A semi-submersible ship module stowage hybrid optimization method, device, equipment and medium

Abstract

The application provides a mixed optimization method for semi-submersible ship module stowage, comprising the following steps: S1, constructing an expression of a target function of minimum cost of a semi-submersible ship project according to ship cargo port information and decision variables; S2, iteratively optimizing the target function by 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 carrying ship based on a residual matrix algorithm and the cargo module stowage scheme; and S4, establishing a port sequence set of the carrying ship according to the cargo module filling scheme, and finding and determining a minimum cost route in the port sequence set. The embodiment of the application optimizes each link in a semi-submersible ship project, so that the cost of the final overall scheme is minimized, and meanwhile, various constraint conditions in the semi-submersible project can be met, so that the loading and transportation plan is feasible.

Description

Technical Field

[0001] This invention relates to the field of ship software development technology, specifically to a hybrid optimization method and apparatus for the loading of modules on a semi-submersible vessel. Background Technology

[0002] Semi-submersible vessels utilize floating technology, offering high flexibility and convenience. They boast large carrying capacity and excellent seaworthiness, making them promising for long-distance transportation. However, the transportation process involving semi-submersible vessels incurs significant costs. These costs primarily consist of vessel rental fees, fuel costs, port berthing fees, canal fees if navigating canals, and other expenses. Cost calculations must also consider cargo loading and unloading times and ports, necessitating route planning and efficient assembly based on the specific loading and unloading times and ports of loading and unloading for different cargo types to minimize costs.

[0003] The cost of a semi-submersible project consists of voyage costs and demobilization costs. Voyage costs are the sum of all voyage costs, while demobilization costs are the sum of the demobilization costs of all vessels used in the project. For a single voyage, the cost = charter cost (number of voyage days × daily charter rate) + fuel cost (number of voyage days × daily fuel consumption) + port usage fee + canal cost (if transited) + other costs; for a single semi-submersible vessel, the demobilization cost = number of voyage days × (daily charter rate + daily fuel consumption). Therefore, minimizing the cost of a semi-submersible project through optimal matching of vessels, cargo, and ports is its main challenge. Firstly, cargo loading must be considered to reduce costs. Since transportation costs show a strong positive correlation with the number of days occupied across all voyages, improving deck utilization and transporting more cargo in fewer voyages, while meeting constraints, yields significant economic benefits.

[0004] Secondly, due to the nature of maritime transport, the calculation scheme needs to meet multiple constraints: a. The loading and unloading port sequence requirements of the voyage must be met; b. The loading and unloading time windows of module batches must be met; c. The module spacing requirements (longitudinal / lateral) and the permitted extension length of the modules must be met, and the modules must not interfere with areas inaccessible to the ship; d. The module travel route must not interfere with inaccessible areas; e. The specific loading position of the modules must meet the hard requirements of actual loading and unloading operations; f. The selected vessels should meet the loading feasibility of the wharf (calculate the empty draft and maximum draft (inlet point restriction) of each vessel, and judge the feasibility in combination with the wharf elevation, water depth, and tide conditions). Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a hybrid optimization method for the loading of semi-submersible vessel modules, comprising:

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

[0007] S2 uses the ant colony algorithm to iteratively optimize the objective function in order to calculate the optimal cargo module loading scheme for the objective function;

[0008] S3 generates a cargo module filling scheme for each carrier vessel based on the residual matrix algorithm and the cargo module loading scheme.

[0009] S4 establishes a port sequence set for the carrier vessels according to the cargo module filling scheme, and finds and determines the route with the lowest cost in the port sequence set.

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

[0011]

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

[0013]

[0014] Among them, C R,j (T VE,k -T VB,k Let ) represent the rental fee for voyage k. For port usage fees, Fuel consumption during the stay in Hong Kong Fuel costs during the voyage For canal fees, satisfy:

[0015]

[0016] and Speed ​​decision to minimize the sum of fuel cost and rental fee for this route:

[0017]

[0018] In one embodiment, the step of iteratively optimizing the objective function using the ant colony algorithm to calculate the optimal cargo module loading scheme includes the following steps:

[0019] S201 Initializes the set of cargoes to be loaded in the semi-submersible vessel project and establishes solutions;

[0020] S202 establishes a single voyage in the solution, randomly selects the carrier vessel for the voyage, and calculates the available deck area of ​​the carrier vessel according to the pontoon scheme;

[0021] S203 Based on 3.5 times the available deck area, select and establish a candidate cargo set from the cargo to be loaded, and load the candidate cargo set onto the deck;

[0022] S204 Repeat steps S201 to S203 until the cargo to be loaded is empty, and calculate the objective function of the solution item;

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

[0024] In one embodiment, generating the cargo module filling scheme for each carrier vessel based on the residual matrix algorithm and the cargo module loading scheme includes the following steps:

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

[0026] S302 Takes out the cargo module from the group with the earlier grouping sequence, creates a new column on the side of the available deck area near the bow, and places the cargo module in the new column, the width of the new column being equal to the width of the cargo module;

[0027] S303 places cargo modules in the new column based on the remaining rectangle algorithm until the new column can no longer be filled with any modules;

[0028] S304 determines whether all cargo modules have been assembled. If not, a new cargo module is selected to fill the assembly; otherwise, the algorithm ends.

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

[0030] S401 establishes port sequence based on the port of loading and unloading and the time of cargo;

[0031] S402 selects all routes that meet the canal constraints in segments based on the determined port sequence;

[0032] S403 uses speed to calculate the route with the lowest cost in each segment and selects the route with the lowest cost.

[0033] S404 connects the least costly routes in each segment to form a complete air route.

[0034] In one embodiment, when calculating the objective function in step S2, the objective function needs to satisfy the following constraints: the number of carrier vessels is less than or equal to the total number of voyages; the sum of the decision variables of carrier vessels or voyages is equal to the total number of voyages; the number of carrier vessels in a single voyage is 1; cargo modules are arranged only on one voyage; cargo modules are placed within the available deck area; the distance between cargo modules is within a preset range; all ports through which the carrier vessels pass in the corresponding voyage can be berthed; 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 the loading of semi-submersible vessel modules, comprising:

[0036] The project construction unit is used to construct an expression for the objective function that minimizes the cost of a semi-submersible vessel project based on ship, cargo, and port information and decision variables.

[0037] The cargo optimization unit is used to iteratively optimize the objective function using an ant colony algorithm to calculate the optimal cargo module loading scheme for the objective function.

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

[0039] The route optimization unit is used to establish a port sequence set for the carrier vessels based on the cargo module filling scheme, and to find and determine the route with the lowest cost in the port sequence set.

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

[0041]

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

[0043]

[0044] Among them, C R,j (T VE,k -T VB,k Let ) represent the rental fee for voyage k. For port usage fees, Fuel consumption during the stay in Hong Kong Fuel costs during the voyage For canal fees, satisfy:

[0045]

[0046] and Speed ​​decision to minimize the sum of fuel cost and rental fee for this route:

[0047]

[0048] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the hybrid optimization method for the loading of semi-submersible vessel modules described above.

[0049] The present invention also provides a computer storage medium storing a computer program that, when executed by a processor, implements the hybrid optimization method for the loading of semi-submersible vessel modules described above.

[0050] This invention optimizes various aspects of a semi-submersible vessel project. By establishing a cost-minimizing objective function, the project cost can be mathematically described and solved. The overall framework can be accurately solved using an ant colony algorithm to identify optimization directions. An improved residual rectangle method is used to fill the deck, increasing deck utilization. Route and speed optimization reduces shipping fuel costs. This ensures a match between ship and cargo ports in the semi-submersible vessel cargo project, minimizing the overall cost of the final solution while satisfying various constraints, ensuring the cargo transportation plan is accurate and feasible. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

[0053] Figure 2 This is a detailed flowchart of S2 in the first embodiment of the present invention;

[0054] Figure 3 This is a detailed flowchart of S3 in the first embodiment of the present invention;

[0055] Figure 4 This is a detailed flowchart of S4 in the first embodiment of the present invention;

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

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

[0058] Figure 7 This is a structural block diagram of the hybrid optimization device mounted on the semi-submersible vessel module according to the 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 Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Well-known modules, units, and their connections, links, communications, or operations are not shown or described in detail. Furthermore, the described features, architectures, or functions can be combined in any way in one or more embodiments. Those skilled in the art should understand that the various embodiments described below are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. It is also readily understood that the modules, units, or processing methods in the various embodiments described herein and shown in the accompanying drawings can be combined and designed in various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] First embodiment:

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

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

[0064] Understandably, the known ship and cargo port information in actual semi-submersible vessel projects is shown in the table below:

[0065]

[0066]

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

[0068]

[0069] Based on the known ship and 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 schedule ship j on voyage k can be expressed as:

[0072]

[0073] Among them, C R,j (T VE,k -T VB,k Let ) represent the rental fee for voyage k. For port usage fees, Fuel consumption during the stay in Hong Kong Fuel costs during the voyage For canal fees, satisfy:

[0074]

[0075] and Speed ​​decision to minimize the sum of fuel cost and rental fee for this route:

[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 achieved:

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

[0079]

[0080] The sum of the decision variables for the vessels or voyages carried equals the total number of voyages:

[0081]

[0082] The number of vessels carried per voyage is 1:

[0083]

[0084] The cargo module is only assigned to one voyage:

[0085]

[0086] The cargo module is placed within the available deck area and can extend outwards as appropriate.

[0087]

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

[0089]

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

[0091]

[0092] When loading and unloading cargo, the side roll or tail roll path must not be obstructed. That is, when side roll, the monotonicity of the cargo loading port sequence on the roll-on / roll-off route must not change; when tail roll, the cargo loading port sequence on the roll-on / roll-off 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 transformed into a mathematical description. Then, by optimizing and solving it, the cost-optimal solution can be obtained.

[0094] S2 uses the ant colony algorithm to iteratively optimize the objective function in order to calculate the optimal cargo module loading scheme for the objective function;

[0095] It is understandable that, such as Figure 2 As shown, this embodiment proposes a method to optimize the assembly scheme by using an ant colony algorithm for matching various ship-cargo-port schemes in a semi-submersible vessel project. Since the optimization of ship cargo module loading is a stochastic search problem, the ant colony algorithm is considered as a heuristic solution. In the ant colony algorithm, the node transfer probability is determined by both pheromone and a heuristic factor. The heuristic factor is the reciprocal of the difference 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 random ship and cargo selection is required, assuming the number of ships to be selected is m, the number of cargoes to be loaded is n, and the size of the pheromone matrix is ​​m*n. The specific steps include:

[0096] S201 Initializes the set of cargoes to be loaded in the semi-submersible vessel project and establishes solutions;

[0097] Understandably, each ant in the ant colony algorithm creates a solution term, and a single solution term is a solution. The set of all solutions is the solution set of each generation of the ant colony algorithm. Each solution contains all voyage information for this loading scheme. A voyage's information includes the selected vessel, cargo, route, and speed for that voyage.

[0098] S202 establishes a single voyage in the solution, randomly selects the carrier vessel for the voyage, and calculates the available deck area of ​​the carrier vessel according to the pontoon scheme;

[0099] Understandably, considering the costs of demobilization and repatriation, priority is given to vessels already at sea, provided their schedules meet the requirements. When all vessels are at sea and their return dates are insufficient to meet the loading time requirements for the cargo to be stowed, a cargo extension operation must be carried out, and the optimal "extension vessel" must be selected. The selected vessel's schedule must meet the requirements.

[0100] S203 Based on 3.5 times the available deck area, select and establish a candidate cargo set from the cargo to be loaded, and load the candidate cargo set onto the deck;

[0101] It is understandable that steps S2 and S3 do not have a strictly sequential order. During the optimization process using the ant colony algorithm, step S203 employs the semi-submersible vessel loading optimization method based on the residual matrix algorithm proposed in step S3 below. This ensures that the loading of each candidate cargo set is reasonable and feasible, and avoids interference problems on the loading path while ensuring that all cargo is loaded. This approach maximizes the use of deck space while meeting loading requirements.

[0102] Specifically, after determining the carrier and the cargo to be loaded, the cost of the voyage is calculated using an objective function. A new ship with a suitable schedule is then selected, the cargo is reloaded, and the cost is calculated again. By comparing the two options, the ship with the lowest cost for each voyage is chosen, thus determining the most suitable carrier for each voyage.

[0103] S204 Repeat steps S201 to S203 until the cargo to be loaded is empty, and calculate the objective function of the solution item;

[0104] Understandably, this involves continuously selecting ships and loading cargo until all cargo to be loaded is assembled. This yields the selected carriers and assembled cargo for all voyages. The cost of each voyage is obtained through objective function techniques, and these costs are accumulated to obtain the solution term, i.e., the total cost of the transportation plan.

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

[0106] Understandably, the algorithm obtains the total cost of each solution in the current generation and selects the optimal solution through cost comparison. After each generation, the pheromone matrix is ​​updated to make the ants' random choices more biased. This step is crucial for the algorithm to converge and favor the better solution.

[0107] If the maximum number of iterations has not been 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 scheme for each carrier vessel based on the residual matrix algorithm and the cargo module loading scheme.

[0109] It is understandable that, such as Figure 3 As shown, this embodiment also proposes a semi-submersible vessel loading optimization method based on the residual rectangle algorithm. Considering the special characteristics of the semi-submersible module loading problem, where obstruction between cargoes of different port sequences cannot occur on the roll-on / roll-off path during loading, the classic residual rectangle method is improved by proposing a column-generated residual rectangle method based on grouping and sorting. In this scheme, to simplify the problem, the available deck area of ​​all vessels is simplified to a standard rectangle, and the shape of all modules is also simplified to a rectangle. Thus, the deck filling problem is transformed into a rectangle filling problem. Furthermore, a coordinate system is established with the ship's axis as X, the positive X-axis direction at the bow, and the origin of the coordinate system at the stern axis point. The port side is the positive Y-axis direction, and the starboard side is the negative Y-axis direction. Specifically, the following steps are included:

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

[0111] S302 Takes out the cargo module from the group with the earlier grouping sequence, creates a new column on the side of the available deck area near the bow, and places the cargo module in the new column, the width of the new column being equal to the width of the cargo module;

[0112] S303 places cargo modules in the new column based on the remaining rectangle algorithm until the new column can no longer be filled with any modules;

[0113] Understandably, the steps of the classic residual matrix algorithm are as follows:

[0114] S3031 sorts all modules to be filled according to their length, 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 according to the order and places it in the lower right corner (the classic method is to place it in the lower left corner, but for the filling problem of the Ro-Ro ship deck, it is generally necessary to start filling from the bow). Then, along the edge of the module, the remaining area of ​​the deck is divided into two remaining rectangles, and these two remaining rectangles are added to the list of remaining rectangles.

[0116] S3033 again selects a module from the list of modules to be filled according to the sorting, and determines which remaining rectangle it should be placed in based on the evaluation function. If none of the remaining rectangles can accommodate it, the next module to be filled in the sorting order is selected, and so on. Once a module to be filled and its required remaining rectangle are selected, the original remaining rectangle is deleted from the list of remaining rectangles, and the two new remaining rectangles obtained by splitting the original remaining rectangle are added to the list of remaining rectangles.

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

[0118] S3035 If, after the merging operation, the largest rectangle in the remaining rectangle list is no longer able to accommodate the smallest rectangle in the list of modules to be filled, then it is determined that there is no usable area left, and the algorithm ends. Otherwise, return to step S3033.

[0119] By combining grouping and sorting strategies with new column creation strategies, this effectively avoids obstruction between goods with different port sequences on the roll-on / roll-off route for most multi-loading / one-unloading problems. This facilitates rapid unloading of goods upon arrival at loading and unloading ports, significantly improving transportation efficiency.

[0120] S304 determines whether all cargo modules have been assembled. If not, a new cargo module is selected to fill the assembly; otherwise, the algorithm ends.

[0121] S4 establishes a port sequence set for the carrier vessels according to the cargo module filling scheme, and finds and determines the route with the lowest cost in the port sequence set.

[0122] Specifically, such as Figure 4 As shown, the step of establishing the port sequence set of the carrier vessels according to the cargo module filling scheme, and finding and determining the route with the lowest cost in the port sequence set, further includes the following steps:

[0123] S401 establishes port sequence based on the port of loading and unloading and the time of cargo;

[0124] Specifically, when establishing the port sequence set, firstly, an empty port sequence array, a loading port array, and a unloading port array are established. Secondly, all cargo loading times for the current voyage are sorted. Then, the loading ports of all cargo are added to the loading port array one by one without repetition. The expected loading date for each loading port is calculated, and the same operation is performed on all unloading ports. Finally, the loading and unloading dates of the loading port array and the unloading port array are mixed and sorted to construct the port sequence array.

[0125] S402 selects all routes that meet the canal constraints in segments based on the determined port sequence;

[0126] Specifically, route selection decision-making involves determining the minimum-cost route for each segment after the port sequence decision, and then connecting them to determine the overall route. The route selection method is a 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 reaches the second element while satisfying the cargo canal constraint. Then find the route from the second element to the third element, also based on minimizing the cost while satisfying the cargo canal constraint. This process continues until the unloading port. A return route from the unloading port to the first loading port is then found; the return route does not need to consider canal constraints. The canal constraint means that in the process from any A to any B, if there is any cargo originating from A that cannot cross the canal, then all routes from A to B that cross the canal are excluded from consideration. The cost of each route is defined as the minimum cost under the optimal speed decision for that route.

[0127] S403 uses speed to calculate the route with the lowest cost in each segment and selects the route with the lowest cost.

[0128] Specifically, current speed decisions only consider "high speed" and "economic speed." At "high speed," fuel costs are higher, but the reduced flight days lower rental costs; at "economic speed," fuel costs are lower, but the increased flight days result in higher rental fees. Therefore, a speed decision must be made for each route segment to select the speed with the lowest overall cost.

[0129] Therefore, the choice of speed can be determined based on 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 C represents the total cost along route r; H For daily fuel costs at high speeds; C E Daily fuel cost at economical cruising speed; C R The daily rental rate for the vessel; λ r Indicates whether to use high speed on route r, λ r =1 indicates high speed, λ r =0 indicates economical cruising speed; ΔT r The time spent on route r.

[0132] Where, ΔT r It should satisfy:

[0133]

[0134] Among them, V H For high speeds; V E The speed of economical cruising speed; D r Let r be the total distance of the route.

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

[0136] S404 connects the lowest-cost routes in each segment to form a complete route. After making decisions on port sequence, route, and speed, connecting the lowest-cost routes selected for each segment allows for the planning of the route for that voyage.

[0137] To facilitate understanding, this embodiment uses a real semi-submersible vessel project as an example for the following simulation explanation:

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

[0139]

[0140] In this embodiment, the cargo to be loaded does not have an estimated unloading date. The longitudinal spacing between the cargo is 2m, the lateral spacing is 2m, and the maximum outward extension is 3m.

[0141] The goods to be loaded are shown in the table below:

[0142]

[0143]

[0144]

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

[0146] The route is as follows:

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

[0148] like Figure 5 As shown, the cargo module, after being optimized using the hybrid optimization method for semi-submersible vessel module loading proposed in this embodiment, yields the following assembly scheme:

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

[0150] The second voyage was 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%, and a total of 7 cargoes were loaded.

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

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

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

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

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

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

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

[0158] This simulation was performed using the ant colony algorithm for 20 iterations. The optimal solutions of the objective function in each iteration are as follows: Figure 6 As shown, the final calculated minimum cost ($) is 52,742,260.533,126,295, with a total time of 11.00 seconds. This demonstrates that, under the premise of satisfying practical constraints, this application can quickly calculate and obtain the semi-submersible vessel freight transport solution with the lowest cost.

[0159] This invention optimizes various aspects of a semi-submersible vessel project. By establishing a cost-minimizing objective function, the project cost can be mathematically described and solved. The overall framework can be accurately solved using an ant colony algorithm to identify optimization directions. An improved residual rectangle method is used to fill the deck, increasing deck utilization. Route and speed optimization reduces shipping fuel costs. This ensures a match between ship and cargo ports in the semi-submersible vessel cargo project, minimizing the overall cost of the final solution while satisfying various constraints, ensuring the cargo 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 the loading of semi-submersible vessel modules, including a project construction unit 110, which is used to construct an expression for the objective function of minimizing the cost of the semi-submersible vessel project based on ship, cargo, and port information and decision variables.

[0162] Cargo optimization unit 120 is used to iteratively optimize the objective function using ant colony algorithm to calculate the optimal cargo module loading scheme for the objective function.

[0163] Cargo filling unit 130 is used to generate a cargo module filling scheme for each carrier vessel based on the residual matrix algorithm and the cargo module loading scheme.

[0164] The route optimization unit 140 is used to establish a port sequence set of the carrier vessels according to the cargo module filling scheme, and to find and determine the route with the lowest cost in the port sequence set.

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

[0166]

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

[0168]

[0169] Among them, C R,j (TVE,k -T VB,k Let ) represent the rental fee for voyage k. For port usage fees, Fuel consumption during the stay in Hong Kong Fuel costs during the voyage For canal fees, satisfy:

[0170]

[0171] And λ rV,k Speed ​​decision to minimize the sum of fuel cost and rental fee for this route:

[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 in this embodiment are the same as the corresponding steps in the above method embodiments, and will not be repeated here.

[0174] This invention optimizes various aspects of a semi-submersible vessel project. By establishing a cost-minimizing objective function, the project cost can be mathematically described and solved. The overall framework can be accurately solved using an ant colony algorithm to identify optimization directions. An improved residual rectangle method is used to fill the deck, increasing deck utilization. Route and speed optimization reduces shipping fuel costs. This ensures a match between ship and cargo ports in the semi-submersible vessel cargo project, minimizing the overall cost of the final solution while satisfying various constraints, ensuring the cargo transportation plan is accurate and feasible.

[0175] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be 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 be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0176] This invention also provides a computer storage medium storing a computer program that, when executed by a processor, implements the hybrid optimization method for semi-submersible vessel module loading as described in the above embodiments.

[0177] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the hybrid optimization method carried by the semi-submersible modules described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can 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 a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0178] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, terminal, or network device, etc.) to execute all or part of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, RAM, ROM, magnetic disks, or optical disks.

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

[0180] This computer device can be a terminal, and its internal structure diagram can be as follows: Figure 8As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a hybrid optimization method for the loading of semi-submersible modules. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device casing, or an external keyboard, touchpad, or mouse.

[0181] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A hybrid optimization method for the trim of a semi-submersible vessel module, characterized in that, Includes the following steps: S1 constructs an expression for the objective function to minimize the cost of the semi-submersible vessel project based on ship and cargo port information and decision variables, wherein the expression for the objective function is as follows: ； in, The number of voyages for the project; The number of available vessels for the project; Whether vessel j is scheduled for voyage k. Yes, Indicates no; Whether or not ship j is in use, Indicates use, Indicates that it is not used; Other expenses for the vessel; This refers to the daily fuel consumption during the ship's decommissioning period. The daily rental rate for vessel j; For vessel j, the number of days of demobilization / recovery. The cost required to schedule ship j on voyage k can be expressed as: ； in, The rental fee for voyage k; The number of port sequences actually executed for voyage k; The number of routes actually executed in voyage k; Let k be the number of canals traversed during voyage k. Let k be the end time of voyage k; The start time of voyage k; For port usage fees, Fuel consumption during the stay in Hong Kong; The daily fuel cost for vessel J while it is berthed in port; For voyage k at the port The time spent on it; Fuel costs during navigation; Whether to use high speed on route r for voyage k. =1 indicates high speed. =0 indicates economical cruising speed; The daily fuel consumption cost of a ship at high speed; The daily fuel consumption cost of a ship at its economic cruising speed. For voyage k on the route The time spent on it; For canal fees; The cost for a fully loaded vessel (j) to pass through canal (c). For voyage k, is the canal crossing empty or fully loaded? This indicates full load; otherwise, it indicates no load. The cost for ship j to pass through canal c unloaded; satisfy: ； And the speed decision that minimizes the sum of fuel and charter costs for the route; the high speed for the ship j; the total distance of the route r actually performed for the voyage k; the economic speed for the ship j: ； wherein, is the time spent by the voyage k on the route ; S2 iteratively optimizes the objective function using an ant colony algorithm to calculate the optimal cargo module loading scheme, including: S201 Initializes the set of cargoes to be loaded in the semi-submersible vessel project and establishes solutions; S202 establishes a single voyage in the solution, randomly selects the carrier vessel for the voyage, and calculates the available deck area of ​​the carrier vessel according to the pontoon scheme; S203 Based on 3.5 times the available deck area, select and establish a candidate cargo set from the cargo to be loaded, and load the candidate cargo set onto the deck; S204 Repeat steps S201 to S203 until the cargo to be loaded is empty, and calculate the objective function of the solution item; S205 obtains the optimal solution set through comparison operations and updates the pheromone matrix, repeating the iteration until the number of iterations reaches a preset value; S3 generates a cargo module filling scheme for each carrier vessel based on the residual matrix algorithm and the cargo module loading scheme, including: S301 obtains the cargo module loading plan of the carrier vessel, groups the cargo modules according to the loading port, sorts them within the group according to length, and sorts them between groups according to loading time. The loading time of each group is defined as the earliest planned loading time of all cargo in that group. S302 Takes out the cargo module from the group with the earlier grouping sequence, creates a new column on the side of the available deck area near the bow, and places the cargo module in the new column, the width of the new column being equal to the width of the cargo module; S303 places cargo modules in the new column based on the remaining rectangle algorithm until the new column can no longer be filled with any modules; S304 checks whether all cargo modules have been assembled. If not, a new cargo module is selected for filling; otherwise, the algorithm ends. S4 establishes a port sequence set for the carrier vessels according to the cargo module filling scheme, and finds and determines the route with the lowest cost in the port sequence set.

2. The method of claim 1, wherein, The step of establishing the port sequence set of the carrier vessels according to the cargo module filling scheme, and finding and determining the route with the lowest cost in the port sequence set, further includes the following steps: S401 establishes port sequence based on the ports of loading and unloading of goods and the time of loading and unloading; S402 selects all routes that meet the canal constraints in segments based on the determined port sequence; S403 uses speed to calculate the route with the lowest cost in each segment and selects the route with the lowest cost. S404 connects the least costly routes in each segment to form a complete air route.

3. The method of claim 2, wherein, When calculating the objective function in step S2, the objective function needs to satisfy the following constraints: the number of carrier vessels is less than or equal to the total number of voyages; the sum of the decision variables of carrier vessels or voyages is equal to the total number of voyages; the number of carrier vessels in a single voyage is 1; cargo modules are arranged only on one voyage; cargo modules are placed within the available deck area; the distance between cargo modules is within a preset range; all ports through which the carrier vessels pass in the corresponding voyage can be berthed; 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.

4. A hybrid optimization device for the trim of a semi-submersible vessel module, characterized in that, include: The project construction unit is used to construct an expression for the objective function that minimizes the cost of the semi-submersible vessel project based on ship, cargo, and port information and decision variables. The expression for the objective function is as follows: ； in, The number of voyages for the project; The number of available vessels for the project; Whether vessel j is scheduled for voyage k. Yes, Indicates no; Whether or not ship j is in use, Indicates use, Indicates that it is not used; Other expenses for the vessel; This refers to the daily fuel consumption during the ship's decommissioning period. The daily rental rate for vessel j; For vessel j, the number of days of demobilization; The cost required to schedule ship j on voyage k can be expressed as: ； in, The rental fee for voyage k; The number of port sequences actually executed for voyage k; The number of routes actually executed in voyage k; Let k be the number of canals traversed during voyage k. Let k be the end time of voyage k; The start time of voyage k; For port usage fees, Fuel consumption during the stay in Hong Kong; The daily fuel cost for vessel J while it is berthed in port; For voyage k at the port The time spent on it; Fuel costs during navigation; Whether to use high speed on route r for voyage k. =1 indicates high speed. =0 indicates economical cruising speed; The daily fuel consumption cost of a ship at high speed; The daily fuel consumption cost of a ship at its economic cruising speed. For voyage k on the route The time spent on it; For canal fees; The cost for a fully loaded vessel (j) to pass through canal (c). For voyage k, is the canal crossing empty or fully loaded? This indicates full load; otherwise, it indicates no load. The cost for ship j to pass through canal c unloaded; satisfy: ； and Speed ​​decision to minimize the sum of fuel costs and rental fees for this route; For the high speed of ship j; The total distance of the route r actually followed in voyage k; For the economical speed of ship j: ； wherein, is the time spent by the voyage k on the route ; A cargo optimization unit, used to iteratively optimize the objective function using an ant colony algorithm to calculate the optimal cargo module loading scheme, includes: Initialize the set of cargoes to be loaded in the semi-submersible vessel project and establish solutions; In the solution, a single voyage is established, the carrier vessel for the voyage is randomly selected, and the available deck area of ​​the carrier vessel is calculated according to the pontoon scheme. Based on 3.5 times the available deck area, a candidate cargo set is selected from the cargo to be loaded and a candidate cargo set is established, and the candidate cargo set is loaded onto the deck. Repeat the above steps until the cargo to be loaded is empty, and calculate the objective function of the solution term; The optimal set of solutions is obtained through comparison operations and the pheromone matrix is ​​updated. This process is repeated until the number of iterations reaches a preset value. A cargo filling unit, used to generate a cargo module filling scheme for each carrier vessel based on the residual matrix algorithm and the cargo module loading scheme, including: Obtain the cargo module loading plan of the carrier vessel, group the cargo modules according to the port of loading, sort them within the group according to length, and sort them between groups according to loading time. The loading time of each group is defined as the earliest planned loading time of all cargo in that group. Take the cargo module from the group with the earlier grouping order, create a new column on the side of the available deck area near the bow, place the cargo module in the new column, and the width of the new column is equal to the width of the cargo module; The cargo modules are placed in the new column based on the remaining rectangle algorithm until the new column can no longer be filled with any modules; Determine if all cargo modules have been assembled. If not, select new cargo modules to fill; otherwise, terminate the algorithm. The route optimization unit is used to establish a port sequence set for the carrier vessels based on the cargo module filling scheme, and to find and determine the route with the lowest cost in the port sequence set.

5. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the hybrid optimization method for the loading of semi-submersible modules as described in any one of claims 1 to 3.

6. A computer storage medium having stored thereon a computer program, characterized in that When executed by the processor, the program implements the hybrid optimization method for the loading of semi-submersible modules as described in any one of claims 1 to 3.

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