[0016] The present invention will be described in further detail below with reference to Figure 1. A container terminal management method includes the following steps: (a) The linear length of the truck in different container areas is transformed into a distance point measurement through a nonlinear scale transformation. Perform fuzzy segmentation, establish the corresponding distance domain, distance fuzzy set, and distance fuzzy set membership function. The expression of the distance point metric is: N = Δ 10 * | i A - i B | + | j A - j B | + | k A - k B | , The position of point A is expressed as (i A , J A , K A ), the position of point B is expressed as (i B , J B , K B ); (b) Fuzzy segmentation of the importance of different container operations, establishing the corresponding importance domain, importance fuzzy set, and importance fuzzy set membership function; (c) Fuzzy segmentation of the number of gantry cranes in the terminal box , Establish the corresponding domain of the number of gantry cranes, the fuzzy set of the number of gantry cranes, and the membership function of the fuzzy set of the number of gantry cranes; (d) Fuzzy segmentation of the number of trucks waiting under the gantry crane, and establish the corresponding domain of the number of trucks waiting under the gantry crane, A fuzzy set of the number of trucks waiting under the gantry crane. The fuzzy set of the number of trucks waiting under the gantry crane is equivalent to the corresponding fuzzy set describing the rapidity of response, thereby establishing the membership function of the fuzzy set of related rapidity of response; (e) According to fuzzy control Regular expression: R i : Ifx is A i ,……And y is B i , Then z=C i (x,...y) Establish a comprehensive fuzzy dispatching rule base for truck comprehensive state evaluation including distance fuzzy sets and importance fuzzy sets, and container fuzzy stacking rule bases including fuzzy sets of number of gantry cranes and fuzzy sets of response speed, where: R i For fuzzy control rules, x, y, and z are linguistic variables representing the system state and control quantity, A i , B i , C i These are the linguistic values of x, y, and z, and x, y, and z have their respective domains; (f) The dispatcher or computer system evaluates the fuzzy dispatching rule base and the container fuzzy stacking rule base according to the above-mentioned comprehensive state of the truck The whole-field dispatching plan and the whole-field container stacking plan are further fuzzy inference to establish the final container terminal management plan.
[0017] The linear lengths of the trucks in different box areas are converted into distance point measures by nonlinear scale transformation, and the positions of the dock front and the storage yard are (i, j, k), i≤n; j≤m; k≤p2 The distance between two different box areas A and B is represented by distance points, where the box area of A is ((i A , I A , K A ), the box location of B is (i B , J B , K B )), the distance point is defined as N = Δ 10 * | i A - i B | + | j A - j B | + | k A - k B | , The distance fuzzy segmentation is 1, 2, 3, 4, 5, 6, 7,> 7, which is the distance domain z = {1, 2, 3, 4, 5, 6, 7,> 7}, where 1, 2, 3, 4, 5, 6, 7 respectively indicate that the distance is in the same operation area of the yard or wharf,> 7 indicates that the distance is in different operation areas, and the range of distance changes and quantification levels are non-uniformly quantified, see table 1. From this, establish a fuzzy set with 8 variables corresponding to describe the distance: N1, N2, N3, N, R3, R2, R1, VR, and determine the membership function of the fuzzy set, see Table 2.
[0018] Quantization level (distance point)
[0019] 1
[0020] The basic idea of determining the importance of homework: the more important the homework, the more trucks should be equipped for homework. Therefore, determining the importance of the operation is directly related to factors such as the artificial determination of the priority of the operation, the actual number of waiting trucks, the number of shortages of waiting trucks, and the degree of operation priority and the number of waiting trucks. Let P be the artificially recognized operation priority (P It should be possible to intervene randomly to reflect people’s experience and intelligence.), N is the actual number of collection cards, L is the number of waiting collection cards, the importance of the job can be defined as: L i =P i -N i , I≤n, where n represents the number of job destinations, if L i <0, indicating that the number of collection cards is sufficient; L i =0 means the number of cards is just right, Li0 means that the number of trucks is insufficient. The principle of truck scheduling according to the importance of the job is that if L iL j , The collection card will be dispatched to the destination i first, if L i =L j (i≠j), and if P iP j , The collection card will be dispatched to the destination i first, if P jP i , The collection card will be dispatched to destination j first. The importance description and its quantification level are shown in Table 3. The importance fuzzy segmentation is ≤0,1,2,3,4,5,6,≥7, and the importance domain y={≤0,1, 2, 3, 4, 5, 6, ≥ 7}, establish the importance fuzzy set P1, P2, P3, P4, VP with 5 variables, establish the importance fuzzy set membership function, see Table 4.
[0021] Quantification level (important index) L i
[0022]
[0023] The truck site intelligent dispatching rule library adopts the fuzzy scheduling plan of state evaluation. The truck site intelligent dispatching rules include 1. Human intervention priority, 2. Priority cabin priority, 3. Closest distance priority, 4. Frontier terminal priority (the principle of repeating and repeating ). According to the above-mentioned principle obtained in practice, and then according to the fuzzy control rule expression: R i : If x is A i ,……And y is B i , Then z=C i (x,……y) Establish a comprehensive state assessment fuzzy scheduling rule base for trucks including distance fuzzy sets and importance fuzzy sets respectively, where: R i For fuzzy control rules, x, y, and z are linguistic variables representing the system state and control quantity, A i , B i , C i These are the linguistic values of x, y, and z, and x, y, and z have their own domains. See Table 5 for the fuzzy scheduling rule base for comprehensive state evaluation of trucks.
[0024] Table 5. Fuzzy scheduling rule base for comprehensive state evaluation of trucks
[0025]
[0026] The rules in the intelligent dispatching of trucks on site using fuzzy technology usually come from the knowledge of terminal operation engineers and managers. For a multiple-input multiple-output (MINO) system, the rules have the following expressions: R = { R MIMO 1 , R MIMO 2 , · · · · · · , R MIMO n } , Where R i MIMO If X is A i and……and Y is B i , Then Z i Is C i ,……,Z q Is D i , R i MIMO The preconditions constitute the fuzzy set A on the direct product space X×……×Y i ×……×B i , The conclusion is that q control functions are combined, and they are independent of each other. Therefore, the i-th rule R i MIMO It can be expressed as the following fuzzy implication relation: R i MIMO : (A i ×……×B i )→(Z j +……+Z q ), where the + sign represents the union of independent control factors, so the rule R can be expressed as:
[0027] = { Y i = 1 n [ ( A i × . . . . . . × B i ) → Z i ] , · · · · · · , Y i = 1 n [ ( A i × . . . . . . × B i ) → Z q ] }
[0028] = { RB MISO i , Λ , RB MISO q }
[0029] It can be seen that the rule base can be regarded as composed of q sub-rule bases, and each sub-rule base is composed of n multiple input single output (MISO) rules. Since each sub-rule is independent of each other, it is only necessary to consider the approximate reasoning problem of one of the MISO sub-rule bases, which is general.
[0030] R = { Y i = 1 n R MIMO i }
[0031] = { Y i = 1 n [ ( A i × . . . . . . × B i ) → ( Z i × . . . . . . × Z q ) ] }
[0032] In a fuzzy system with two inputs and one output,
[0033] Input: x is A’ and y is B’,
[0034] R 1 : If x is A 1 and y is B 1 , Then z is C 1 ,
[0035] Same R 2 : If x is A 2 and y is B 2 , Then z is C 2
[0036]...
[0037] Derive R n : If x is An and y is B n , Then z is Cn,
[0038] Output: z is C’.
[0039] Where x, y and z are linguistic variables representing the state and control of the system, A i , B i , C i These are the linguistic values of x, y, and z, and x, y, and z have their own domains X, Y, and Z.
[0040] The implication relation Ri of the fuzzy control rule "if x is An and y is Bn, then z is Cn" is defined as:
[0041] Ri=(Ai and Bi)→Ci, namely
[0042] μ R i = μ ( A i and B i → C i ) ( x , y , z )
[0043] = [ μ A i ( x ) and μ B i ( y ) ] → μ C i ( z )
[0044] “Ai and Bi” is the fuzzy set Ai×Bi defined on X×Y, Ri=(Ai and Bi)→Ci, which is the fuzzy implication relationship defined on X×Y×Z, considering n fuzzy control rules The total fuzzy implication relation is (take the connective "also" to seek the combined operation R = Y i = 1 n R i Count):
[0045] Finally, the conclusion of the reasoning is:
[0046] C′=(A′and B′)oR
[0047] among them
[0048] μ (A′and B′) (x,y)=μ A′ (x)∧μ B′ (y)
[0049] or
[0050] "O" is a composite operator, usually the largest
[0051] μ (Aa′nd B′) (x,y)=μ A′ (x)μ B′ (y)-Minimal synthesis method.
[0052] After the fuzzy logic reasoning theory conclusions are combined with actual experience, the intelligent dispatching real-time query control table of the trucks is obtained, as shown in Table 6.
[0053] C
[0054] The waiting time of the truck is an important factor in the actual on-site operation. Theoretically, this factor can be ignored in the whole-site scheduling of the truck, but it should be integrated in the actual operation. Therefore, the intelligent all-on-site intelligent dispatch of the truck comprehensively considers the waiting time of the truck The control expression is: Ct=C·t n , N is obtained by category experiment.
[0055] If there are n trucks with the same number of shortages, distance points, and waiting time, this problem is called a singularity problem. The following methods are used to solve the singularity problem: 1. Determined by the priority difference of n trucks, that is, if Pi>Pj, the trucks will be dispatched to the destination i first; 2. Random selection, random selection means that the computer randomly selects Select the collection card.
[0056] The number of gantry cranes is divided into 0, 1, 2, 3, which is the domain of the number of gantry cranes z={1,2,3,4,5,6,7,≥7}, thus establishing a correspondence with 4 variables Describe the fuzzy set N, N1, N2, N3 of the number of gantry cranes, determine the membership function of the fuzzy set, see Table 7.
[0057] 0
[0058] The number of trucks waiting under the gantry crane is divided into 0, 1, 2, 3, 4, 5, 6, or ≥ 7. The description of the number of trucks waiting for work and its quantitative level are shown in Table 8. The number of trucks waiting under the gantry crane The domain z={0,1,2,3,4,5,6,≥7}, thereby establishing a fuzzy set Q1, Q2, Q3, Q4 with 5 variables correspondingly describing the number of trucks waiting under the gantry crane, Q5, determine the membership function of the fuzzy set, see Table 9.
[0059] The number of cards waiting to be collected under the gantry crane (important
[0060] 0
[0061] The rules for intelligent container stacking are considered in order for imported (ship-to-yard) containers: 1. Whether there are tire cranes in the container area and their number, 2. The number of trucks waiting in the container area with tire cranes, and 3. The container to be operated is Heavy container or light container, 4. Whether the container to be operated is 20 feet or 40 feet; the main considerations for export (crossing to yard) containers: 1. Ship name, 2. Port, 3. Whether the container to be operated is 20 feet or 40 feet Ruler, whether it is a special box. According to the fuzzy control rule expression: R i : If x is A i ,……And y is B i , Then z=C i (x,……y) Establish a container fuzzy stacking rule database including the fuzzy set of gantry crane quantity and the fuzzy set of response speed, where: R i For fuzzy control rules, x, y, and z are linguistic variables representing the system state and control quantity, A i , B i , C i These are the linguistic values of x, y, and z, and x, y, and z have their own domains. See Table 10 for the fuzzy container stacking rule base.
[0062] Table 10. Fuzzy container stacking rule base table
[0063]
[0064] Through the theoretical derivation of the container fuzzy stacking and the actual experience correction, the container intelligent stacking query control table is obtained.
[0065]A container terminal management system includes a wireless network covering the entire terminal area, wireless terminal equipment on operating machinery, a number of computer information processing devices, and related fuzzy logic inference program modules. The container terminal management system framework is a three-layer system three-dimensional structure and a continuously expandable plane structure. The three-layer structure of the system is: business processing layer, comprehensive management layer and management decision support layer. These three layers reflect the various business/management elements of this optimization project and the application layers of the information system. In fact, at each system level, separate subsystems and sub-systems need to be further integrated. In the standardization of business management, it is necessary to ensure data consistency and standard processing procedures, and it is necessary to ensure the unity of basic data and code. . At the same time, with the deepening of management and the advancement of technical means, the application of each system level can be continuously expanded, which can expand the connotation of the entire container terminal management system. See Figure 1 for the logical processing flow of the three-tier structure of the container terminal management system.
