Ship loading method and electronic equipment

Abstract

The present invention relates to a ship stowage method and electronic equipment, belonging to the field of port engineering technology, wherein the method comprises: taking the maximum balanced loading volume as the objective function, taking the equipment operation time, the number of container flipping, the stability and strength of the ship as constraints, establishing a first stowage model; solving the first stowage model based on an artificial bee colony algorithm to complete the ship stowage. The method first establishes a mathematical model, wherein the mathematical model considers the number of container flippings at the next port and the stability of the container ship during the voyage while considering the impact of the position of the container on the ship on the equipment waiting time, thereby optimizing the balanced loading volume of the stowage process, strengthening the information exchange between the railway and the port, and then using the artificial bee colony algorithm to obtain the optimal solution to the model, thereby effectively solving the problem that the existing technology ignores the impact of the railway transportation plan on the port operation.

Description

Technical Field

[0001] The present invention relates to the technical field of port engineering, and in particular to a ship loading method and electronic equipment. Background Art

[0002] In recent years, my country's rail-water transport has entered a period of rapid development, with a rapid increase in total transport volume, optimization and adjustment of transport structure, and accelerated interconnection of infrastructure, playing a positive role in reducing logistics costs, promoting regional development, and promoting energy conservation and carbon reduction. However, my country's rail-water transport has shortcomings in the connection between railways and ports, the utilization of yard resources, the loading mode of container ships, and the information exchange between railways and ports, especially in the transformation of loading modes. At the same time, with the advent of the information age, the development of rail-water transport still needs to rely on modern technologies such as digitalization, intelligence, and greening to provide it with new development space and direction, so as to make the information communication between railways and ports closer. Therefore, under the background of rail-water transport, the loading mode of container ships must change.

[0003] In the rail-water transport, railways and ports are the main entities responsible for the transportation and loading operations. The differences in their operation modes will affect each other during the operation connection process. It is necessary to consider changing the loading mode of railway transport boxes when port conditions permit, and reduce the large accumulation of railway transport boxes in the port yard. This is a problem that needs to be solved urgently. In addition, since container ships stop at more than one port during the entire voyage, the loading and unloading operations of multiple ports will be involved in the ship loading process. The number of containers required to be loaded and unloaded at each port is different, and the loading position of the container inside the ship needs to meet certain constraints to reduce the workload of the quay crane and minimize the completion time of the quay crane operation.

[0004] The existing single-port stowage operation mainly focuses on container slot planning, stowage model solution, algorithm improvement and other aspects, while the multi-port stowage operation planning is based on the single-port stowage operation and focuses more on the destination port turnover volume, pre-stowage yard turnover volume, ship stability and strength and other aspects. Due to the complexity of the stowage problem, few scholars can fully consider all constraints. When studying the optimization of the operation of rail-water transport equipment, existing scholars are more focused on the optimization of the operation of a single equipment in the port. When studying the coordinated optimization of multiple equipment, the objective functions set are mostly biased towards the port, ignoring the impact of the railway transportation plan on the port operation, and not combining the container ship stowage operation with the pre-stowage transportation mode. It focuses on the planning of container ship stowage operations in the port. Therefore, the method of rail-water transport port ship stowage operation still needs to be improved. Summary of the invention

[0005] In view of this, it is necessary to provide a ship loading method to solve the problem that the existing technology ignores the impact of railway transportation plan on port operations.

[0006] In order to solve the above problems, the present invention provides a ship loading method, comprising:

[0007] The first loading model is established with the maximum balanced loading quantity as the objective function and the equipment operation time, container turnover times, ship stability and strength as constraints.

[0008] Solving the first loading model based on an artificial bee colony algorithm to obtain an optimal loading solution;

[0009] The ship loading is completed based on the optimal loading plan.

[0010] In a possible implementation, the objective function includes:

[0011]

[0012] in, represents the set of all ports, ; represents the set of all containers, ; Indicates the total waiting time of the device; Indicates port The number of collection cards; Indicates port Total time required to complete the loading of export containers; Decision variables, if in port container After the loading operation is completed, ,otherwise, ; Indicates that the container ship is in port The sum of the stability gap percentages; Indicates container The weight, .

[0013] In a possible implementation, the constraints include: continuous operation time constraints between equipment, constraints between the start and end times of equipment operations, box space constraints, stability and strength constraints, and equipment operation constraints.

[0014] In a possible implementation, the continuous operation time constraint between devices includes:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] in, Represents the collection of rail-water combined transport port quay cranes, ; represents the collection of rail-water transport port container trucks, ; represents the collection of gantry cranes in the iron-water transport port, ; represents the collection of rail-water combined transport port yard bridges, ; Indicates the device Start operating containers time point, ; Indicates the device End of operation container time point, ; Indicates the device End of operation container To start operating the container , resulting in idle time, ; Indicates the device To end the operation of the container After that, the subsequent container Operation generates wait device time of arrival; Decision variables, if the device For container and If the operation is carried out successively, ,otherwise, .

[0022] In a possible implementation, the constraint between the start and end time of the equipment operation includes:

[0023]

[0024]

[0025]

[0026]

[0027] in, Indicates the device Start operating containers time point, ; Indicates the device Operating Containers time, ; Indicates the device To end the operation of the container Generate waiting device Time of arrival.

[0028] In a possible implementation, the box space constraint includes:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] in, Decision variables, if in port ,container Located in the box In ,otherwise, ; Indicates the location of the container on the ship. ; represents the collection of all columns of container ships, , ; represents the set of all layers of a container ship, ; Decision variables, if in port ,container Located in container Above, ,otherwise, ; represents the set of all bays of a container ship, ; Indicates container destination port.

[0035] In one possible implementation, the stability and strength constraints

[0036]

[0037]

[0038]

[0039]

[0040] in, Indicates the threshold value for left-right balance of container ships; The threshold value indicating the fore-aft balance of a container ship; represents the set of even columns of container ships; represents the set of odd columns of container ships; represents the set of bay positions in the front half of a container ship; Represents the collection of front and rear bays of a container ship.

[0041] In a possible implementation, the device operation constraint includes:

[0042]

[0043] in, Decision variables, if in port ,container By device Operation, then ,otherwise, .

[0044] In a possible implementation, solving the first loading model based on an artificial bee colony algorithm includes:

[0045] Step 1: Initialize the population, current search times, and current iteration times;

[0046] Step 2: Calculate the first fitness value of each bee in the population, confirm the first nectar source based on the first fitness value, perform neighborhood search on the first nectar source based on employed bees to obtain several second nectar sources, and calculate the second fitness values ​​of the several second nectar sources;

[0047] Step 3: Determine whether the second fitness value is greater than the first fitness value;

[0048] Step 4: If it is greater than, replace the first nectar source with the second nectar source;

[0049] Step 5: If it is less than, keep the first nectar source;

[0050] Step 6: The follower bee follows the employed bee, and after completing the following, the employed bee is transformed into the employed bee to perform a neighborhood search on the first nectar source or the second nectar source to obtain a plurality of third nectar sources, and calculate the third fitness values ​​of the plurality of third nectar sources;

[0051] Step 7: Determine whether the third fitness value is greater than the second fitness value;

[0052] Step 8: If it is greater than, the first nectar source or the second nectar source is replaced by the third nectar source, and the current search times are retained;

[0053] Step nine: if it is less than, retain the first nectar source or the second nectar source, and increase the current search times until the current search times are greater than the maximum search times;

[0054] Step 10: Determine whether the current search number is greater than the maximum search number;

[0055] Step 11: If it is greater than, abandon the first nectar source or the second nectar source, and randomly search and regenerate possible solutions;

[0056] Step 12: If it is not greater than, save the current third honey source as a possible solution and go to step 10;

[0057] Step 13: Determine whether the current number of iterations is greater than the preset maximum number of iterations. If it is less, return to step 3.

[0058] Step 14: If it is greater than , the possible solution with the largest fitness value is taken as the final output.

[0059] The present invention also provides an electronic device, comprising:

[0060] Memory, used to store programs;

[0061] A processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the ship loading method described in any one of the above method items.

[0062] The beneficial effects of the present invention are as follows: the present invention provides a ship stowage method, firstly a mathematical model is established, wherein the mathematical model not only considers the influence of the position of the container on the ship on the equipment waiting time, but also considers the number of times the container ship turns over the container at the next port and the stability of the container ship during the voyage, thereby optimizing the balanced loading quantity of the stowage process, strengthening the information exchange between the railway and the port, and then using the artificial bee colony algorithm to obtain the optimal solution of the model, thereby effectively solving the problem that the prior art ignores the influence of the railway transportation plan on the port operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A method flow chart of an embodiment of a ship loading method provided by the present invention;

[0064] Figure 2 A schematic diagram of the railway transport box loading operation mode before and after the port cut-off time provided by the present invention;

[0065] Figure 3 A schematic diagram of the connection time of different equipment operations provided by the present invention;

[0066] Figure 4 A schematic diagram of the container truck transportation path under the "train-ship" and "train-yard-ship" operation modes provided by the present invention;

[0067] Figure 5 for Figure 1 A method flow chart of an embodiment of step S102;

[0068] Figure 6 A schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0070] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0071] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0072] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0073] In order to solve the above problems, Figure 1 As shown, the present invention provides a method for obtaining logistics operation efficiency evaluation, comprising:

[0074] Step S101, establishing a first loading model with the maximum balanced loading quantity as the objective function and equipment operation time, container turnover times, ship stability and strength as constraints;

[0075] Step S102: Solve the first loading model based on the artificial bee colony algorithm to complete the ship loading.

[0076] It should be noted that, since the port has certain time restrictions on the entry of rail transport containers, a cut-off time is set in this embodiment, that is, the latest time node for the container to enter the port and directly load the ship after completing the relevant procedures, such as Figure 2 shown.

[0077] The connection time of different equipment operations during the loading process is also considered, such as Figure 3 As shown in the figure. The operation time of the quay crane and the gantry crane is similar, which includes the operation time of the equipment for container operation; the idle driving time generated by moving to a fixed position after the operation is completed to start the next container operation; and the time waiting for the arrival of the container truck after arriving at the fixed position. The container truck is a connecting device between the railway operation area and the front operation area of ​​the terminal. During the operation process, there will be two kinds of waiting time for the container truck to wait for the quay crane and the container truck to wait for the gantry crane.

[0078] When the train arrives at the port, there are two operation modes for loading rail transport boxes from the train to the ship, namely the "train-ship" mode and the "train-yard-ship" mode. Figure 4 As shown. Among them, the solid line represents the "train-ship" mode transportation path, and the dotted line represents the "train-yard-ship" mode transportation path. During the loading process, the degree of connection between the "train-ship" mode equipment is higher than that of the "train-yard-ship" mode. Therefore, before the cut-off time, the railway transport container shall take the "train-ship" mode first; after the cut-off time, the "train-yard-ship" mode shall be taken.

[0079] Long equipment waiting time will not only increase the loss of the equipment itself, but also affect the connectivity of the entire loading process. Therefore, the waiting time of equipment operation will affect the container loading volume. In order to better reflect the impact of the container turnover volume generated during container unloading and the waiting time generated during equipment loading on the container ship loading operation of the entire route, a balanced loading volume is proposed.

[0080] Therefore, when the berths where the ship is berthing and the number of containers to be loaded at all ports are known, the railway transport containers are loaded on board in a "train-ship" mode based on the original yard loading operation, realizing the coordinated loading of multiple operation areas. According to the stability and strength requirements of container ships and the loading principles of containers on ships, the container positions of different destination ports and weights are determined on the ship, reducing the number of container turnovers at various ports.

[0081] Compared with the prior art, the ship stowage method provided by the present invention first establishes a mathematical model, wherein the mathematical model not only considers the influence of the position of the container on the ship on the equipment waiting time, but also considers the number of times the container ship turns over the container at the next port and the stability of the container ship during the voyage, thereby optimizing the balanced loading quantity in the stowage process, strengthening the information exchange between the railway and the port, and then using the artificial bee colony algorithm to obtain the optimal solution of the model, thereby effectively solving the problem that the prior art ignores the influence of the railway transportation plan on the port operation.

[0082] In one possible implementation, the objective function includes:

[0083] (1)

[0084] In formula (1), represents the set of all ports, ; represents the set of all containers, ; Indicates the total waiting time of the device; Indicates port The number of collection cards; Indicates port Total time required to complete the loading of export containers; Decision variables, if in port container After the loading operation is completed, ,otherwise, ; Indicates that the container ship is in port The sum of the stability gap percentages; Indicates container The weight, .

[0085] In a possible implementation, the constraints include: continuous operation time constraints between equipment, constraints between the start and end times of equipment operations, box space constraints, stability and strength constraints, and equipment operation constraints.

[0086] In a possible implementation, the continuous operation time constraint between devices includes:

[0087] (2)

[0088] (3)

[0089] (4)

[0090] (5)

[0091] (6)

[0092] (7)

[0093] In formulas (2)-(7), Represents the collection of rail-water combined transport port quay cranes, ; represents the collection of rail-water transport port container trucks, ; represents the collection of gantry cranes in the iron-water transport port, ; represents the collection of rail-water combined transport port yard bridges, ; Indicates the device Start operating containers time point, ; Indicates the device End of operation container time point, ; Indicates the device End of operation container To start operating the container , resulting in idle time, ; Indicates the device To end the operation of the container After that, the subsequent container Operation generates wait device time of arrival; Decision variables, if the device For container and If the operation is carried out successively, ,otherwise, .

[0094] It should be noted that formula (2) indicates that the time when the subsequent container of the quay crane starts operation must be later than the time when the previous container task is completed and moved to the next container operation area, otherwise the quay crane will wait for the container truck. Formula (3) and formula (4) indicate that the time when the subsequent container of the container truck starts operation must be later than the time when the previous container task is completed and the empty container truck moves to the railway operation area and the yard crane operation area, otherwise the container truck will wait for the gantry crane and the yard crane. Formula (5) indicates that the time when the subsequent container of the gantry crane starts operation must be later than the time when the previous container task is completed and moved to the next container to be unloaded. Formula (6) indicates that the time when the subsequent container of the yard crane starts operation must be later than the time when the previous container task is completed and moved to the next container to be unloaded. Formula (7) indicates that the total waiting time is the sum of the waiting time generated by each device during the continuous operation process.

[0095] In a possible implementation, the constraints between the start and end times of equipment operations include:

[0096] (8)

[0097] (9)

[0098] (10)

[0099] (11)

[0100] In formulas (8)-(11), Indicates the device Start operating containers time point, ; Indicates the device Operating Containers time, ; Indicates the device To end the operation of the container Generate waiting device Time of arrival.

[0101] It should be noted that formula (8) represents the relationship between the start time and the completion time of the quay crane. Formula (9) represents the relationship between the start time and the completion time of the container truck. Formula (10) represents the relationship between the start time and the completion time of the gantry crane. Formula (11) represents the relationship between the start time and the completion time of the yard crane.

[0102] In a possible implementation, the box space constraints include:

[0103] (12)

[0104] (13)

[0105] (14)

[0106] (15)

[0107] (16)

[0108] (17)

[0109] In formulas (12)-(17), Decision variables, if in port ,container Located in the box In ,otherwise, ; Indicates the location of the container on the ship. ; represents the collection of all columns of container ships, , ; represents the set of all layers of a container ship, ; Decision variables, if in port ,container Located in container Above, ,otherwise, ; represents the set of all bays of a container ship, ; Indicates container destination port.

[0110] It should be noted that formula (12) indicates that a container can only occupy one slot. Formula (13) indicates that each slot can only hold one container. Formula (14) indicates that loaded containers cannot be placed in the air. Formula (15) indicates the position relationship of containers on the ship. Formula (16) determines whether there are other containers above a container to be unloaded. Formula (17) indicates the constraint that the far side does not press the near side, that is, the container near the destination port should be placed above the container far from the destination port.

[0111] In one possible implementation, the stability and strength constraints include:

[0112] (18)

[0113] (19)

[0114] (20)

[0115] (twenty one)

[0116] In formulas (18)-(21), Indicates the threshold value for left-right balance of container ships; The threshold value indicating the fore-aft balance of a container ship; represents the set of even columns of container ships; represents the set of odd columns of container ships; represents the set of bay positions in the front half of a container ship; Represents the collection of front and rear bays of a container ship.

[0117] It should be noted that formula (18) represents the lateral equilibrium constraint between the port and starboard of the container ship. Formula (19) represents the longitudinal equilibrium constraint between the bow and stern of the container ship. Formula (20) ensures that the weight of each layer of containers must be less than the weight of the next layer of containers during container loading. Formula (21) ensures that the total weight of containers on the ship must be less than the maximum load of the container ship.

[0118] In a possible implementation, the device operation constraints include:

[0119] (twenty two)

[0120] in, Decision variables, if in port ,container By device Operation, then ,otherwise, .

[0121] It should be noted that formula (22) ensures that at each port there can only be one quay crane, one container truck, one gantry crane and one yard crane to operate each container.

[0122] like Figure 5 In a possible implementation, step S102 includes:

[0123] Step S501, initializing the population, the current search times and the current iteration times;

[0124] Specifically, in this embodiment, the total number of bees is set to N, the number of employed bees is set to Ne, the number of follower bees is set to Nu (Ne=Nu), the maximum number of searches is set to limit, the number of iterations is set to iter=0, the maximum number of iterations is set to maxiter, the crossover probability is set to Pc, and the mutation probability is set to Pm.

[0125] It is understandable that in the artificial bee colony algorithm, the nectar source corresponds to the optimal solution required by the algorithm, and the purpose of employing bees is to find the best quality nectar source.

[0126] Step S502, calculating the first fitness value of each bee in the population, and confirming the first nectar source based on the first fitness value, performing neighborhood search on the first nectar source based on employed bees to obtain a plurality of second nectar sources, and calculating the second fitness values ​​of the plurality of second nectar sources;

[0127] Specifically, the solution of the initialized population , , and calculate its fitness value according to the following formula,

[0128] (twenty three)

[0129] Determine the initial nectar source that the employed bees go to based on the size of the fitness value and set the number of searches first(i)= 0.

[0130] Furthermore, when performing neighborhood search, the employed bees search for new solutions near the nectar source according to the following formula:

[0131] (twenty four)

[0132] The generated new solution has a probability of Pc for crossover operation and a probability of Pm for mutation operation, and then a new solution is generated. , and calculate its fitness value;

[0133] Step S503, determining whether the second fitness value is greater than the first fitness value;

[0134] Step S504: if it is greater than, the first nectar source is replaced by the second nectar source;

[0135] Step S505: if it is less than, retain the first nectar source;

[0136] Step S506: The follower bee follows the employed bee, and after completing the following, the follower bee is transformed into an employed bee to perform a neighborhood search on the first nectar source or the second nectar source to obtain a plurality of third nectar sources, and calculate the third fitness values ​​of the plurality of third nectar sources;

[0137] It should be noted that, in this embodiment, the following bee is based on the probability Following the hired bees, The calculation formula is:

[0138] (25)

[0139] Step S507, determining whether the third fitness value is greater than the second fitness value;

[0140] Step S508: If it is greater than, the first nectar source or the second nectar source is replaced by the third nectar source, and the current search times are retained;

[0141] Step S509: if it is less than, retain the first honey source or the second honey source, and increase the current search times until the current search times are greater than the maximum search times;

[0142] Step S510, determining whether the current search times is greater than the maximum search times;

[0143] Step S511: If it is greater than, abandon the current first honey source or second honey source, and randomly search and regenerate possible solutions;

[0144] It should be noted that when the current search times are greater than the maximum search times, it means that the area near the current nectar source has been fully exploited, that is, there is no global optimal solution.

[0145] Step S512: If it is not greater than, save the current third honey source as a possible solution and go to step ten;

[0146] Step S513, determine whether the current number of iterations is greater than the preset maximum number of iterations, if less, return to step S503;

[0147] Step S514: If it is greater than, the possible solution with the largest fitness value is taken as the final output.

[0148] like Figure 6 As shown, the present invention further provides an electronic device 60, comprising:

[0149] A memory 610, used for storing programs;

[0150] The processor 620 is coupled to the memory 610 and is used to execute the program stored in the memory 610 to implement the steps in the method for obtaining logistics operation efficiency evaluation described in any one of the above embodiments.

[0151] Figure 6 Only some of the components of the electronic device 60 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0152] In some embodiments, the processor 620 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 610, such as the logistics operation efficiency evaluation acquisition method of the present invention.

[0153] In some embodiments, the processor 620 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 620 may be local or remote. In some embodiments, the processor 620 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0154] In some embodiments, the memory 610 may be an internal storage unit of the electronic device 60, such as a hard disk or memory of the electronic device 60. In other embodiments, the memory 610 may also be an external storage device of the electronic device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 60.

[0155] Furthermore, the memory 610 may include both an internal storage unit of the electronic device 60 and an external storage device. The memory 610 is used to store application software installed in the electronic device 60 and various data.

[0156] In one embodiment, when the processor 620 executes the logistics operation efficiency evaluation acquisition program in the memory 610, the following steps may be implemented:

[0157] The first loading model is established with the maximum balanced loading quantity as the objective function and the equipment operation time, container turnover times, ship stability and strength as constraints.

[0158] The first loading model is solved based on the artificial bee colony algorithm to complete the ship loading.

[0159] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 60 mentioned, and the electronic device 60 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 60 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0160] The present invention also provides a storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps of the method for obtaining logistics operation efficiency evaluation described in any one of the above embodiments.

[0161] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer storage medium, wherein the computer storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0162] The above is a detailed introduction to the logistics operation efficiency evaluation acquisition method, device, electronic device and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A ship stowage method, characterized in that: include: The first loading model is established with the maximum balanced loading volume as the objective function and the equipment operation time, the number of container turning, the stability and strength of the ship as the constraints. The balanced loading volume includes: the final container loading volume after deducting the container volume converted by considering the equipment waiting time, the number of container turning at the next port and the stability of the ship from the actual loading volume; Solving the first loading model based on the artificial bee colony algorithm to complete the ship loading; Wherein, the objective function includes: in, represents the set of all ports, ; represents the set of all containers, ; Indicates the total waiting time of the device; Indicates port The number of collection cards; Indicates port Total time required to complete the loading of export containers; Decision variables, if in port container After the loading operation is completed, ,otherwise, ; Indicates that the container ship is in port The sum of the stability gap percentages.

2. The ship stowage method according to claim 1, characterized in that: The constraints include: continuous operation time constraints between equipment, constraints between the start and end times of equipment operations, box space constraints, stability and strength constraints, and equipment operation constraints.

3. The ship stowage method according to claim 2, characterized in that: The continuous operation time constraints between devices include: in, represents the collection of quay cranes in the railway-water transport port. ; represents the collection of rail-water transport port container trucks, ; represents the collection of gantry cranes for the combined iron and water transport port. ; represents the collection of rail-water combined transport port yard bridges, ; Indicates the device Start operating containers time point, ; Indicates the device End of operation container time point, ; Indicates the device End of operation container To start operating the container The idle time generated, ; Indicates the device To end the operation of the container After that, the subsequent container Operation generates wait device Arrival time, ; Indicates the device End of operation container Waiting device time of arrival; Decision variables, if the device For container and If the operation is carried out successively, ,otherwise, .

4. The ship stowage method according to claim 2, characterized in that: The constraints between the start and end time of the equipment operation include: in, represents the set of all containers, ; represents the collection of quay cranes in the railway-water transport port. ; represents the collection of rail-water transport port container trucks, ; represents the collection of gantry cranes for the combined iron and water transport port. ; represents the collection of rail-water combined transport port yard bridges, ; Indicates the device Start operating containers time point, ; Indicates the device Operating Containers time, ; Indicates the device End of operation container Waiting device Time of arrival.

5. The ship stowage method according to claim 2, characterized in that: The slot constraints include: in, represents the set of all ports, ; represents the set of all containers, ; Decision variables, if in port ,container Located in the box In ,otherwise, ; Indicates the location of the container on the ship. ; Decision variables, if in port ,container Located in the box In ,otherwise, , t +1 means t The upper layer of the container; Decision variables, if in port ,container Located in the box In ,otherwise, , t’ Indicates t Any layer above the container layer; Decision variables, if in port ,container n Located in the box In ,otherwise, ; represents the collection of all columns of container ships, , ; represents the set of all layers of a container ship, ; Decision variables, if in port ,container Located in container Above, ,otherwise, ; Decision variables, if in port ,container Located in container Above, ,otherwise, ; Decision variables, if the container m There are containers on top m’ ,but ,otherwise, ; represents the set of all bays of a container ship, ; represents the set of even columns of container ships; represents the set of odd columns of container ships; Indicates container the port of destination; Indicates container n destination port.

6. The ship stowage method according to claim 2, characterized in that: The stability and strength constraints include: in, represents the set of all ports, ; represents the set of all containers, ; Indicates container or weight; Indicates the threshold value for left-right balance of container ships; The threshold value indicating the fore-aft balance of a container ship; represents the set of even columns of container ships; represents the set of odd columns of container ships; represents the collection of all columns of container ships, , ; represents the set of bay positions in the front half of a container ship; It represents the set of front and rear bay positions of a container ship; represents the set of all bays of a container ship, ; represents the set of all layers of a container ship, ; Indicates the location of the container on the ship. ; Decision variables, if in port ,container Located in the box In ,otherwise, ; Decision variables, if in port ,container Located in the box In ,otherwise, , t +1 means t The upper layer of the container; Indicates the maximum deadweight of the ship.

7. The ship stowage method according to claim 2, characterized in that: The equipment operation constraints include: in, Decision variables, if in port ,container By device Operation, then ,otherwise, ; represents the set of all containers, ; represents the set of all ports, ; represents the collection of quay cranes in the railway-water transport port. ; represents the collection of rail-water transport port container trucks, ; represents the collection of gantry cranes for the combined iron and water transport port. ; represents the collection of rail-water combined transport port yard bridges, ; It represents the collection of quay cranes, container trucks, gantry cranes and yard cranes. , , .

8. The ship stowage method according to claim 1, characterized in that: The solving the first loading model based on the artificial bee colony algorithm includes: Step 1: Initialize the population, current search times, and current iteration times; Step 2: Calculate the first fitness value of each bee in the population, confirm the first nectar source based on the first fitness value, perform neighborhood search on the first nectar source based on employed bees to obtain several second nectar sources, and calculate the second fitness values ​​of the several second nectar sources; Step 3: Determine whether the second fitness value is greater than the first fitness value; Step 4: If it is greater than, replace the first nectar source with the second nectar source; Step 5: If it is less than, keep the first nectar source; Step 6: The follower bee follows the employed bee, and after completing the following, the employed bee is transformed into the employed bee to perform a neighborhood search on the first nectar source or the second nectar source to obtain a plurality of third nectar sources, and calculate the third fitness values ​​of the plurality of third nectar sources; Step 7: Determine whether the third fitness value is greater than the second fitness value; Step 8: If it is greater than, the first nectar source or the second nectar source is replaced by the third nectar source, and the current search times are retained; Step nine: if it is less than, retain the first nectar source or the second nectar source, and increase the current search times until the current search times are greater than the maximum search times; Step 10: Determine whether the current search number is greater than the maximum search number; Step 11: If it is greater than, abandon the first nectar source or the second nectar source, and randomly search and regenerate possible solutions; Step 12: If it is not greater than, save the current third honey source as a possible solution and go to step 10; Step 13: Determine whether the current number of iterations is greater than the preset maximum number of iterations. If it is less, return to step 3. Step 14: If it is greater than , the possible solution with the largest fitness value is taken as the final output.

9. An electronic device, characterized in that: include: Memory, used to store programs; A processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the ship loading method described in any one of claims 1 to 8.

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