random number with the interval of [0, 1], and then generate the required random variables through some transformation and operation.
After getting random numbers with uniform distribution, good independence and long period in the interval of [0, 1], the next problem is how to generate random variables corresponding to the actual system. The premise of generating random variables is to determine the distribution and parameters of random variables according to the observed values of random variables in the actual system.
Inverse transformation method is the most commonly used method, which is based on the inverse transformation law of probability integral, and the distribution function of random variable X is F (x). UI is a random number evenly distributed in the interval of [0, 1], and the required random variable x can be obtained by using the inverse distribution function X=F- 1(μ).