CN114048994A - Method capable of evaluating delay conduction characteristics of multi-stage operation super-network of automatic wharf - Google Patents
Method capable of evaluating delay conduction characteristics of multi-stage operation super-network of automatic wharf Download PDFInfo
- Publication number
- CN114048994A CN114048994A CN202111323724.6A CN202111323724A CN114048994A CN 114048994 A CN114048994 A CN 114048994A CN 202111323724 A CN202111323724 A CN 202111323724A CN 114048994 A CN114048994 A CN 114048994A
- Authority
- CN
- China
- Prior art keywords
- network
- nodes
- conduction
- node
- wharf
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
Abstract
The invention relates to a method capable of evaluating the delay and conduction characteristics of a multi-stage operation super-network of an automatic wharf. And analyzing the characteristics of the super-network structure from the aspects of characteristic path length, clustering coefficient and susceptibility-infection-susceptibility simulation. The conduction characteristics of the delay in the multi-level operation super network are revealed from the aspects of conduction speed, conduction width and conduction capacity. The result of the invention shows that the delay can be conducted faster and wider in the multi-stage operation super-network due to the shorter conduction path and the larger clustering coefficient, the conduction diffusion of the fault node with the smaller clustering coefficient can be inhibited by the clustering characteristic, the conduction capability of the multi-stage operation super-network is increased along with the increase of the number of the initial fault nodes and is reduced along with the increase of the repair probability, thereby being beneficial to a manager to actively cope with the delay conduction and improving the operation efficiency of a port.
Description
Technical Field
The invention relates to a method for delaying the conduction characteristic of a multi-stage operation super network of an automatic wharf.
Background
The automatic wharf is used as an important logistics node for connecting the container transportation on water and land, and the operating efficiency of the automatic wharf directly influences the smoothness degree of a container transportation system. In the actual operation process, various uncertain events such as ship delay, severe weather, AGV driving conflict and the like can not only cause the interruption of the current operation and delay, but also can be conducted between the upper and lower-stage operations of the wharf even the delay, so that a 'linkage' effect is formed. With the lapse of time, the problems of supply chain management such as increased time cost, idle equipment resources, increased container retention, delayed delivery, and disordered operation caused by delay are more and more serious, and the production efficiency of the operation of the automated wharf is affected.
The automatic wharf is a complex system, multi-stage operation is interactively coupled, all stages of operations are mutually influenced, planning and scheduling are carried out from a certain link alone, and the influence of delay conduction on the whole wharf cannot be well analyzed and described. With the development of network science theories such as graph theory, complex network and the like, the method of using the network to discuss the connotation and the characteristics of the complex system gradually becomes new research content and development direction. The topology of a complex network is a general graph in which a node represents some entity and an edge represents some connection that exists between two nodes. To date, the study of complex networks in transportation has yielded a great deal of work. Because an edge only can contain two nodes in a common graph, the method has the defects in describing the complex relation of a real network. Therefore, the learners find that the hypergraph-based hypergraph network has good advantages in this respect, and can simply and accurately depict the complex relationships among various nodes and the multi-element structure of the real network. At present, the application and research of the hyper-network theory mainly focuses on the directions of traffic network, knowledge learning, network evaluation and the like, and the application in the multistage operation of the automatic wharf is less.
Disclosure of Invention
The invention discloses a method capable of evaluating the characteristics of delay transmission of a multi-stage operation super-network of an automatic wharf, which is beneficial for a manager to actively cope with the delay transmission and improve the operation efficiency of a port.
In order to achieve the above object, the present invention provides a method for evaluating the delay and conduction characteristics of a multi-stage operation super-network of an automated wharf, comprising the following steps:
s1, constructing a multistage operation hyper-network model of the automatic wharf by taking operation equipment as nodes and taking operation tasks as hyper-edges and adding fault nodes according to the production operation characteristics of the automatic wharf;
step S2, analyzing the characteristics of the super-network structure from the aspects of characteristic path length, clustering coefficient, susceptibility-infection-susceptibility simulation;
step S3 reveals the conduction characteristics of the delay in the multi-stage operation super network from the perspective of conduction speed, conduction breadth, and conduction capability.
The step S1 includes the following steps:
s1.1, automatically carrying out production operation characteristics of a wharf;
the automatic wharf comprises four regions, namely a shoreside region, a transportation region storage yard region and a land side region, and mainly performs multistage operation such as loading and unloading of a shore bridge, transport of an AGV in a storage yard, loading and unloading of a yard inner bridge, delivery of an outer container truck and the like. The characteristics of multiple resource and multiple links of the automatic wharf enable the multistage operations of the wharf to be mutually linked and influenced, and the characteristics of networking are presented.
In the actual operation process of the wharf, various uncertain events such as ship delay, severe weather, AGV driving conflict and the like can not only cause the interruption of the current operation and delay, but also can be conducted between the upper-stage operation and the lower-stage operation of the wharf even so as to form a 'linkage' effect. And as time goes on, supply chain management problems such as time cost increase, equipment resource idling, container detention increase, delivery delay, operation disorder and the like caused by delay are more and more serious, and the production efficiency of ports is influenced.
S1.2, adding a 'fault node' by taking the operation equipment as a node and taking the operation task as a super edge;
the method takes AGV, shore bridge, yard bridge and other operation equipment as nodes and takes operation tasks as over edges. When the task is finished, releasing the nodes contained in the task, and defaulting the 'failure nodes' in the task to be repaired 'normal nodes' in the network next time; the node frequently appears in a plurality of job tasks, and the higher the conduction probability of the node is when delay occurs; one super edge is added in each time step.
And S1.3, constructing a multi-stage operation hyper-network model of the automatic wharf.
Step S1.3.1, initialization: m nodes and a super edge E containing m nodes1；
Step S1.3.2, super edge growing: each time step is added with a super edge which comprises n new nodes and has m with the old super edge0The nodes are coincident, i.e. the new super edge contains n new nodes and m0Old nodes, n and m0Given by the poisson probability;
step S1.3.3, network formation: judging whether all tasks are finished, if so, forming a network, and if not, returning to the step S1.3.2;
delays in the automated terminal multi-stage operations network due to the occurrence of uncertain events such as equipment failures, severe weather conditions, increased loading and unloading, and ship delays affect multiple nodes within the terminal, which is generally represented in step S1.3.2. When a 'fault node' appears in the network, the 'fault node' influences the operation of a 'normal node' with a probability beta, namely the 'normal node' becomes the 'fault node' after being infected, and the number of the 'fault nodes' in the network is increased; on the other hand, the "failed node" is recovered with a probability γ. And (4) completing a part of job tasks along with the increase of time, exiting the network by the super edge at the moment, and re-releasing the original nodes in the super edge to wait for re-entering the network along with the new super edge.
The step S2 includes the following steps:
s2.1, analyzing the structural characteristics of the multi-stage operation super-network of the automatic wharf from the aspect of characteristic path length;
the operations at all levels of the automatic wharf are closely related, the nodes have shorter characteristic path length, and the characteristics of the small-world network are more obvious because the multi-level operation network does not have preferential connection.
The characteristic path length is the average of the distance between any two nodes in the network. If the 'fault node' infects the 'normal node' with the probability beta when a new excess edge is added at the moment t, the 'fault node' will infect the 'normal node' with the speed of beta s (t) at the momentGenerating connection with nodes in the network, wherein s (t) represents the number of normal nodes in the network at the time t, and is equal to the total number of nodes in the network minus the number of fault nodes, and the number of the fault nodes is i (t); n is the total number of nodes in the network, the new excess edge is adjacent to the old excess edge by the probability p, and the corrected characteristic path is
Wherein f (p β s (t) i (t)) is a general scaling function satisfying:
s2.2, analyzing the structural characteristics of the multi-stage operation super-network of the automatic wharf from the aspect of clustering coefficients;
the clustering coefficient represents the aggregation degree among nodes in the network, and is one of global standard parameters for representing the complex network topology. The larger the clustering coefficient of the node v is, the greater the tightness between the node v and other nodes is, which indicates that the more the conducting paths are, the wider the conducting range is, so that the clustering coefficient is one of the important indexes for describing the structural characteristics of the automatic wharf multi-stage operation super-network. The clustering coefficient C for the whole network is generally expressed as
Wherein k isiFor node excess of node v, node v passes kiThe total number of other nodes connected by the bar excess edge is called the node degree of the node v. e.g. of the typeiThe number of edges that the node v actually connects to its neighbor nodes. Usually C ranges from 0 to 1, when C is 0, it indicates that all nodes are isolated nodes, and no conduction condition exists between the nodes; when C ═ 1, it indicates that there is direct contact between any two nodes in the network. Both of these cases do not conform to the automated wharf multi-level operations super-network model built herein. The automatic wharf multi-stage operation hyper-network model is neither completely random nor completely regular, and accords with the characteristics of a small-world network. Expressing the clustering coefficient by the conduction probability beta, simplifying the operation, and correctingThe cluster coefficient is expressed as
And S2.3, analyzing the structural characteristics of the multi-stage operation super-network of the automatic wharf from the aspects of susceptibility-infection-susceptibility simulation.
The transmission capability, namely the effective transmission rate of the fault node, delays the transmission problem in the multistage operation of the automatic wharf according to the characteristics of the transmission dynamic Model, and is suitable for solving a Susceptible-infectious-Susceptible Model (SIS Model). That is, after a healthy node becomes a failed node after being infected with a certain probability, the healthy node is restored to a healthy node with a certain probability. SIS kinetic equation of
Wherein the content of the first and second substances,
represents the proportion of normal nodes at the moment t, namely S-state (normal state) density; s (t) is a set of all normal nodes;
represents the proportion of the fault node at the time t, namely the density of I state (fault state); i (t): a set of all failed nodes; gamma is the repair probability of the failed node, i.e. the efficiency of the failed node being detected and maintained and becoming a healthy node again. The higher the repair probability, the faster the failed node can re-enter the network. After derivation, the equation is solved as
Wherein, I0Is the number of initial failed nodes. In a practical network, when network nodes interact with each other due to certain factors to form conduction behaviors, simulation can be carried out by using an infectious disease model. In contrast, since the model of infectious diseases is a continuous function, it needs to be discretized when applied to the multi-stage operation of the automated wharf to be close to the actual situation, so the model can be adjusted to be
The step S3 includes the following steps:
s3.1, revealing the conduction characteristic of delaying in the multi-stage operation hyper-network from the perspective of conduction speed;
the smaller the characteristic path length l (g), i.e. the shorter the paths through which the different nodes can be connected, the faster the delayed conduction speed. When the number i (t) of the fault nodes is not changed, the characteristic path length l (g) curve gradually moves to the upper right direction along with the increase of the total number N of the network nodes, that is, the characteristic path length l (g) gradually increases along with the increase of the total number N of the network nodes, the characteristic path length l (g) gradually increases, different two nodes need to be connected through a longer path, the conduction speed is reduced, and the negative correlation relationship exists between the conduction speed and the total number N of the network nodes.
The different numbers of failed nodes i (t) also have an effect on the variation of the characteristic path length l (g) of the network. Under the condition that the total number N of the nodes of the network is not changed, along with the increase of the number i (t) of the fault nodes, the characteristic path length L (G) is gradually reduced, namely, the two different nodes can be linked through a shorter path, and the conduction speed is increased. The positive correlation between the conduction speed and the number i (t) of the fault nodes is shown.
For a wharf manager, by adding equipment, improving the modernization level, responding to the rapidly changing automatic wharf operation environment, constructing a complete operation system, reasonably allocating idle equipment resources when delay occurs, and extracting a fault node out of the network, the delay conduction speed can be effectively controlled, and the operation efficiency is improved.
S3.2, revealing the conduction characteristic of the delay in the multi-stage operation hyper-network from the perspective of the conduction width;
the clustering coefficient represents the tightness degree among all nodes in the network, the larger the clustering coefficient is, the tighter the connection among the nodes in the network is, and the wider the conduction range of the fault node in the network is. The clustering coefficient C gradually increases with the number of failed nodes i (t) and decreases with the increase of the conduction probability β. That is, if the number i (t) of fault nodes in the network is large, the propagation range of the delay risk is expanded, and if the propagation probability is too high, the propagation range is narrowed.
The change is very powerful in representing that in the operation of the automatic wharf, the close connection of the operation of each link can help the wharf to finish the operation more quickly and efficiently, but when an uncertain event occurs, the diffusion and the conduction of delay in the wharf operation caused by the uncertain event can be accelerated. For the wharf manager, the decision level can be improved by enhancing the level of information sharing and information transmission. When a fault node occurs in operation, a new relation is established through effective information transmission and sharing, the overall cooperation degree of the network is enlarged, the clustering characteristic of the fault node is reduced, and the delayed conduction range is narrowed.
Step S3.3 reveals the conductance characteristics of the delay in the multi-stage operation super network from a conductance perspective.
The conduction process is affected by the number of initially failed nodes, the probability of repair, and the total number of nodes. SIS simulation is carried out through different variable control, and the conduction characteristic of an automatic wharf operation network is discussed. While the repair probability gamma is kept constant, the number of nodes I is varied according to the initial failure0The number of infected nodes is increasing, the conductivity is increasing, and the time for completing the SIS transmission process is shorter.
As the repair summary γ increases, the conductivity of the failed node is inhibited. And the inhibition effect is gradually obvious, when the repair probability gamma is gradually the same as the conduction probability beta, namely, the fault node is repaired in time, the fault node cannot influence the operation of the normal node, and the conduction capability of the fault node is 0 at the moment.
In the multi-stage operation super network of the automatic wharf, the increase of the number of the super edges means the increase of wharf operation tasks, the more tasks, and the larger the network scale. Number of excess edges EiThe increase or decrease of (a) is similar to the change of the total number of nodes N of the network. The number of nodes in the network increases, the length of the characteristic path increases, the conduction speed is slower, and the conduction capability is reduced.
The results of the invention show that:
(1) the conduction speed of the network is in negative correlation with the number of network nodes and in positive correlation with the number of fault nodes; under the condition that the total number N of the network nodes is fixed, the more the number of the fault nodes is, the faster the delay conduction is. On the other hand, when the total number N of nodes in the network increases, in actual operation, it can be understood that the replaceable selection space of the failed node is larger, that is, the failed node is replaced by other nodes in the network, and therefore, the conduction velocity is suppressed.
(2) Under the same conduction probability, the larger the number of fault nodes is, the larger the aggregation coefficient of the network is, the wider the conduction range of the network is, and the low clustering characteristic can inhibit delayed conduction.
(3) The greater the number of initial failure nodes, the greater the conductivity and the shorter the time to complete the SIS propagation process. And higher divisions when the probability of conduction is lower. The repair probability is in a negative correlation with the conductivity, and the higher the repair probability is, the lower the conductivity is.
Drawings
FIG. 1 is a schematic flow chart of a method for evaluating the delay and conduction characteristics of a multi-stage operation super network of an automated terminal according to the present invention;
FIG. 2 is an automated terminal multi-level operations network;
FIG. 3 is a schematic diagram of the evolution of a multi-stage operation hyper-network model of an automation terminal;
FIG. 4 is a graph showing the variation trend of the characteristic path length with the total number of nodes and the number of failed nodes of the network;
FIG. 5 is a graph of the variation of transmission probability versus infection density;
FIG. 6 is a graph of conduction at different initially failed nodes at different conduction probabilities;
FIG. 7 is a graph of repair probability versus conductivity.
Detailed Description
The preferred embodiment of the present invention will be described in detail below with reference to fig. 1 to 7.
As shown in fig. 1, the present invention provides a method for evaluating the delay and conduction characteristics of a multi-stage operation super network of an automated wharf, comprising the following steps:
s1, constructing a multistage operation hyper-network model of the automatic wharf by taking operation equipment as nodes and taking operation tasks as hyper-edges and adding fault nodes according to the production operation characteristics of the automatic wharf;
step S2, analyzing the characteristics of the super-network structure from the aspects of characteristic path length, clustering coefficient, susceptibility-infection-susceptibility simulation;
step S3 reveals the conduction characteristics of the delay in the multi-stage operation super network from the perspective of conduction speed, conduction breadth, and conduction capability.
Further, the step S1 includes the following steps:
s1.1, automatically carrying out production operation characteristics of a wharf;
the automatic wharf comprises four regions, namely a shoreside region, a transportation region storage yard region and a land side region, and mainly performs multistage operation such as loading and unloading of a shore bridge, transport of an AGV in a storage yard, loading and unloading of a yard inner bridge, delivery of an outer container truck and the like. The multi-resource and multi-link characteristics of the automatic wharf enable the multistage operations of the wharf to be mutually linked and influenced, and the characteristics of networking are shown in fig. 2.
In the actual operation process of the wharf, various uncertain events such as ship delay, severe weather, AGV driving conflict and the like can not only cause the interruption of the current operation and delay, but also can be conducted between the upper-stage operation and the lower-stage operation of the wharf even so as to form a 'linkage' effect. And as time goes on, supply chain management problems such as time cost increase, equipment resource idling, container detention increase, delivery delay, operation disorder and the like caused by delay are more and more serious, and the production efficiency of ports is influenced.
S1.2, adding a 'fault node' by taking the operation equipment as a node and taking the operation task as a super edge;
the method takes AGV, shore bridge, yard bridge and other operation equipment as nodes and takes operation tasks as over edges. When the task is finished, releasing the nodes contained in the task, and defaulting the 'failure nodes' in the task to be repaired 'normal nodes' in the network next time; the node frequently appears in a plurality of job tasks, and the higher the conduction probability of the node is when delay occurs; one super edge is added in each time step.
And S1.3, constructing a multi-stage operation hyper-network model of the automatic wharf.
The step S1.3 specifically includes the following steps:
step S1.3.1, initialization: m nodes and a super edge E containing m nodes1；
Step S1.3.2, super edge growing: each time step is added with a super edge which comprises n new nodes and has m with the old super edge0The nodes are coincident, i.e. the new super edge contains n new nodes and m0Old nodes, n and m0Given by the poisson probability;
step S1.3.3, network formation: judging whether all tasks are finished, if so, forming a network, and if not, returning to the step S1.3.2;
delays in the automated terminal multi-stage operations network due to the occurrence of uncertain events such as equipment failures, severe weather conditions, increased loading and unloading, and ship delays affect multiple nodes within the terminal, which is generally represented in step S1.3.2. When a 'fault node' appears in the network, the 'fault node' influences the operation of a 'normal node' with a probability beta, namely the 'normal node' becomes the 'fault node' after being infected, and the number of the 'fault nodes' in the network is increased; on the other hand, the "failed node" is recovered with a probability γ. And (4) completing a part of job tasks along with the increase of time, exiting the network by the super edge at the moment, and re-releasing the original nodes in the super edge to wait for re-entering the network along with the new super edge.
Fig. 3 shows a schematic diagram of the evolution model of the automatic wharf multi-stage operation super network at an initial time m-4, β -0.2, and γ -0.1. At T0Only one super edge E in the time network1And comprises 4 nodes, T2New time edge exceeding E2Join, generate a new node V5、V6、V7. Node V due to the occurrence of an indeterminate event3And the delay occurs, and the node becomes a fault node. T is2New time edge exceeding E3Increase if E3The job in (1) does not involve node V3I.e. E3Does not include a "failed node", conduction is suspended, e.g., T2The time of day is shown in case (1). Otherwise conduction continues, e.g. T2The time of day is shown in case (2). At this time V3Infect V with probability beta7So that node V7Becomes a 'fault node', on the other hand, a wharf manager can repair the 'fault node' according to the probability gamma, and after the completion of all tasks after repeated for a plurality of times, T is finally formediA temporal super network.
The step S2 includes the following steps:
s2.1, analyzing the structural characteristics of the multi-stage operation super-network of the automatic wharf from the aspect of characteristic path length;
the operations at all levels of the automatic wharf are closely related, the nodes have shorter characteristic path length, and the characteristics of the small-world network are more obvious because the multi-level operation network does not have preferential connection.
The characteristic path length is the average of the distance between any two nodes in the network. Supposing that the 'fault node' infects the 'normal node' with the probability beta when a new excess edge is added at the time t, the 'fault node' is linked with the nodes in the network at the time beta s (t), s (t) represents the number of the normal nodes in the network at the time t, and is equal to the number of the total nodes in the network minus the number of the fault nodes, and the number of the fault nodes is i (t); n is the total number of nodes in the network, the new excess edge is adjacent to the old excess edge by the probability p, and the corrected characteristic path is
Wherein f (p β s (t) i (t)) is a general scaling function satisfying:
s2.2, analyzing the structural characteristics of the multi-stage operation super-network of the automatic wharf from the aspect of clustering coefficients;
the clustering coefficients represent the clustering horizon between nodes in the networkDegree, is one of the global standard parameters characterizing complex network topologies. The larger the clustering coefficient of the node v is, the greater the tightness between the node v and other nodes is, which indicates that the more the conducting paths are, the wider the conducting range is, so that the clustering coefficient is one of the important indexes for describing the structural characteristics of the automatic wharf multi-stage operation super-network. The clustering coefficient C for the whole network is generally expressed as
Wherein k isiFor node excess of node v, node v passes kiThe total number of other nodes connected by the bar excess edge is called the node degree of the node v. e.g. of the typeiThe number of edges that the node v actually connects to its neighbor nodes. Usually C ranges from 0 to 1, when C is 0, it indicates that all nodes are isolated nodes, and no conduction condition exists between the nodes; when C ═ 1, it indicates that there is direct contact between any two nodes in the network. Both of these cases do not conform to the automated wharf multi-level operations super-network model built herein. The automatic wharf multi-stage operation hyper-network model is neither completely random nor completely regular, and accords with the characteristics of a small-world network. The clustering coefficient is expressed by the conduction probability beta and simplified operation, and the corrected clustering coefficient is expressed as
And S2.3, analyzing the structural characteristics of the multi-stage operation super-network of the automatic wharf from the aspects of susceptibility-infection-susceptibility simulation.
The transmission capability, namely the effective transmission rate of the fault node, delays the transmission problem in the multistage operation of the automatic wharf according to the characteristics of the transmission dynamic Model, and is suitable for solving a Susceptible-infectious-Susceptible Model (SIS Model). That is, after a healthy node becomes a failed node after being infected with a certain probability, the healthy node is restored to a healthy node with a certain probability. SIS kinetic equation of
Wherein the content of the first and second substances,
represents the proportion of normal nodes at the moment t, namely S-state (normal state) density; s (t) is a set of all normal nodes;
represents the proportion of the fault node at the time t, namely the density of I state (fault state); i (t): a set of all failed nodes; gamma is the repair probability of the failed node, i.e. the efficiency of the failed node being detected and maintained and becoming a healthy node again. The higher the repair probability, the faster the failed node can re-enter the network. After derivation, the equation is solved as
Wherein, I0Is the number of initial failed nodes. In a practical network, when network nodes interact with each other due to certain factors to form conduction behaviors, simulation can be carried out by using an infectious disease model. In contrast, since the model of infectious diseases is a continuous function, it needs to be discretized when applied to the multi-stage operation of the automated wharf to be close to the actual situation, so the model can be adjusted to be
The step S3 includes the following steps:
s3.1, revealing the conduction characteristic of delaying in the multi-stage operation hyper-network from the perspective of conduction speed;
the facilities and equipment of the automatic wharf are divided into large scale and small scale according to different quantities and scales. For example, the mansion open sea container terminal is currently provided with 3 automatic double-trolley shore bridges, 16 yard bridges, 18 AGV, 8 automatic container transfer platforms and the like; whereas the four-season terminal in the mountains is expected to eventually configure 130 AGVs, 26 shore bridges and 120 yard bridges as planned. In order to represent the change of the conduction velocity under different node numbers, when the total node number N of the network is set to be 50, 100 and 200 respectively, the change relationship between the characteristic path length l (g) and the conduction probability β is simulated, as shown in fig. 4.
The smaller the characteristic path length l (g), i.e. the shorter the paths through which the different nodes can be connected, the faster the delayed conduction speed. When the number i (t) of the fault nodes is not changed, the characteristic path length l (g) curve gradually moves to the upper right direction along with the increase of the total number N of the network nodes, that is, the characteristic path length l (g) gradually increases along with the increase of the total number N of the network nodes, the characteristic path length l (g) gradually increases, different two nodes need to be connected through a longer path, the conduction speed is reduced, and the negative correlation relationship exists between the conduction speed and the total number N of the network nodes.
The different numbers of failed nodes i (t) also have an effect on the variation of the characteristic path length l (g) of the network. Under the condition that the total number N of the nodes of the network is not changed, along with the increase of the number i (t) of the fault nodes, the characteristic path length L (G) is gradually reduced, namely, the two different nodes can be linked through a shorter path, and the conduction speed is increased. The positive correlation between the conduction speed and the number i (t) of the fault nodes is shown.
For a wharf manager, by adding equipment, improving the modernization level, responding to the rapidly changing automatic wharf operation environment, constructing a complete operation system, reasonably allocating idle equipment resources when delay occurs, and extracting a fault node out of the network, the delay conduction speed can be effectively controlled, and the operation efficiency is improved.
S3.2, revealing the conduction characteristic of the delay in the multi-stage operation hyper-network from the perspective of the conduction width;
the clustering coefficient represents the tightness degree among all nodes in the network, the larger the clustering coefficient is, the tighter the connection among the nodes in the network is, and the wider the conduction range of the fault node in the network is. According to
The relationship between the clustering coefficient C and the conduction probability β and the number of fault nodes i (t) is simulated, and the simulation result is shown in fig. 5. As can be seen from FIG. 5, the clustering coefficient C increases with the number of failed nodes i (t)Increasing and decreasing with increasing conduction probability beta. This is because the internal connections between nodes in the network limit the process of conduction diffusion, and the low order nature inhibits the conduction diffusion of the failed node. That is, if the number i (t) of fault nodes in the network is large, the propagation range of the delay risk is expanded, and if the propagation probability is too high, the propagation range is narrowed. In this process, although the low clustering coefficient can suppress propagation, since the number of affected normal nodes is continuously enlarged and the number of affected normal nodes (effective contact rate of fault nodes) is increased per unit time, the suppression effect of the low clustering characteristic of the network on delayed conduction is weakened.
The change is very powerful in representing that in the operation of the automatic wharf, the close connection of the operation of each link can help the wharf to finish the operation more quickly and efficiently, but when an uncertain event occurs, the diffusion and the conduction of delay in the wharf operation caused by the uncertain event can be accelerated. For the wharf manager, the decision level can be improved by enhancing the level of information sharing and information transmission. When a fault node occurs in operation, a new relation is established through effective information transmission and sharing, the overall cooperation degree of the network is enlarged, the clustering characteristic of the fault node is reduced, and the delayed conduction range is narrowed.
Step S3.3 reveals the conductance characteristics of the delay in the multi-stage operation super network from a conductance perspective.
By
The available conduction processes are affected by the number of initially failed nodes, the probability of repair, and the total number of nodes. SIS simulation is carried out through different variable control, and the conduction characteristic of an automatic wharf operation network is discussed.
Fig. 6 simulates the initial number of failed nodes I when the total number of nodes N in the network is 1000A non-simultaneous conduction situation. The results of fig. 6 show that the number of nodes I with initial failure while the repair probability γ remains constant0The number of infected nodes is increased, and the conduction capability is increasedThe stronger the SIS propagation process, the shorter the time to complete. When beta is less than or equal to 0.45, the beta is greatly changed, and the beta belongs to [0.45,0.65 ]]The change is weak, and the change is not obvious when beta is more than or equal to 0.75. Therefore, the conduction capability and the conduction probability are positively correlated when the number of different fault nodes is the initial node, and the degree is higher when the conduction probability is smaller.
Fig. 7 shows SIS simulation performed when the repair probability γ is a variable, the total number of nodes N of the control network is 100, and the propagation probability β is 0.45. The results in fig. 7 show that when the repair probability γ is smaller, the conduction capability of the failed node in the network is stronger. As the repair summary γ increases, the conductivity of the failed node is inhibited. And the inhibition effect is gradually obvious, when the repair probability gamma is gradually the same as the conduction probability beta, namely, the fault node is repaired in time, the fault node cannot influence the operation of the normal node, and the conduction capability of the fault node is 0 at the moment.
In the multi-stage operation super network of the automatic wharf, the increase of the number of the super edges means the increase of wharf operation tasks, the more tasks, and the larger the network scale. Number of excess edges EiThe increase or decrease of (a) is similar to the change of the total number of nodes N of the network. The number of nodes in the network increases, the length of the characteristic path increases, the conduction speed is slower, and the conduction capability is reduced.
In summary, the method result of the invention for evaluating the delay conduction characteristic of the automatic wharf multi-stage operation super-network shows that the delay can be conducted faster and wider in the multi-stage operation super-network due to the shorter conduction path and the larger clustering coefficient, the conduction spread of the fault node with the smaller clustering coefficient can be inhibited by the clustering characteristic, the conduction capability of the multi-stage operation super-network is increased along with the increase of the number of the initial fault nodes and is reduced along with the increase of the repair probability, thereby being beneficial for a manager to actively cope with the delay conduction and improving the operation efficiency of the port.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (4)
1. A method for evaluating the delay and conduction characteristics of a multi-stage operation super network of an automatic wharf is characterized by comprising the following steps:
s1, constructing a multistage operation hyper-network model of the automatic wharf by taking operation equipment as nodes and taking operation tasks as hyper-edges and adding fault nodes according to the production operation characteristics of the automatic wharf;
step S2, analyzing the characteristics of the super-network structure from the aspects of characteristic path length, clustering coefficient, susceptibility-infection-susceptibility simulation;
step S3 reveals the conduction characteristics of the delay in the multi-stage operation super network from the perspective of conduction speed, conduction breadth, and conduction capability.
2. The method of claim 1, wherein said step S1 comprises the steps of:
s1.1, automatically carrying out production operation characteristics of a wharf;
the automatic wharf comprises four regions, namely a shoreside region, a transportation region storage yard region and a land side region, and mainly performs multistage operation such as loading and unloading of a shore bridge, transport of an AGV in a storage yard, loading and unloading of a yard inner bridge, delivery of an outer container truck and the like. The characteristics of multiple resource and multiple links of the automatic wharf enable the multistage operations of the wharf to be mutually linked and influenced, and the characteristics of networking are presented.
In the actual operation process of the wharf, various uncertain events such as ship delay, severe weather, AGV driving conflict and the like can not only cause the interruption of the current operation and delay, but also can be conducted between the upper-stage operation and the lower-stage operation of the wharf even so as to form a 'linkage' effect. And as time goes on, supply chain management problems such as time cost increase, equipment resource idling, container detention increase, delivery delay, operation disorder and the like caused by delay are more and more serious, and the production efficiency of ports is influenced.
S1.2, adding a 'fault node' by taking the operation equipment as a node and taking the operation task as a super edge;
the method takes AGV, shore bridge, yard bridge and other operation equipment as nodes and takes operation tasks as over edges. When the task is finished, releasing the nodes contained in the task, and defaulting the 'failure nodes' in the task to be repaired 'normal nodes' in the network next time; the node frequently appears in a plurality of job tasks, and the higher the conduction probability of the node is when delay occurs; one super edge is added in each time step.
And S1.3, constructing a multi-stage operation hyper-network model of the automatic wharf.
Step S1.3.1, initialization: m nodes and a super edge E containing m nodes1；
Step S1.3.2, super edge growing: each time step is added with a super edge which comprises n new nodes and has m with the old super edge0The nodes are coincident, i.e. the new super edge contains n new nodes and m0Old nodes, n and m0Given by the poisson probability;
step S1.3.3, network formation: judging whether all tasks are finished, if so, forming a network, and if not, returning to the step S1.3.2;
delays in the automated terminal multi-stage operations network due to the occurrence of uncertain events such as equipment failures, severe weather conditions, increased loading and unloading, and ship delays affect multiple nodes within the terminal, which is generally represented in step S1.3.2. When a 'fault node' appears in the network, the 'fault node' influences the operation of a 'normal node' with a probability beta, namely the 'normal node' becomes the 'fault node' after being infected, and the number of the 'fault nodes' in the network is increased; on the other hand, the "failed node" is recovered with a probability γ. And (4) completing a part of job tasks along with the increase of time, exiting the network by the super edge at the moment, and re-releasing the original nodes in the super edge to wait for re-entering the network along with the new super edge.
3. The method of claim 1, wherein said step S2 comprises the steps of:
s2.1, analyzing the structural characteristics of the multi-stage operation super-network of the automatic wharf from the aspect of characteristic path length;
the operations at all levels of the automatic wharf are closely related, the nodes have shorter characteristic path length, and the characteristics of the small-world network are more obvious because the multi-level operation network does not have preferential connection.
The characteristic path length is the average of the distance between any two nodes in the network. Supposing that the 'fault node' infects the 'normal node' with the probability beta when a new excess edge is added at the time t, the 'fault node' is linked with the nodes in the network at the time beta s (t), s (t) represents the number of the normal nodes in the network at the time t, and is equal to the number of the total nodes in the network minus the number of the fault nodes, and the number of the fault nodes is i (t); n is the total number of nodes in the network, the new excess edge is adjacent to the old excess edge by the probability p, and the corrected characteristic path is
Wherein f (p β s (t) i (t)) is a general scaling function satisfying:
s2.2, analyzing the structural characteristics of the multi-stage operation super-network of the automatic wharf from the aspect of clustering coefficients;
the clustering coefficient represents the aggregation degree among nodes in the network, and is one of global standard parameters for representing the complex network topology. The larger the clustering coefficient of the node v is, the greater the tightness between the node v and other nodes is, which indicates that the more the conducting paths are, the wider the conducting range is, so that the clustering coefficient is one of the important indexes for describing the structural characteristics of the automatic wharf multi-stage operation super-network. The clustering coefficient C for the whole network is generally expressed as
Wherein k isiIs a nodeNode excess of v, node v passes kiThe total number of other nodes connected by the bar excess edge is called the node degree of the node v. e.g. of the typeiThe number of edges that the node v actually connects to its neighbor nodes. Usually C ranges from 0 to 1, when C is 0, it indicates that all nodes are isolated nodes, and no conduction condition exists between the nodes; when C ═ 1, it indicates that there is direct contact between any two nodes in the network. Both of these cases do not conform to the automated wharf multi-level operations super-network model built herein. The automatic wharf multi-stage operation hyper-network model is neither completely random nor completely regular, and accords with the characteristics of a small-world network. The clustering coefficient is expressed by the conduction probability beta and simplified operation, and the corrected clustering coefficient is expressed as
And S2.3, analyzing the structural characteristics of the multi-stage operation super-network of the automatic wharf from the aspects of susceptibility-infection-susceptibility simulation.
The transmission capability, namely the effective transmission rate of the fault node, delays the transmission problem in the multistage operation of the automatic wharf according to the characteristics of the transmission dynamic Model, and is suitable for solving a Susceptible-infectious-Susceptible Model (SIS Model). That is, after a healthy node becomes a failed node after being infected with a certain probability, the healthy node is restored to a healthy node with a certain probability. SIS kinetic equation of
Wherein the content of the first and second substances,
represents the proportion of normal nodes at the moment t, namely S-state (normal state) density; s (t) is a set of all normal nodes;
represents the proportion of the fault node at the time t, namely the density of I state (fault state); i (t): a set of all failed nodes; gamma is a failed nodeI.e. how efficiently the failed node is detected to be maintained and becomes a healthy node again. The higher the repair probability, the faster the failed node can re-enter the network. After derivation, the equation is solved as
Wherein, I0Is the number of initial failed nodes. In a practical network, when network nodes interact with each other due to certain factors to form conduction behaviors, simulation can be carried out by using an infectious disease model. In contrast, since the model of infectious diseases is a continuous function, it needs to be discretized when applied to the multi-stage operation of the automated wharf to be close to the actual situation, so the model can be adjusted to be
4. The method of claim 1, wherein said step S3 comprises the steps of:
s3.1, revealing the conduction characteristic of delaying in the multi-stage operation hyper-network from the perspective of conduction speed;
the smaller the characteristic path length l (g), i.e. the shorter the paths through which the different nodes can be connected, the faster the delayed conduction speed. When the number i (t) of the fault nodes is not changed, the characteristic path length l (g) curve gradually moves to the upper right direction along with the increase of the total number N of the network nodes, that is, the characteristic path length l (g) gradually increases along with the increase of the total number N of the network nodes, the characteristic path length l (g) gradually increases, different two nodes need to be connected through a longer path, the conduction speed is reduced, and the negative correlation relationship exists between the conduction speed and the total number N of the network nodes.
The different numbers of failed nodes i (t) also have an effect on the variation of the characteristic path length l (g) of the network. Under the condition that the total number N of the nodes of the network is not changed, along with the increase of the number i (t) of the fault nodes, the characteristic path length L (G) is gradually reduced, namely, the two different nodes can be linked through a shorter path, and the conduction speed is increased. The positive correlation between the conduction speed and the number i (t) of the fault nodes is shown.
For a wharf manager, by adding equipment, improving the modernization level, responding to the rapidly changing automatic wharf operation environment, constructing a complete operation system, reasonably allocating idle equipment resources when delay occurs, and extracting a fault node out of the network, the delay conduction speed can be effectively controlled, and the operation efficiency is improved.
S3.2, revealing the conduction characteristic of the delay in the multi-stage operation hyper-network from the perspective of the conduction width;
the clustering coefficient represents the tightness degree among all nodes in the network, the larger the clustering coefficient is, the tighter the connection among the nodes in the network is, and the wider the conduction range of the fault node in the network is. The clustering coefficient C gradually increases with the number of failed nodes i (t) and decreases with the increase of the conduction probability β. That is, if the number i (t) of fault nodes in the network is large, the propagation range of the delay risk is expanded, and if the propagation probability is too high, the propagation range is narrowed.
The change is very powerful in representing that in the operation of the automatic wharf, the close connection of the operation of each link can help the wharf to finish the operation more quickly and efficiently, but when an uncertain event occurs, the diffusion and the conduction of delay in the wharf operation caused by the uncertain event can be accelerated. For the wharf manager, the decision level can be improved by enhancing the level of information sharing and information transmission. When a fault node occurs in operation, a new relation is established through effective information transmission and sharing, the overall cooperation degree of the network is enlarged, the clustering characteristic of the fault node is reduced, and the delayed conduction range is narrowed.
Step S3.3 reveals the conductance characteristics of the delay in the multi-stage operation super network from a conductance perspective.
The conduction process is affected by the number of initially failed nodes, the probability of repair, and the total number of nodes. SIS simulation is carried out through different variable control, and the conduction characteristic of an automatic wharf operation network is discussed. In the repair probability gamma remains unchangedTime varying, with initial failure node number I0The number of infected nodes is increasing, the conductivity is increasing, and the time for completing the SIS transmission process is shorter.
As the repair summary γ increases, the conductivity of the failed node is inhibited. And the inhibition effect is gradually obvious, when the repair probability gamma is gradually the same as the conduction probability beta, namely, the fault node is repaired in time, the fault node cannot influence the operation of the normal node, and the conduction capability of the fault node is 0 at the moment.
In the multi-stage operation super network of the automatic wharf, the increase of the number of the super edges means the increase of wharf operation tasks, the more tasks, and the larger the network scale. Number of excess edges EiThe increase or decrease of (a) is similar to the change of the total number of nodes N of the network. The number of nodes in the network increases, the length of the characteristic path increases, the conduction speed is slower, and the conduction capability is reduced.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111323724.6A CN114048994A (en) | 2021-11-10 | 2021-11-10 | Method capable of evaluating delay conduction characteristics of multi-stage operation super-network of automatic wharf |
| PCT/CN2022/114198 WO2023082763A1 (en) | 2021-11-10 | 2022-08-23 | Measurement method for delay propagation in multi-level handling hypernetwork of automated container terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111323724.6A CN114048994A (en) | 2021-11-10 | 2021-11-10 | Method capable of evaluating delay conduction characteristics of multi-stage operation super-network of automatic wharf |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114048994A true CN114048994A (en) | 2022-02-15 |
Family
ID=80207902
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111323724.6A Withdrawn CN114048994A (en) | 2021-11-10 | 2021-11-10 | Method capable of evaluating delay conduction characteristics of multi-stage operation super-network of automatic wharf |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN114048994A (en) |
| WO (1) | WO2023082763A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023082763A1 (en) * | 2021-11-10 | 2023-05-19 | 上海海事大学 | Measurement method for delay propagation in multi-level handling hypernetwork of automated container terminal |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101893854B (en) * | 2010-08-10 | 2012-09-05 | 上海海事大学 | Multiple delivering sequence decision-making system for container terminal shipment |
| US20180032955A1 (en) * | 2016-08-01 | 2018-02-01 | Sap Se | System and method of minimizing waiting time in vehicle routing and scheduling with narrow time-windows |
| CN107545366B (en) * | 2017-08-28 | 2020-07-31 | 上海振华重工电气有限公司 | Ship loading scheduling method and system for full-automatic container wharf |
| CN113205444A (en) * | 2021-05-31 | 2021-08-03 | 上海海事大学 | Regional container marine physical layer-task layer-information layer network model |
| CN113361804B (en) * | 2021-06-28 | 2022-10-28 | 同济大学 | Buffer yard operation and inter-dock truck transportation cooperative scheduling system and method thereof |
| CN114048994A (en) * | 2021-11-10 | 2022-02-15 | 上海海事大学 | Method capable of evaluating delay conduction characteristics of multi-stage operation super-network of automatic wharf |
-
2021
- 2021-11-10 CN CN202111323724.6A patent/CN114048994A/en not_active Withdrawn
-
2022
- 2022-08-23 WO PCT/CN2022/114198 patent/WO2023082763A1/en unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023082763A1 (en) * | 2021-11-10 | 2023-05-19 | 上海海事大学 | Measurement method for delay propagation in multi-level handling hypernetwork of automated container terminal |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023082763A1 (en) | 2023-05-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108965014B (en) | QoS-aware service chain backup method and system | |
| CN101807159B (en) | Self-adapting task scheduling method | |
| CN109710374A (en) | The VM migration strategy of task unloading expense is minimized under mobile edge calculations environment | |
| Angeloudis et al. | Security and reliability of the liner container-shipping network: analysis of robustness using a complex network framework | |
| CN103595805A (en) | Data placement method based on distributed cluster | |
| CN108040008B (en) | Cross-domain deployment method of online service function chain | |
| CN101803340B (en) | System and method for balancing information loads | |
| Verstichel et al. | Scheduling algorithms for the lock scheduling problem | |
| CN114048994A (en) | Method capable of evaluating delay conduction characteristics of multi-stage operation super-network of automatic wharf | |
| CN110830570A (en) | Resource equalization deployment method for robust finite controller in software defined network | |
| Yin et al. | Mitigation strategy of cascading failures in urban traffic congestion based on complex networks | |
| Hu et al. | Analyzing interactions between Japanese ports and the Maritime Silk Road based on complex networks | |
| CN106875043B (en) | Node migration network block optimization method based on GN splitting algorithm | |
| Wang et al. | Analysis of price of anarchy in traffic networks with heterogeneous price-sensitivity populations | |
| Tabash et al. | A fuzzy logic based network congestion control using active queue management techniques | |
| Jin et al. | Cyber-physical risk driven routing planning with deep reinforcement-learning in smart grid communication networks | |
| Liu et al. | Area: an automatic runtime evolutionary adaptation mechanism for creating self-adaptation algorithms in wireless networks | |
| CN108600096B (en) | Intra-domain energy-saving routing method based on fast rerouting | |
| Sansavini et al. | A deterministic representation of cascade spreading in complex networks | |
| Pashaie et al. | Population dynamics approach for resource allocation problems | |
| Hoshino et al. | Improved design methodology for an existing automated transportation system with automated guided vehicles in a seaport container terminal | |
| CN112819048B (en) | Distributed community detection method for inter-satellite dynamic network | |
| Wang et al. | Migration cost-sensitive load balancing for social networked multiagent systems with communities | |
| Kumazaki et al. | VNF management with model predictive control for service chains | |
| CN116893884B (en) | Method, device, equipment and storage medium for expanding and shrinking containerized application service resources |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220215 |
|
| WW01 | Invention patent application withdrawn after publication |