CN101526971A - Method for setting input parameters of general simulation system used in container terminal logistics operation - Google Patents

Abstract

The invention discloses a method for setting input parameters of a universal simulation system for container terminal logistics operation. The method is based on common abstraction and random variable extraction. Parameters, loading and unloading bridge parameters, horizontal handling system parameters serving the loading and unloading bridge, stacking system parameters at the front of the wharf, container dumping and collection and distribution system parameters. These input parameters determine the versatility of modeling and simulation of container terminal logistics operation system.

Description

Method for Setting Input Parameters of Universal Simulation System for Container Terminal Logistics Operation

Technical field:

The invention relates to a modeling method of a universal simulation system for logistics operation of a packing wharf, in particular to a method for setting input parameters and random variables of the universal simulation system of logistics operation of a packing wharf.

Background technique:

The input parameters are the driving force of the simulation experiment, and these input parameters determine the generality of the modeling and simulation of the container terminal logistics operation system. The most important part of the set parameters are the random variable parameters.

The container terminal logistics operation simulation system is a typical discrete event dynamic system. In a discrete event dynamic system, the occurrence of a discrete event drives the state of the system to change, and at the same time stimulates new discrete events in the system according to the operating rules of the system, thus forming the evolution process of the system state. In such systems, it is a batch of discrete events rather than continuous variables that determine the course of system behavior. Therefore, it is very important to determine the model of the random variable.

There are a large number of random variables in the container terminal logistics operation simulation system based on the discrete event dynamic system, which makes the system random, complex and dynamic.

Based on the characteristics of the above parameters, the defects in the setting of the parameters in the prior art affect the versatility of the modeling and simulation of the logistics operation system of the container terminal.

Invention content:

The present invention aims at the situation that the generality of modeling and simulation of the existing container terminal logistics operation system is not high, and proposes a method for setting the general model and simulation system parameters of the container terminal logistics operation system, so that the input parameters become a general simulation system impetus for experimentation.

In order to achieve the above object, the technical scheme adopted in the present invention is as follows:

The setting method of the input parameters of the universal simulation system of container terminal logistics operation is based on the common abstraction and random variable extraction. The parameters determined by this method include the setting and selection parameters of the system, ship related parameters, loading and unloading bridge Parameters, parameters of the horizontal handling system serving the loading and unloading bridge, parameters of the stacking system at the front of the wharf, parameters of the container dumping and collection and distribution system.

The setting of the system setting and selection parameters includes process machinery system selection, berth division, number of berths, total simulation time, simulation step size, whether to dump containers and shoreline length;

The setting of the ship parameters includes the distribution pattern of the time interval of the arrival of the ship, the characteristics of the arrival ship type, the distribution pattern of the arrival ship type, the distribution pattern of the loading and unloading rate of the arrival ship, the probability of loading and unloading, the probability of unloading the ship at the same time, and all The lower limit of the operating line of the ship type; and the first seven parameters are random variables.

The setting of the loading and unloading bridge parameters includes loading and unloading bridge type, loading and unloading bridge total amount, loading and unloading bridge loading and unloading efficiency distribution mode, loading and unloading bridge intact rate, loading and unloading bridge allocation mode, wherein the loading and unloading bridge loading and unloading efficiency distribution mode is a random variable;

The setting of the parameters of the horizontal handling system includes the number of machines equipped for each operation line, the intact rate of the horizontal machines, the distribution mode of the operating cycle of the horizontal machines and the distribution mode of the pause time of the horizontal machines, wherein the distribution mode of the operating cycle of the horizontal machines and the distribution of the pause time of the horizontal machines are The model is a random variable;

The setting of the stacking system parameters in the front yard of the wharf includes the total amount of machinery, the intact rate of machinery, the distribution mode of mechanical efficiency, the number of machines in the front yard and the number of machines in the rear yard, wherein the distribution mode of the mechanical efficiency is a random variable;

The setting of the box dumping parameter system includes box dumping time, box dumping ratio, number of horizontal machines, horizontal machine operating cycle distribution pattern, horizontal machine pause time distribution pattern, front-back stacking machine quantity, rear-front dumping The number of machinery in the container yard and the distribution pattern of the efficiency of the machinery in the yard, among which the distribution pattern of the horizontal machinery operation period, the distribution pattern of the pause time of the horizontal machinery and the distribution pattern of the efficiency of the machinery in the yard are random variables;

The setting of the collection and distribution system parameters includes the collection and distribution efficiency distribution mode of the entrance and the distribution mode of the distribution efficiency of the exit, and both are random variables.

The random variable is fitted through the following steps:

(1) First, identify the distribution of random variables, and use the frequency distribution to establish a histogram;

(2) Carry out the assumption of distribution type again;

(3) Check the fitting degree of the distribution type. If it passes, it is determined that the random discrete event conforms to this distribution. If it does not pass, the empirical distribution form is used as parameter input.

The present invention obtained according to the above-mentioned technical scheme makes the input parameters become the driving force of the universal simulation system experiment. The main parameters set are based on common abstraction and random variable extraction. These input parameters determine the versatility of the modeling and simulation of the container terminal logistics operation system.

Description of drawings:

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

Fig. 1 is a relation diagram of parameters in the present invention.

Detailed ways:

In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific illustrations.

The input parameters are the driving force of the simulation experiment. On the basis of general abstraction and random variables, seven types of parameters are set according to the characteristics of general demand. That is: system setting and selection parameters, ship-related parameters, loading and unloading bridge related parameters, horizontal handling system parameters serving the loading and unloading bridge, stacking system parameters at the front of the wharf, container dumping and collection and distribution system parameters. The details are as follows (as shown in Figure 1):

The setting of system setting and selection parameters includes process machinery system selection, berth division, number of berths, total simulation time, simulation step size, container dumping or not, and shoreline length;

The setting of ship parameters includes ship arrival time interval distribution mode, arrival ship type characteristics, arrival ship type distribution mode, arrival ship loading and unloading rate distribution mode, ship loading probability, ship unloading probability, ship unloading probability at the same time and all ship types The lower limit of the operating line; and the first 7 parameters are random variables.

The setting of loading and unloading bridge parameters includes loading and unloading bridge type, loading and unloading bridge total amount, loading and unloading bridge loading and unloading efficiency distribution mode, loading and unloading bridge intact rate, loading and unloading bridge allocation mode, among which the loading and unloading bridge loading and unloading efficiency distribution mode is a random variable;

The setting of the parameters of the horizontal handling system includes the number of machines equipped in each operation line, the intact rate of the horizontal machines, the distribution mode of the operating cycle of the horizontal machines, and the distribution mode of the pause time of the horizontal machines. Random Variables;

The parameter setting of stacking and retrieving system in the front yard of the wharf includes the total amount of machinery, the intact rate of machinery, the distribution mode of mechanical efficiency, the number of machines in the front yard and the number of machines in the rear yard, and the distribution mode of the mechanical efficiency is a random variable;

The setting of the container dumping parameter system includes the container dumping time, the container dumping ratio, the number of horizontal machines, the distribution mode of the horizontal machine running cycle, the distribution mode of the horizontal machine pause time, the number of machines in the dump site A, and the number of machines in the dump site B ( A and B here do not have specific meanings, but are just a code, indicating that there are two container areas that need to be unloaded, from one container area to another, A and B may be any two container areas in the yard) , and the distribution pattern of the yard machinery efficiency, in which the distribution pattern of the horizontal machinery operation cycle, the distribution pattern of the horizontal machinery stop time and the distribution pattern of the stackyard machinery efficiency are random variables;

The setting of the parameters of the collection and distribution system includes the distribution mode of the collection efficiency at the entrance and the distribution mode of the distribution efficiency at the exit, and both of them are random variables.

According to the setting of the above parameters, the generic modeling and simulation can meet the following requirements in terms of resource allocation:

Suitable for simulation of any number of container berths;

How to use the berths of the wharf, whether the number of berths is statically constant, or the berths and their number are determined dynamically according to the shoreline and ship type;

Piers with straight/broken shorelines

The terminal production triggered by the arrival of any type of ship;

Determination of the number of machinery in each link and the proportion of various models;

Is the dumping operation beneficial.

The main parameters designed by the present invention are composed of random variables and non-random variable parameters.

In the discrete simulation system, since the actual system reflected contains the interaction and influence of various random factors, a large number of random factors need to be dealt with repeatedly in the simulation process. Whether it is the occurrence time of various random events, or the arrival flow of temporary entities and the stay time of temporary entities in the simulation system, etc., they are all random variables with different probability distributions. A random sample is taken to obtain the actual parameters for this simulation run.

The random variables involved in the present invention are mainly divided into two categories: random variables related to mechanical efficiency and random variables related to ships.

where the random variable associated with the mechanical efficiency

The random variables related to mechanical efficiency are obtained according to the general abstraction of the general simulation system. Including: efficiency of container loading and unloading bridge, running time and pause time of horizontal handling, mechanical efficiency of storage yard, efficiency of collection and transportation at entrance, efficiency of dredging at exit, period of horizontal handling machinery during container unloading, etc.

1. The efficiency of the container loading and unloading bridge refers to the container volume loaded or unloaded by the container loading and unloading bridge per hour, and the unit is TEU/hour.

2. The horizontal handling cycle between the front of the wharf and the yard refers to the handling cycle of the horizontal handling machinery between the front of the wharf and the yard, including the running and stopping time of the vehicle, specifically including the sum of four times: between the front of the wharf and the yard The operating cycle of horizontal handling machinery, the pause time of horizontal handling machinery waiting for loading and unloading ships at the front of the wharf, the pause time of horizontal handling machinery waiting for loading and unloading boxes in the yard, and the abnormal pause time of horizontal handling machinery during operation. All four times are random variables.

3. The mechanical efficiency of the yard includes the efficiency of the tire gantry crane and the efficiency of the track gantry crane;

The efficiency of RTG refers to the amount of containers processed by RTG per hour, and the unit is TEU/hour.

The efficiency of the track gantry crane refers to the container volume handled by the track gantry crane per hour, and the unit is TEU/hour.

4. Efficiency of containers entering the crossing

The efficiency of containers entering the crossing refers to the volume of containers entering the crossing per hour in the crossing link, and the unit is TEU/hour.

5. The container efficiency at the crossing of the crossing refers to the amount of containers leaving the yard from the crossing per hour at the crossing, and the unit is TEU/hour.

6. The handling cycle of the horizontal handling machine in the box dumping operation is composed of three parts: the running cycle of the horizontal handling machine during the box dumping process, the pause time of the horizontal handling machine waiting for loading and unloading boxes during the box dumping process, and the abnormal pause of the horizontal handling machine during the box dumping process time.

7. The mechanical efficiency of the yard in the container dumping operation includes the efficiency of the tire gantry crane and the efficiency of the track gantry crane;

The efficiency of RTG refers to the amount of containers processed by RTG per hour, and the unit is TEU/hour.

The efficiency of the track gantry crane refers to the container volume handled by the track gantry crane per hour, and the unit is TEU/hour.

Ship-related random variables

1. The loading and unloading rate of ships arriving at the port refers to the ratio of the number of containers loaded or unloaded after the ship arrives at the port to the maximum container capacity of the ship, expressed as a percentage.

2. Arrival ship type characteristics: The ships arriving at the port are random, so the arrival ship type characteristics are random variables, and its characteristics are composed of ship type, maximum container capacity, ship length, spacing requirements, etc.

3. Arrival ship type distribution: The proportion of various types of arriving ships composed of the characteristics of arriving ship types is a random variable, expressed in terms of ship type categories and their percentages.

4. Loading and unloading rate of ships arriving at the port: when loading and unloading ships arriving at the port, the loading and unloading is not limited to the maximum container capacity, but only a certain proportion of containers are required to be loaded and unloaded. This ratio is the loading and unloading rate of ships arriving at the port. The loading and unloading rate of ships arriving at the port is a random variable expressed as a percentage.

5. Probability of loading, unloading, and simultaneous loading and unloading: After the ship is berthed, it may be loaded, unloaded, or loaded and unloaded at the same time.

The proportion of shipment is called the probability of shipment, expressed as a percentage;

The proportion of unloading is called unloading probability, expressed as a percentage;

The probability of simultaneous loading and unloading of ships is called the probability of simultaneous loading and unloading of ships, expressed as a percentage. Therefore, the sum of the loading probability, the unloading probability and the simultaneous loading and unloading probability of the ship is equal to 1.

The scope of data collection of the above random variables mainly includes: time interval of ship arrival at port; distribution mode of ship arrival operation mode; loading and unloading bridge efficiency; tire gantry crane efficiency; wait.

The collected data are as follows:

The loading and unloading time of each box of the loading and unloading bridge: it starts after the spreader of the loading and unloading bridge leaves the collection truck, and the end time is before the spreader of the loading and unloading bridge leaves the collection truck after a cycle.

The horizontal handling cycle time is the sum of the time of waiting for loading and unloading on the loading bridge at the front of the wharf, the running time of handling, the time waiting for the loading and unloading of the gantry crane at the yard, and the abnormal stop time in the middle of driving.

Storage yard mechanical storage time, which includes stacking time and picking time.

Stacking: from the time when the container is lifted on the truck to when the next container is lifted on the truck;

Container pick-up: the container is placed on the truck until the next container is placed on the truck. Among them, if there is a box turned over, the number of box turned over should be recorded one by one.

There are many types of distribution assumptions for system random variables: inductive statistics method, histogram method, probability graph method, empirical distribution, etc.

For above random variable classification methods, the present invention has two kinds of distribution type assumption methods to random variables:

Assumption method for the distribution type of "random variable related to mechanical efficiency": firstly, use the histogram method to fit the random variable; if the random variable conforms to one of the exponential distribution, normal distribution, binomial distribution, and Poisson distribution, Then determine it as the distribution type, and calculate the corresponding parameters; if not, determine it as the empirical distribution type.

The empirical distribution method is adopted for the distribution type assumption method of "ship-related random variables".

2. Fitting algorithm of system random variables

The histogram is based on the basic graph of the density function drawn from the distribution of the obtained samples (X1, X2, ..., Xn) to assume the distribution type; the empirical distribution refers to simply using the observed data to generate a random model, and no longer selects an existing Known theoretical distribution and design distribution parameters to fit the observed data [3].

The random discrete event histogram fitting algorithm proposed in this paper includes: firstly, the identification of the distribution, using the frequency distribution to establish the histogram, and then making the assumption of the distribution; then the fitting degree test of the distribution, if it passes, then determine the random discrete event Events conform to this distribution, and if not passed, an empirical distribution form is used as parameter input.

The specific fitting steps are as follows, which is a fitting algorithm for normal distribution:

1. Provide i original data X, where i satisfies i≥50, and delete some obviously unreasonable numbers that are too large or too small.

2. The simulation system finds out from the sample data $x_1^{*}=\min (x_1,...,x_{\mathrm{no}})$ and $x_{\mathrm{no}}^{*}=\max (x_1,...,x_{\mathrm{no}})$

3. Let the simulation system take the simulation interval as [a, b], where a and b satisfy

$\left\{\begin{array}{c}\&ForAll;\mathrm{\&epsiv;}>0:0<x_1^{*}-a<\mathrm{\&epsiv;}\\ \&ForAll;\mathrm{\&epsiv;}>0:0<b-x_{\mathrm{no}}^{*}<\mathrm{\&epsiv;}\end{array}\right.,$ make

$\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; <</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; <</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; b</span>}\\ \frac{\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}\end{array}\right.$

Among them, the value of the number of interval groups m is suggested to be

$\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; int</span>}<span\; class="notranslate">\; (</span>\sqrt{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span>$

4. Determine the size of the interval determined by the user. This system can adopt a value of (b-a)/m.

5. Let the number (frequency) of statistical sample data falling into each subinterval (t _i , t _i+1 ) of the simulation system be f _i , that is, there is f in the first interval (t ₁ , t ₂ ). ₁ , there is f _m on the mth interval (t _m , t _m+1 ).

6. The simulation system automatically draws the frequency histogram. The simulation system makes a rectangle on (t _i , t _i+1 ), height h _i =f _i , where i=1, 2, . . . , m.

7. The user observes the histogram and selects the distribution type

For example, if the image is normally distributed, and the estimated values of its parameters μ and σ are calculated, N(μ, σ ² ) is initially selected as the probability distribution of X.

8. The simulation system calculates the theoretical probability value on the interval: in order to facilitate the calculation of the probability on each small interval, first standardize x～N(μ, σ ² ),

make $Y=\frac{x-\mathrm{\&mu;}}{{\mathrm{\&sigma;}}^2}~N\left(\mathrm{0,1}\right),$ And expand the value of Y to (-∞, +∞).

beg $\mathrm{\&Phi;}\left(Y_1\right)=\mathrm{\&Phi;}\left(\frac{t_1-\mathrm{\&mu;}}{{\mathrm{\&sigma;}}^2}\right),...,$ $\mathrm{\&Phi;}\left(Y_m\right)=\mathrm{\&Phi;}\left(\frac{t_m-\mathrm{\&mu;}}{{\mathrm{\&sigma;}}^2}\right)$

The simulation system checks the standard normal distribution table provided by the system to get Φ(Y _m )

$p_1=P(-\&infin;,t_1)=\mathrm{\&Phi;}\left(\frac{t_1-\mathrm{\&mu;}}{{\mathrm{\&sigma;}}^2}\right)=\mathrm{\&Phi;}\left(Y_1\right)$ Similarly, p _m = p(Y _m , +∞) = 1-Φ(Y _m )

(9) The simulation system performs the x ² test:

calculate $x_0^2=\underset{k=1}{\overset{m}{\mathrm{\&Sigma;}}}\frac{{(f_k-\mathrm{no}{p}_k)}^2}{{\mathrm{np}}_k}$

The simulation system is used for hypothesis testing, H ₀ : x～N(μ, σ ² ), ^the assumed statistic x ₀ ² obeys x ² (mr-1) when H ₀ is established, and x _α ² (mr- 1), α is the significant level. like ${x_0}^2\&le;x_{\mathrm{\&alpha;}}^2(m-r-1)$ Then accept the hypothesis H ₀ , otherwise, do not accept the hypothesis H ₀ , the simulation system returns to step 7 to ask the user to re-select the distribution, and the system fits again.

The basic principles and main features of the present invention and the advantages of the present invention have been shown and described above. Those skilled in the industry should understand that the present invention is not limited by the above-mentioned embodiments. What are described in the above-mentioned embodiments and the description only illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have Variations and improvements all fall within the scope of the claimed invention. The protection scope of the present invention is defined by the appended claims and their equivalents.

Claims (9)

1. The method for setting the input parameters of the universal simulation system for container terminal logistics operation. This method is based on common abstraction and random variable extraction. It is characterized in that the parameters determined by this method include the setting and selection parameters of the system, Parameters of ship customs, loading and unloading bridge parameters, parameters of horizontal handling system serving loading and unloading bridges, parameters of stacking and retrieving system at the front yard of the wharf, parameters of container dumping and collection and distribution system. 2. The method for setting input parameters of the universal simulation system for container terminal logistics operation according to claim 1, characterized in that, the setting of the system setting and selection parameters includes selection of process machinery system, division of berths, and number of berths , the total simulation time, the simulation step size, whether to dump the box, and the shoreline length; 3. The method for setting input parameters of the universal simulation system for container terminal logistics operation according to claim 1, characterized in that, the setting of the ship parameters includes the distribution mode of the time interval of the arrival of the ship, the characteristics of the arrival ship type, and the arrival time interval. The distribution pattern of ship types in port, the distribution pattern of loading and unloading rate of arriving ships, the probability of loading, the probability of unloading, the probability of unloading ships at the same time, and the lower limit of the operation line of all ship types; and the first seven parameters are random variables. 4. The method for setting input parameters of the universal simulation system for container terminal logistics operation according to claim 1, wherein the setting of the loading and unloading bridge parameters includes the loading and unloading bridge type, the total amount of loading and unloading bridges, and the loading and unloading efficiency of the loading and unloading bridge Distribution mode, loading and unloading bridge integrity rate, loading and unloading bridge allocation mode, among which the distribution mode of loading and unloading bridge loading and unloading efficiency is a random variable; 5. The method for setting input parameters of the universal simulation system for container terminal logistics operation according to claim 1, characterized in that the setting of the parameters of the horizontal handling system includes the number of machines equipped for each operation line, the intact rate of horizontal machines , the distribution mode of the horizontal mechanical operation period and the distribution mode of the horizontal mechanical pause time, wherein the distribution mode of the horizontal mechanical operation cycle and the distribution mode of the horizontal mechanical pause time are random variables; 6. The method for setting input parameters of the universal simulation system for container terminal logistics operation according to claim 1, characterized in that, the setting of the parameters of the stacking and retrieving system at the frontier storage yard of the terminal includes the total amount of machinery, the intact rate of machinery, The distribution pattern of mechanical efficiency, the number of machinery in the front yard and the number of machinery in the rear yard, where the distribution pattern of mechanical efficiency is a random variable; 7. The method for setting input parameters of the universal simulation system for container terminal logistics operation according to claim 1, characterized in that, the setting of the container dumping parameter system includes container dumping time, container dumping ratio, number of horizontal machines, The distribution mode of horizontal mechanical operation period, the distribution mode of horizontal mechanical pause time, the number of machines in the front-back dump yard, the number of machines in the back-front dump yard, and the distribution mode of the efficiency of the yard machinery, among which the distribution mode of horizontal mechanical operation period, The distribution mode of horizontal mechanical stop time and the distribution mode of yard mechanical efficiency are random variables; 8. The method for setting input parameters of the universal simulation system for container terminal logistics operation according to claim 1, characterized in that, the setting of the parameters of the collection and distribution system includes the collection efficiency distribution mode of the entrance and the distribution mode of the exit. Efficiency distribution patterns, and they are all random variables. 9. The method for setting input parameters of the universal simulation system for container terminal logistics operation according to any one of claims 2 to 8, wherein the random variable is fitted through the following steps: (1) First, identify the distribution of random variables, and use the frequency distribution to establish a histogram; (2) Carry out the assumption of distribution type again; (3) Check the fitting degree of the distribution type. If it passes, it is determined that the random discrete event conforms to this distribution. If it does not pass, the empirical distribution form is used as parameter input.

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