Hybrid optimization method, device and equipment for semi-submerged ship module stowage and medium

Abstract

The invention provides a hybrid optimization method for semi-submerged ship module stowage. The method comprises the following steps: S1, constructing an expression of a target function of the minimum cost of a semi-submerged ship project according to ship and cargo port information and decision variables; s2, performing iterative optimization on the target function through an ant colony algorithm to calculate an optimal cargo module stowage scheme of the target function; s3, generating a cargo module filling scheme of each carrier ship based on a residual matrix algorithm and the cargo module stowage scheme; and S4, establishing a port order set of the carrier ships according to the cargo module filling scheme, and searching and determining a route with the minimum cost in the port order set. According to the embodiment of the invention, each link in the semi-submersible ship project is optimized, so that the cost of the final overall scheme is the minimum value, meanwhile, various constraint conditions in the semi-submersible project can be met, and a loading transportation plan is ensured to be exact and feasible.

Description

Technical Field

[0001] The present invention relates to the technical field of ship software development, and in particular to a hybrid optimization method and device for module loading of a semi-submersible ship. Background Art

[0002] Semi-submersible ships adopt floating technology, which is very flexible and convenient. They not only have a large carrying capacity, but also have strong seaworthiness. They have broad prospects for development in long-distance transportation. However, in the transportation process of semi-submersible ships, huge costs are required. The main components of their navigation costs are the ship's rental costs, navigation fuel costs, port berthing costs, and if they pass through the canal, they also need to consider the canal costs and other costs. The calculation of costs also needs to take into account the loading and unloading time and loading and unloading ports of goods. It is necessary to plan the route according to the loading and unloading time and loading and unloading ports of different goods, and reasonably assemble to reduce costs.

[0003] The cost of a semi-submersible project consists of voyage cost and demobilization cost. Among them, voyage cost is the sum of all voyage costs, and demobilization cost is the sum of the demobilization costs of all ships used in the project. For a single voyage, its cost = rental cost (voyage days × daily rental) + fuel cost (voyage days × daily fuel consumption) + port usage fee + canal cost (if passing) + other costs; for a single semi-submersible ship, its demobilization cost = voyage days × (daily rental + daily fuel consumption). Therefore, how to minimize the cost of the semi-submersible project through the optimal matching of ships, cargoes and ports is its main problem. First of all, the assembly of goods must be considered to reduce costs. Since the transportation cost shows a very obvious positive correlation with the number of days occupied by all voyages of the project, if the deck utilization rate can be improved and more cargo can be transported with fewer voyages, it will have very obvious economic benefits under the premise of meeting the constraints.

[0004] Secondly, due to the nature of sea transportation, multiple constraints need to be met when calculating the plan: a. Meet the loading and unloading port sequence requirements of the voyage; b. The loading and unloading time window of the module batch must be met; c. The module spacing requirements (longitudinal / lateral) and the module's permitted overhang length must be met, and the module will not interfere with the inaccessible area of ​​the ship; d. The module's route will not interfere with the inaccessible area; e. The specific loading position of the module must meet the rigid requirements of the actual loading and unloading operation requirements; f. The selected ship must meet the loading feasibility of the terminal (calculate the empty ship draft and maximum draft (water entry point limit) for each ship, and judge the feasibility in combination with the terminal elevation, water depth, and tidal conditions.) Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a hybrid optimization method for module loading of a semi-submersible vessel, comprising:

[0006] S1 constructs the expression of the objective function of the minimum cost of the semi-submersible vessel project based on the ship cargo port information and decision variables;

[0007] S2 iteratively optimizes the objective function through an ant colony algorithm to calculate an optimal cargo module loading plan for the objective function;

[0008] S3 generates a cargo module filling plan for each carrier ship based on the residual matrix algorithm and the cargo module loading plan;

[0009] S4: establishing a port sequence set for the carrier ship according to the cargo module filling plan, and searching and determining the route with the minimum cost in the port sequence set.

[0010] In one embodiment, in the expression of the objective function for constructing the minimum cost of the semi-submersible vessel project based on the ship cargo port information and the decision variables, the constructed objective function expression is as follows:

[0011]

[0012] Among them, C V,jk The cost required to arrange ship j on voyage k can be expressed as:

[0013]

[0014] Among them, C R,j (T VE,k -T VB,k ) is the rental cost of voyage k, Port usage fees, is the fuel consumption during the stay in Hong Kong, The fuel consumption during the voyage is For canal fees, satisfy:

[0015]

[0016] and The speed decision to minimize the sum of fuel cost and rental for this route is:

[0017]

[0018] In one embodiment, the iterative optimization of the objective function by using an ant colony algorithm to calculate the optimal cargo module loading solution of the objective function includes the following steps:

[0019] S201 initializes the cargo collection to be loaded in the semi-submersible vessel project and establishes a solution item;

[0020] S202: establishing a single voyage in the solution item, randomly selecting a carrier ship for the voyage, and calculating the available deck area of ​​the carrier ship according to the pontoon solution;

[0021] S203, based on 3.5 times of the available area of ​​the deck, selecting and establishing a set of candidate cargoes from the cargoes to be loaded, and loading the set of candidate cargoes onto the deck;

[0022] S204 repeats step S201 to step S203 until the cargo to be loaded is empty, and calculates the objective function of the solution item;

[0023] S205 obtains the optimal solution item set through comparison operation and updates the pheromone matrix, and repeats the iteration until the number of iterations reaches a preset value.

[0024] In one embodiment, the method of generating a cargo module filling plan for each carrier ship based on the residual matrix algorithm and the cargo module loading plan includes the following steps:

[0025] S301 obtains the cargo module loading plan of the carrier ship, groups the cargo modules according to the loading port, sorts the modules within the group according to the length, and sorts the modules between groups according to the loading time. The loading time of each group is defined as the earliest planned loading time of all the cargo in the group;

[0026] S302: taking out a cargo module from a group with a forward grouping sequence, establishing a new row on the side of the available deck area close to the bow, placing the cargo module in the new row, and the width of the new row being equal to the width of the cargo module;

[0027] S303 placing cargo modules in the new column based on a remaining rectangle algorithm until the new column cannot be filled with any modules;

[0028] S304 determines whether all cargo modules have been assembled. If not, reselect cargo modules to fill in. Otherwise, the algorithm ends.

[0029] In one embodiment, the step of establishing a port sequence set for the carrier ship according to the cargo module filling plan, and searching and determining the route with the minimum cost in the port sequence set further includes the following steps:

[0030] S401 establishes port sequence according to cargo loading and unloading ports and time;

[0031] S402 selects all routes satisfying the canal constraint in sections according to the determined port sequence;

[0032] S403 calculates the route with the lowest cost in each section through speed decision, and selects the route with the lowest cost;

[0033] S404 connects the route with the lowest cost in each segment to form a complete route.

[0034] In one embodiment, when calculating the objective function in step S2, the objective function needs to meet the following constraints: the number of carrier ships is less than or equal to the total number of voyages, the sum of the decision variables of the carrier ships or voyages is equal to the total number of voyages, the number of carrier ships for a single voyage is 1, the cargo modules are only arranged on one voyage, the cargo modules are placed within the available area of ​​the deck, the distance between the cargo modules is within a preset range, the carrier ships can berth at all ports passed by the corresponding voyage, and the difference between the planned loading and unloading time and the actual loading and unloading time of the cargo modules is less than a preset value.

[0035] The present invention also provides a hybrid optimization device for module loading of a semi-submersible vessel, comprising:

[0036] A project construction unit, which is used to construct an expression of the objective function of the minimum cost of the semi-submersible vessel project according to the ship cargo port information and the decision variables;

[0037] A cargo optimization unit, which is used to iteratively optimize the objective function through an ant colony algorithm to calculate a cargo module loading solution with the optimal objective function;

[0038] A cargo filling unit, which is used to generate a cargo module filling plan for each carrier ship based on a residual matrix algorithm and the cargo module loading plan;

[0039] A route optimization unit is used to establish a port sequence set for the carrier ship according to the cargo module filling plan, and to search and determine the route with the minimum cost in the port sequence set.

[0040] In one embodiment, in the project construction unit, the objective function expression constructed is as follows:

[0041]

[0042] Among them, C V,jk The cost required to arrange ship j on voyage k can be expressed as:

[0043]

[0044] Among them, C R,j (T VE,k -T VB,k ) is the rental cost of voyage k, Port usage fees, is the fuel consumption during the stay in Hong Kong, The fuel consumption during the voyage is For canal fees, satisfy:

[0045]

[0046] and The speed decision to minimize the sum of fuel cost and rental for this route is:

[0047]

[0048] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned hybrid optimization method for module loading of a semi-submersible vessel when executing the computer program.

[0049] The present invention also provides a computer storage medium on which a computer program is stored, and when the program is executed by a processor, the hybrid optimization method for module loading of a semi-submersible vessel described above is implemented.

[0050] The embodiment of the present invention optimizes each link of the semi-submersible ship project and establishes the objective function of cost minimization, so that the cost of the semi-submersible ship project can be described and solved by mathematical language, and the overall framework can be accurately solved based on the ant colony algorithm to find the optimization direction; the deck is filled based on the improved residual rectangle method to improve the deck utilization rate; the route and speed optimization are used to reduce the shipping fuel consumption cost. The ship, cargo and port in the semi-submersible ship cargo project are matched, so that the cost of the final overall solution is the minimum value, and at the same time, it can meet multiple constraints in the semi-submersible project to ensure that the loading and transportation plan is accurate and feasible. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0052] Figure 1 It is a flow chart of a hybrid optimization method for semi-submersible vessel module loading according to a first embodiment of the present invention;

[0053] Figure 2 is a detailed flow chart of S2 of the first embodiment of the present invention;

[0054] Figure 3 is a detailed flow chart of S3 of the first embodiment of the present invention;

[0055] Figure 4 is a detailed flow chart of S4 of the first embodiment of the present invention;

[0056] Figure 5This is a schematic diagram of optimal cargo module assembly simulated in the first embodiment of the present invention;

[0057] Figure 6 It is an iteration-cost graph of the ant colony algorithm in the first embodiment of the present invention;

[0058] Figure 7 It is a structural block diagram of a hybrid optimization device for semi-submersible vessel module loading according to a second embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram of the internal structure of a computer according to another embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and the well-known modules, units and their connections, links, communications or operations are not shown or described in detail. In addition, the described features, architectures or functions can be combined in any way in one or more embodiments. It should be understood by those skilled in the art that the various embodiments described below are only for illustration and are not intended to limit the scope of protection of the present invention. It can also be easily understood that the modules or units or processing methods in the various embodiments described herein and shown in the drawings can be combined and designed according to various different configurations. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0061] First embodiment:

[0062] Please refer to Figures 1 to 6 As shown, an embodiment of the present invention discloses a hybrid optimization method for module loading of a semi-submersible vessel, comprising:

[0063] S1 constructs the expression of the objective function of the minimum cost of the semi-submersible vessel project based on the ship cargo port information and decision variables;

[0064] It is understandable that the known cargo port information in actual semi-submersible vessel projects is shown in the following table:

[0065]

[0066]

[0067] The decision variables are shown in the following table:

[0068]

[0069] According to the known cargo port information and decision variables, the objective function expression constructed in this embodiment is as follows:

[0070]

[0071] Among them, C V,jk The cost required to arrange ship j on voyage k can be expressed as:

[0072]

[0073] Among them, C R,j (T VE,k -T VB,k ) is the rental cost of voyage k, Port usage fees, is the fuel consumption during the stay in Hong Kong, The fuel consumption during the voyage is For canal fees, satisfy:

[0074]

[0075] and The speed decision to minimize the sum of fuel cost and rental for this route is:

[0076]

[0077] Specifically, when calculating the objective function, the following constraints are also satisfied to ensure that the loading scheme corresponding to the calculated objective function can be realized:

[0078] The number of vessels carrying is less than or equal to the total number of voyages:

[0079]

[0080] The sum of the decision variables for the carrier ships or voyages is equal to the total number of voyages:

[0081]

[0082] The number of carrier vessels for a single voyage is 1:

[0083]

[0084] The cargo module is only scheduled on one voyage:

[0085]

[0086] The cargo module is placed within the available area of ​​the deck and can be extended appropriately:

[0087]

[0088] The distance between cargo modules is within the preset range:

[0089]

[0090] The difference between the planned loading and unloading time and the actual loading and unloading time of the cargo module is less than the preset value:

[0091]

[0092] When loading and unloading cargo, there must be no obstruction in the side-roll or tail-roll path, that is, during side-roll, the monotonicity of the cargo loading port sequence on the ro-ro route cannot change; during tail-roll, the cargo loading port sequence on the ro-ro route must be monotonically reduced along the X direction.

[0093] Based on the mathematical model and objective function constructed above, the transportation cost problem of the semi-submersible vessel project is converted into a mathematical language description, and then it is optimized and solved to obtain the cost-optimal solution.

[0094] S2 iteratively optimizes the objective function through an ant colony algorithm to calculate an optimal cargo module loading plan for the objective function;

[0095] It is understandable that if Figure 2 As shown, for the matching problem of multiple ship, cargo and port solutions in the semi-submersible ship project, this embodiment also proposes a method for optimizing and solving its assembly solution using the node transfer strategy of the ant colony algorithm. Since the optimization of the loading of ship cargo modules is a random search problem, the heuristic algorithm such as the ant colony algorithm is considered to be used for solution. In the ant colony algorithm, the node transfer probability is determined by pheromones and heuristic factors. The heuristic factor is the reciprocal of the difference in the number of days between the planned loading time of the cargo to be selected and the earliest planned loading time of the voyage. The initial value of the pheromone concentration on each path is 1. Since it is necessary to randomly select ships and cargoes, it is assumed that the number of ships to be selected is m, the number of cargoes to be loaded is n, and the scale of the pheromone matrix is ​​m*n. Specifically, the following steps are included:

[0096] S201 initializes the cargo collection to be loaded in the semi-submersible vessel project and establishes a solution item;

[0097] It can be understood that a solution is established for each ant in the ant colony algorithm, a single solution is a solution, and the set of all solutions is the solution set of each generation of the ant colony algorithm. Each solution contains all the voyage information of this loading plan, and a voyage information contains information such as the selected ship, loaded cargo, route and speed of this voyage.

[0098] S202: establishing a single voyage in the solution item, randomly selecting a carrier ship for the voyage, and calculating the available deck area of ​​the carrier ship according to the pontoon solution;

[0099] It is understandable that, considering the cost of demobilization, priority is given to ships that are already sailing, provided that the sailing date meets the conditions. When all ships are sailing and the return date cannot meet the loading time requirements of the cargo to be loaded, the cargo extension operation is to be carried out and an optimal "extension ship" is selected. The sailing date of the selected ship needs to meet the requirements.

[0100] S203, based on 3.5 times of the available area of ​​the deck, selecting and establishing a set of candidate cargoes from the cargoes to be loaded, and loading the set of candidate cargoes onto the deck;

[0101] It is understandable that there is no complete order between steps S2 and S3. When the ant colony algorithm is optimized and solved, step S203 will use the semi-submersible ship loading optimization method based on the residual matrix algorithm proposed in step S3 below to ensure that each set of alternative cargo loading is reasonable and feasible, and to ensure that the interference problem of cargo on the assembly path is avoided under the premise of satisfying the full assembly of cargo. It can not only make full use of the deck space, but also meet the loading requirements.

[0102] Specifically, after determining the carrier ship and the cargo to be equipped, the cost of the voyage is calculated through the objective function. A new ship with a sailing date that meets the requirements is selected, and the cargo is re-equipped and the cost is calculated. By comparing the before and after, the ship with the lowest voyage cost is selected to determine the most suitable carrier ship for each voyage.

[0103] S204 repeats step S201 to step S203 until the cargo to be loaded is empty, and calculates the objective function of the solution item;

[0104] It can be understood that by continuously selecting ships and loading goods until all the goods to be loaded are assembled, the carrier ships and assembled goods selected for all voyages are obtained. The cost of each voyage is obtained through the objective function technology, and the solution is accumulated to obtain the total cost of the transportation plan.

[0105] S205 obtains the optimal solution item set through comparison operation and updates the pheromone matrix, and repeats the iteration until the number of iterations reaches a preset value.

[0106] It is understandable that the total cost of each solution in the current era is obtained, and the optimal solution is selected by cost comparison; after each generation cycle, the pheromone matrix is ​​updated to make the random selection of ants more inclined. This step is the key to make the algorithm converge and tend to a better solution.

[0107] If the maximum number of iterations is not reached, each ant needs to search for a solution again until the maximum number of iterations is met.

[0108] S3 generates a cargo module filling plan for each carrier ship based on the residual matrix algorithm and the cargo module loading plan;

[0109] It is understandable that if Figure 3 As shown, this embodiment also proposes a semi-submersible ship loading optimization method based on the residual rectangle algorithm. Taking into account the particularity of the semi-submersible module loading problem, there should be no occlusion between goods of different port sequences on the roll-on / roll-off path during the loading process. Therefore, the classic residual rectangle method is improved, and a column-generated residual rectangle method based on grouping sorting is proposed. In this scheme, in order to simplify the problem, the available deck areas of all ships are simplified to standard rectangles, and the shapes of all modules are also simplified to rectangles. In this way, the deck filling problem is transformed into a rectangle filling problem. In addition, a coordinate system is established with the axis of the ship as X, the bow direction is the positive direction of the X-axis, and the stern axis system point is the origin of the coordinate system. The port side is the positive direction of the Y-axis, and the starboard side is the negative direction of the Y-axis. Specifically, the following steps are included:

[0110] S301 obtains the cargo module loading plan of the carrier ship, groups the cargo modules according to the loading port, sorts the modules within the group according to the length, and sorts the modules between groups according to the loading time. The loading time of each group is defined as the earliest planned loading time of all the cargo in the group;

[0111] S302: taking out a cargo module from a group with a forward grouping sequence, establishing a new row on the side of the available deck area close to the bow, placing the cargo module in the new row, and the width of the new row being equal to the width of the cargo module;

[0112] S303 placing cargo modules in the new column based on a remaining rectangle algorithm until the new column cannot be filled with any modules;

[0113] It can be understood that the steps of the classic residual matrix algorithm are as follows:

[0114] S3031 sorts all modules to be filled according to their lengths, where the length is defined as the size of the edge along the X direction.

[0115] S3032 takes a rectangle from the list of modules to be filled in order and places it in the lower right corner (the classic method usually places it in the lower left corner, but for the problem of loading the deck of a ro-ro ship, it is usually necessary to start from the bow). Then, along the edge of the module, the remaining area of ​​the deck is divided into two remaining rectangles, and the two remaining rectangles are placed in the list of remaining rectangles.

[0116] S3033 takes out a module from the list of modules to be filled again according to the sorting, and determines which remaining rectangle it is placed in according to the evaluation function. If none of the remaining rectangles can fit, take the module to be filled that is sorted and placed next, and so on. Once a module to be filled and the remaining rectangle to be placed are selected, the original remaining rectangle is deleted from the list of remaining rectangles, and the two new remaining rectangles obtained by separating the original remaining rectangle are placed in the list of remaining rectangles.

[0117] S3034 merges the remaining rectangles that can be merged according to the principle of maximizing the utilization of area, and updates the remaining rectangle list.

[0118] S3035 If after the merging operation, the largest one in the remaining rectangle list can no longer accommodate the smallest one in the to-be-filled module list, it is determined that there is no available area and the algorithm ends. Otherwise, it returns to step S3033.

[0119] By combining the grouping and sorting strategies with the new column creation strategy, the blocking of goods in different port sequences on the roll-on / roll-off path is effectively avoided for most of the multiple loading and unloading problems. It is convenient for the rapid unloading of goods when arriving at the loading and unloading port, effectively improving the transportation efficiency.

[0120] S304 determines whether all cargo modules have been assembled. If not, reselect cargo modules to fill in. Otherwise, the algorithm ends.

[0121] S4: establishing a port sequence set for the carrier ship according to the cargo module filling plan, and searching and determining the route with the minimum cost in the port sequence set.

[0122] Specifically, Figure 4 As shown, the port sequence set of the carrier ship is established according to the cargo module filling plan, and the minimum cost route is searched and determined in the port sequence set, and the following steps are also included:

[0123] S401 establishes port sequence according to cargo loading and unloading ports and time;

[0124] Specifically, when establishing a port sequence set, first establish an empty port sequence array, a loading port array, and an unloading port array. Secondly, sort all the cargoes according to the loading time of the current voyage. Then, add all the loading ports of all the cargoes to the loading port array one by one without duplication. Calculate the expected loading date for each loading port and perform the same operation on all the unloading ports. Finally, perform mixed sorting according to the loading and unloading dates of the loading port array and the unloading port array to construct the port sequence array.

[0125] S402 selects all routes satisfying the canal constraint in sections according to the determined port sequence;

[0126] Specifically, the route selection decision is to determine the minimum cost route for each section after the port sequence decision, and finally connect them together to determine the total driving route. The route selection method is the segment-by-segment determination method. That is, starting from the first element of the port sequence array, find the route with the minimum cost that can reach the second element and meet the cargo canal constraint. Then find the route from the second element to the bottom 3 elements. The principle is also to minimize the cost that meets the cargo canal constraint. And so on to the unloading port. Find the return route from the unloading port to the first loading port. The return route does not need to consider the canal constraint. The canal constraint means that in the process from any A to any B, as long as there is a piece of cargo departing from A that is marked as not passing through the canal, all routes from A→B that pass through the canal are not considered. The cost of each route is defined as the minimum cost under the optimal speed decision on the route.

[0127] S403 calculates the route with the lowest cost in each section through speed decision, and selects the route with the lowest cost;

[0128] Specifically, the current speed decision only considers "high speed" and "economic speed". At "high speed", the fuel cost is higher, but the rental cost is reduced as the number of voyage days is reduced; at "economic speed", the fuel cost is lower, but the number of voyage days increases, resulting in more rental costs. Therefore, a speed decision must be made for each route, and the speed with the lowest comprehensive cost must be selected.

[0129] Therefore, the choice of speed can be determined according to the following formula:

[0130] C r = arg min[λ r C H +(1-λ r )C E +C R ]ΔT r

[0131] Among them, C r is the total cost on route r; C H is the daily fuel consumption cost at high speed; C E is the daily fuel consumption cost at economic speed; C R is the daily rental of the ship; r Indicates whether high speed is adopted on route r, λ r =1 means high speed, λ r =0 means economic speed; ΔT r is the time spent on route r.

[0132] Where, ΔT r Should meet:

[0133]

[0134] Among them, V H is the speed of high speed; V E The speed of economic speed; D r is the total distance of route r

[0135] The sailing time should take into account the constraints of cargo loading and unloading time.

[0136] S404 connects the route with the lowest cost in each section to form a complete route. After making decisions on the port sequence, route, and speed, the route with the lowest cost selected in each section is connected to make a route plan for the voyage.

[0137] For ease of understanding, this embodiment takes an actual semi-submersible ship project as an example for the following simulation description:

[0138] The available ship types are shown in the following table:

[0139]

[0140] In this embodiment, no estimated unloading date is set for the cargo to be loaded, the longitudinal spacing between the cargoes is 2m, the transverse spacing is 2m, and the maximum extension length is 3m.

[0141] The cargo to be loaded is shown in the following table:

[0142]

[0143]

[0144]

[0145] The loading port and unloading port are PO1 and PO2 respectively;

[0146] The routes are as follows:

[0147] route Starting port Canal Code distance Persian Gulf route PO1 1 10000 Cape of Good Hope Route PO2 0 16000

[0148] like Figure 5 As shown, the above cargo module is optimized by the hybrid optimization method for semi-submersible ship module loading proposed in this embodiment to obtain the following assembly scheme:

[0149] The first voyage is: V01_XYK_01, start date: 2021-04-29 00:00:00, end date: 2021-07-04 21:41:29.142857, deck utilization rate: 62.68%, and a total of 19 cargoes were loaded.

[0150] The second voyage is: V02_DONGBANGGIANTNO.7_01, start date: 2021-04-29 00:00:00, end date: 2021-07-17 02:00:37.565217, deck utilization rate: 83.97%, a total of 7 cargoes loaded:

[0151] The third voyage is: V03_DBG3_01, start date: 2021-04-29 00:00:00, end date: 2021-07-02 05:22:17.142857, deck utilization rate: 57.58%, and a total of 4 cargoes were loaded.

[0152] The 4th voyage is: V04_XHK_01, start date: 2021-05-01 00:00:00, end date: 2021-07-07 14:58:17.142857, deck utilization rate: 69.73%, with a total of 11 cargoes loaded.

[0153] The 5th voyage is: V05_HXL_01, start date: 2021-05-03 00:00:00, end date: 2021-07-20 05:22:13.565217, deck utilization: 72.11%, and a total of 8 cargoes loaded.

[0154] The 6th voyage is: V06_XAK_01, start date: 2021-05-04 00:00:00, end date: 2021-07-10 07:17:29.142857, deck utilization: 72.14%, with a total of 13 cargoes loaded.

[0155] The 7th voyage is: V07_TAK_01, start date: 2021-05-04 00:00:00, end date: 2021-07-08 02:58:17.142857, deck utilization: 77.70%, and a total of 4 cargoes loaded.

[0156] The 8th voyage is: V08_HSL_01, start date: 2021-05-05 00:00:00, end date: 2021-07-21 11:36:37.565217, deck utilization: 61.42%, and a total of 9 cargoes loaded.

[0157] The 9th voyage is: V09_ZYK_01, start date: 2021-06-22 00:00:00, end date: 2021-08-24 18:19:53.142857, deck utilization rate: 38.52%, and a total of 9 cargoes loaded.

[0158] This simulation was repeated 20 times through the ant colony algorithm, and the optimal solution of each generation of the objective function is as follows: Figure 6 As shown, the final minimum cost ($) is: 52742260.533126295, and the total time is 11.00s. It can be seen that this application can quickly calculate and obtain the semi-submersible ship freight solution with the lowest cost under the premise of meeting the actual constraints.

[0159] The embodiment of the present invention optimizes each link of the semi-submersible ship project and establishes the objective function of cost minimization, so that the cost of the semi-submersible ship project can be described and solved by mathematical language, and the overall framework can be accurately solved based on the ant colony algorithm to find the optimization direction; the deck is filled based on the improved residual rectangle method to improve the deck utilization rate; the route and speed optimization are used to reduce the shipping fuel consumption cost. The ship, cargo and port in the semi-submersible ship cargo project are matched, so that the cost of the final overall solution is the minimum value, and at the same time, it can meet multiple constraints in the semi-submersible project to ensure that the loading and transportation plan is accurate and feasible.

[0160] Second embodiment:

[0161] Please refer to Figure 7 As shown, the present invention also provides a hybrid optimization device 100 for module loading of a semi-submersible vessel, comprising a project construction unit 110, which is used to construct an expression of an objective function of the minimum cost of a semi-submersible vessel project according to ship cargo port information and decision variables;

[0162] A cargo optimization unit 120, which is used to iteratively optimize the objective function through an ant colony algorithm to calculate a cargo module loading solution with the optimal objective function;

[0163] A cargo filling unit 130, which is used to generate a cargo module filling plan for each carrier ship based on a residual matrix algorithm and the cargo module loading plan;

[0164] The route optimization unit 140 is used to establish a port sequence set of the carrier ship according to the cargo module filling plan, and search and determine the route with the minimum cost in the port sequence set.

[0165] In one embodiment, in the project construction unit, the objective function expression constructed is as follows:

[0166]

[0167] Among them, C V,jk The cost required to arrange ship j on voyage k can be expressed as:

[0168]

[0169] Among them, C R,j (TVE,k -T VB,k ) is the rental cost of voyage k, Port usage fees, is the fuel consumption during the stay in Hong Kong, The fuel consumption during the voyage is For canal fees, satisfy:

[0170]

[0171] And rV,k The speed decision to minimize the sum of fuel cost and rental for this route is:

[0172] λ rV,k = argmin[λ rV,k C H,j +(1-λ rV,k )C E,j +C R,j ]ΔT rV,k

[0173] The modules of this embodiment are the same as the corresponding steps of the above method embodiment, and will not be repeated here.

[0174] The embodiment of the present invention optimizes each link of the semi-submersible ship project and establishes the objective function of cost minimization, so that the cost of the semi-submersible ship project can be described and solved by mathematical language, and the overall framework can be accurately solved based on the ant colony algorithm to find the optimization direction; the deck is filled based on the improved residual rectangle method to improve the deck utilization rate; the route and speed optimization are used to reduce the shipping fuel consumption cost. The ship, cargo and port in the semi-submersible ship cargo project are matched, so that the cost of the final overall solution is the minimum value, and at the same time, it can meet multiple constraints in the semi-submersible project to ensure that the loading and transportation plan is accurate and feasible.

[0175] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0176] An embodiment of the present invention further provides a computer storage medium on which a computer program is stored. When the program is executed by a processor, the hybrid optimization method for module loading of a semi-submersible vessel as in the above-mentioned embodiments is implemented.

[0177] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the hybrid optimization method for the loading of each semi-submersible ship module as described above. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0178] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the relevant technology. The computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, RAM, ROM, magnetic disks or optical disks.

[0179] Corresponding to the above-mentioned computer storage medium, in one embodiment, a computer device is also provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a hybrid optimization method for semi-submersible vessel module loading as in the above-mentioned embodiments is implemented.

[0180] The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a hybrid optimization method for module loading of a semi-submersible vessel is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0181] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0182] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A hybrid optimization method for module loading of a semi-submersible vessel, characterized in that: The following steps are involved: S1 constructs the expression of the objective function of the minimum cost of the semi-submersible vessel project based on the ship cargo port information and decision variables; S2 iteratively optimizes the objective function through an ant colony algorithm to calculate an optimal cargo module loading plan for the objective function; S3 generates a cargo module filling plan for each carrier ship based on the residual matrix algorithm and the cargo module loading plan; S4: establishing a port sequence set for the carrier ship according to the cargo module filling plan, and searching and determining the route with the minimum cost in the port sequence set.

2. The method according to claim 1, characterized in that In the expression of the objective function for the minimum cost of the semi-submersible vessel project constructed according to the ship cargo port information and the decision variables, the constructed objective function expression is as follows: Among them, C V,jk The cost required to arrange ship j on voyage k can be expressed as: Among them, C R,j (T VE,k -T VB,k ) is the rental cost of voyage k, Port usage fees, is the fuel consumption during the stay in Hong Kong, The fuel consumption during the voyage is For canal fees, satisfy: and The speed decision to minimize the sum of fuel cost and rental for this route is:

3. The method according to claim 1, characterized in that The iterative optimization of the objective function by using an ant colony algorithm to calculate the optimal cargo module loading solution of the objective function includes the following steps: S201 initializes the cargo collection to be loaded in the semi-submersible vessel project and establishes a solution item; S202: establishing a single voyage in the solution item, randomly selecting a carrier ship for the voyage, and calculating the available deck area of ​​the carrier ship according to the pontoon solution; S203, based on 3.5 times of the available area of ​​the deck, selecting and establishing a set of candidate cargoes from the cargoes to be loaded, and loading the set of candidate cargoes onto the deck; S204 repeats step S201 to step S203 until the cargo to be loaded is empty, and calculates the objective function of the solution item; S205 obtains the optimal solution item set through comparison operation and updates the pheromone matrix, and repeats the iteration until the number of iterations reaches a preset value.

4. The method according to claim 1, characterized in that The method of generating a cargo module filling plan for each carrier ship based on the residual matrix algorithm and the cargo module loading plan includes the following steps: S301 obtains the cargo module loading plan of the carrier ship, groups the cargo modules according to the loading port, sorts the modules within the group according to the length, and sorts the modules between groups according to the loading time. The loading time of each group is defined as the earliest planned loading time of all the cargo in the group; S302: taking out a cargo module from a group with a forward grouping sequence, establishing a new row on the side of the available deck area close to the bow, placing the cargo module in the new row, and the width of the new row being equal to the width of the cargo module; S303 placing cargo modules in the new column based on a remaining rectangle algorithm until the new column cannot be filled with any modules; S304 determines whether all cargo modules have been assembled. If not, reselect cargo modules to fill in. Otherwise, the algorithm ends.

5. The method according to claim 1, characterized in that The step of establishing a port sequence set for the carrier ship according to the cargo module filling plan, and searching and determining the route with the minimum cost in the port sequence set, further includes the following steps: S401 establishes port sequence according to cargo loading and unloading ports and time; S402 selects all routes satisfying the canal constraint in sections according to the determined port sequence; S403 calculates the route with the lowest cost in each section through speed decision, and selects the route with the lowest cost; S404 connects the route with the lowest cost in each segment to form a complete route.

6. The method according to claim 5, characterized in that When calculating the objective function in step S2, the objective function needs to meet the following constraints: the number of carrier ships is less than or equal to the total number of voyages, the sum of the decision variables of the carrier ships or voyages is equal to the total number of voyages, the number of carrier ships for a single voyage is 1, the cargo modules are only arranged on one voyage, the cargo modules are placed within the available area of ​​the deck, the distance between the cargo modules is within a preset range, the carrier ships can berth at all ports passed by the corresponding voyage, and the difference between the planned loading and unloading time and the actual loading and unloading time of the cargo modules is less than a preset value.

7. A hybrid optimization device for module loading of a semi-submersible vessel, characterized in that: include: A project construction unit, which is used to construct an expression of the objective function of the minimum cost of the semi-submersible vessel project according to the ship cargo port information and the decision variables; A cargo optimization unit, which is used to iteratively optimize the objective function through an ant colony algorithm to calculate a cargo module loading solution with the optimal objective function; A cargo filling unit, which is used to generate a cargo module filling plan for each carrier ship based on a residual matrix algorithm and the cargo module loading plan; A route optimization unit is used to establish a port sequence set for the carrier ship according to the cargo module filling plan, and to search and determine the route with the minimum cost in the port sequence set.

8. The device according to claim 7, characterized in that In the project construction unit, the objective function expression constructed is as follows: Among them, C V,jk The cost required to arrange ship j on voyage k can be expressed as: Among them, C R,j (T VE,k -T VB,k ) is the rental cost of voyage k, Port usage fees, is the fuel consumption during the stay in Hong Kong, The fuel consumption during the voyage is For canal fees, satisfy: and The speed decision to minimize the sum of fuel cost and rental for this route is:

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the hybrid optimization method for semi-submersible vessel module loading as described in any one of claims 1 to 6 is implemented.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the hybrid optimization method for module loading of a semi-submersible vessel as described in any one of claims 1 to 6 is implemented.

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