An optimization method for Arctic shipping route fleet scheduling based on BeiDou III

Abstract

The present invention provides an Arctic waterway fleet scheduling optimization method based on BeiDou III, comprising: designing an Arctic waterway multi-base station fleet information service framework based on BeiDou III, reshaping the information interaction process between ships and between ships and shores under BeiDou service; constructing an Arctic waterway fleet scheduling optimization model based on BeiDou III based on the designed Arctic waterway multi-base station fleet information service framework; and improving the differential evolution algorithm to solve the non-deterministic polynomial problem in the fleet scheduling process. The present invention designs a BeiDou-based multi-base station fleet information service framework to provide support for the dynamic scheduling of Arctic waterway fleets, solving the problem of difficulty in real-time information interaction on Arctic routes. With the support of the BeiDou multi-base station fleet information service framework, the information obtained by the framework is used to solve the dynamic scheduling optimization model of the Arctic waterway fleet. The model enables the fleet to quickly modify the freight scheduling plan after the navigation environment information is distorted, thereby reducing economic losses.

Description

Technical Field

[0001] The present invention relates to the technical fields of the communications industry and the maritime transport industry, and more specifically, to a method for optimizing Arctic waterway fleet scheduling based on BeiDou III. Background Art

[0002] In recent years, the global shipping crisis has directly resulted in trade losses of $10 billion per day. Using the Arctic route can effectively alleviate these difficulties, with transit voyages increasing by an average of 17.2% over the past five years. In response to this trend, major shipping companies have begun implementing cargo plans using fleets instead of single vessels.

[0003] Compared to traditional shipping routes, the harsh ocean, climate, and communication conditions of Arctic shipping routes lead to a high degree of uncertainty in fleet scheduling. Fleets often face issues such as distortion in existing communication methods, such as weather forecasts, during navigation. Over the past five years, direct economic losses from accidents in Arctic shipping routes have increased by 25.8% year-on-year. With the successful launch of the BeiDou-3 satellite in 2022, the BeiDou satellite navigation system has achieved full coverage of the Arctic shipping routes. However, the application of the BeiDou-3 satellite navigation system in Arctic shipping routes is still in its infancy, and there is a lack of an information service framework suitable for fleet scheduling in these routes. Summary of the Invention

[0004] In response to the technical problems raised above, a method for optimizing fleet scheduling in the Arctic route based on BeiDou III is provided. This paper designs a multi-base station fleet information service framework based on the BeiDou III satellite navigation system, provides a top-level design for a real-time dynamic interaction framework between ships and ships, and between ships and shores, and provides a design scheme for BeiDou regional short messages within the framework. This scheme enables real-time updates of a ship's current spatiotemporal status and future available ports of call. Based on the designed BeiDou III information service framework, an optimization model for fleet scheduling in the Arctic route is established, and an improved differential evolution algorithm is used to solve the non-deterministic polynomial (NP-hard) problem in the fleet scheduling process.

[0005] The technical means adopted in the present invention are as follows:

[0006] A BeiDou-3-based fleet scheduling optimization method for Arctic routes includes:

[0007] S1. Design a multi-base station fleet information service framework for Arctic routes based on BeiDou-3, and reshape the information exchange process between ships and between ships and shores under BeiDou services;

[0008] S2. Based on the designed Arctic shipping route multi-base station fleet information service framework, a BeiDou-3-based Arctic shipping route fleet scheduling optimization model is constructed;

[0009] S3. Improve the differential evolution algorithm to solve the non-deterministic polynomial problem in the fleet scheduling process.

[0010] Furthermore, step S1 specifically includes:

[0011] S11. The Arctic route ocean-going fleet will be equipped with various terminals to transmit information;

[0012] S12, the BeiDou III satellite receives BeiDou short messages from the terminal and ground base station and completes the forwarding of the corresponding messages;

[0013] S13. After receiving the short message from the terminal, the ground support system quickly connects to the shore-based information release and management platform through the Internet. The shore-based system immediately generates the subsequent dispatch plan for the fleet and forwards it back to the terminal, thereby ensuring the security and efficiency of information exchange between the ship and shore sides.

[0014] Furthermore, in step S1, the integration of fleet, hydrological, and discrete information is completed within the designed Arctic route multi-base station fleet information service framework through Beidou short messages. The design of short messages includes coding design and data packet structure design, specifically including:

[0015] The coding design adopts BCD coding, and the interactive data includes ship information and environmental information;

[0016] The data packet structure design includes:

[0017] Divide long packets into sub-packets according to the Beidou standard. That is, the encoded monitoring data is "unpacked" at the sending end, and corresponding "unpacking control" is added to identify the uniqueness of each sub-packet.

[0018] At the receiving end, the "unpacking control" is removed from each sub-packet, and the data is merged according to the information of the "unpacking control" to restore the original message correctly and orderly;

[0019] A 3-byte "depacket control" message is added to the header of each sub-packet, and the remaining 74 bytes of each sub-packet are used for monitoring data;

[0020] Redundancy calculation is performed when the monitoring data length of the last sub-packet is less than 74 bytes;

[0021] Perform an exclusive OR (XOR) operation on each 74-byte sub-packet to obtain a redundant check packet, which carries "depacketization control" information and is sent together with the previous three sub-packets;

[0022] As long as the receiver successfully receives any three data packets in the forward error correction block, the complete monitoring information can be successfully restored.

[0023] Furthermore, step S2 specifically includes:

[0024] S21. With the help of a multi-base station fleet information service framework for the Arctic route, the shore can quickly respond to environmental changes in the fleet's current route. Based on this, a generalized spatiotemporal network for coordinated scheduling between ships in the NSR fleet is designed to dynamically update whether a ship can sail on a certain route segment at a certain time and space.

[0025] S22. Construct the objective function of the BeiDou-3-based Arctic shipping fleet scheduling optimization model as follows:

[0026]

[0027] Where F represents the fitness function; S represents the fleet ship set; C represents the cargo set; UA represents the unloading arc set in the cargo subnet; represents the carrier’s net profit on the arc (i, j); represents a 0-1 variable. If cargo c is loaded from the loading node origin i of ship s in the cargo subnet to the destination node j in the cargo subnet, then otherwise SA s represents the arc set s in the ship subnet; represents the cost of the navigation arc in the ship subnetwork, which includes ship capital, fuel and voyage-related costs; represents a 0-1 variable. If ship s sails along the arc (i, j) in the ship subnet, then otherwise LA represents the set of loading arcs in the cargo subnetwork; represents the loading cost of cargo c on the transportation arc (i, j); represents a 0-1 variable. If cargo c is loaded from the origin i of the cargo subnet to the loading node j of ship s in the cargo subnet, then otherwise N c Represents the set of nodes in the cargo subnet; represents a 0-1 variable. If cargo c is transferred from ship s1 to ship s2 at port i, then otherwise represents a 0-1 variable. If ship s sails along the arc (i, j) in the ship subnetwork and rents an icebreaker, then otherwise CI ij represents the charge of the icebreaker on the arc (i, j);

[0028] S23. Set the constraints as follows:

[0029] Constraint 1: Among them, NWs represents the node set s in the ship subnet; represents a 0-1 variable. If ship s sails along the arc (j, k) in the ship subnet, then otherwise Indicates that the ship's destination is in the ship subnet; Indicates that the ship's origin is in the ship subnet;

[0030] Constraint 2: in, represents a 0-1 variable. If cargo c is loaded from the loading node origin i of ship s1 in the cargo subnet to the destination node j in the cargo subnet, then otherwise represents a 0-1 variable. If cargo c is transferred from ship s1 to ship s2 at port j, then otherwise represents a 0-1 variable. If cargo c is sailing along the arc (j, k) on ship s1, then otherwise represents a 0-1 variable. If cargo c is loaded from the loading node origin j of ship s1 in the cargo subnet to the destination node r in the cargo subnet, then otherwise D c Indicates that the delivery location of cargo c is in the cargo O / D subnet; c Indicates that the shipping location of cargo c is in the cargo O / D subnet; represents the unloading subnode in the cargo subnetwork; Represents the loading subnode in the cargo subnet; N c Represents the set of nodes in the cargo subnet;

[0031] Constraint Three:

[0032] Constraint Four:

[0033] Constraint Five: Where T represents the set of ship types; VA s represents the navigation arc set s in the ship subnet;

[0034] Constraint Six: Wherein, M represents a large positive number.

[0035] Furthermore, in step S21, the designed generalized spatiotemporal network for coordinated scheduling between ships in the NSR fleet includes a ship subnet and a cargo subnet, wherein:

[0036] The ship subnet represents the current available ports for all ships to dock. In the construction of the ship subnet, assuming that the fleet has k ships, k ship subnets are established. Each ship has two types of arcs: waiting arcs and sailing arcs. The waiting arc indicates that the ship is allowed to stay at the port and wait for the next mission; the sailing arc indicates that the ship is allowed to travel from one port to another within a certain time period.

[0037] The cargo subnet consists of a cargo flow subnet and a cargo O / D subnet. The cargo flow subnet displays the fleet's resource allocation plan, and the cargo O / D subnet represents the actual route of the corresponding ship. The cargo subnet can simulate the cargo transportation process. Assuming that each cargo task corresponds to a cargo subnet, the cargo subnet can truly reflect the fleet's NSR dynamic planning and the collaborative operation scenarios between ships in the fleet.

[0038] Furthermore, step S3 specifically includes:

[0039] S31. In order to reduce the search space, freight tasks with the same starting port and destination port are grouped. A group of task groups forms a task sequence. Finally, the freight tasks are converted into task sequences and the task sequences are studied.

[0040] S32, introduce the elite strategy, directly copy the parent individual with the highest fitness value to the next generation, and select the remaining individuals normally;

[0041] S33. Use the improved DE / rand-to-best / 1 strategy to perform mutation operations on all ships in the selected chromosome. That is, two randomly selected individuals are differentially calculated with the best individual in the current population. Since the new gene already contains the optimal information, a new variant can be directly generated using the generated differential vector.

[0042] S34, looping through steps S32 and S33. When the fitness function of the optimal solution has the same result four times in a row, the loop ends.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] 1. The present invention provides an Arctic waterway fleet scheduling optimization method based on BeiDou III, designs a BeiDou-based multi-base station fleet information service framework to provide support for the dynamic scheduling of Arctic waterway fleets, and solves the problem of difficulty in real-time information exchange on Arctic routes.

[0045] 2. This invention provides a method for optimizing Arctic shipping fleet scheduling based on BeiDou-3. This method, supported by the BeiDou multi-base station fleet information service framework, uses information generated by this framework to solve a dynamic scheduling optimization model for Arctic shipping fleets. This model enables fleets to rapidly modify freight scheduling plans in the event of distorted navigational environment information, minimizing economic losses.

[0046] Based on the above reasons, the present invention can be widely promoted in the fields of communications industry, maritime transportation industry, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0048] Figure 1 Flow chart of the method of the present invention.

[0049] Figure 2 This invention is an Arctic route multi-base station information service framework based on BeiDou III.

[0050] Figure 3 This is the short message data packet structure based on BeiDou III of the present invention.

[0051] Figure 4 This is the ship subnet of the Arctic shipping fleet of the present invention.

[0052] Figure 5 This is the cargo subnet of the Arctic shipping fleet of the present invention.

[0053] Figure 6 This is the flowchart of the improved differential evolution algorithm of the present invention.

[0054] Figure 7 Schematic diagram of the mutation strategy of the differential evolution algorithm of the present invention. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0056] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0057] like Figure 1 As shown, the present invention provides an Arctic shipping route fleet scheduling optimization method based on BeiDou III, comprising:

[0058] S1. Design a multi-base station fleet information service framework for Arctic routes based on BeiDou-3, and reshape the information exchange process between ships and between ships and shores under BeiDou services;

[0059] S2. Based on the designed Arctic shipping route multi-base station fleet information service framework, a BeiDou-3-based Arctic shipping route fleet scheduling optimization model is constructed;

[0060] S3. Improve the differential evolution algorithm to solve the non-deterministic polynomial (NP-hard) problem in the fleet scheduling process.

[0061] In specific implementation, as a preferred embodiment of the present invention, step S1 specifically includes:

[0062] S11. The Arctic route ocean-going fleet will be equipped with various terminals to complete the transmission of information. Figure 2 ① in

[0063] S12, BeiDou III satellite receives BeiDou short messages from terminals and ground base stations, and completes the forwarding of corresponding messages, see Figure 2 ② in the

[0064] S13. After receiving the short message from the terminal, the ground support system quickly connects to the shore-based information release and management platform through the Internet. Figure 2 In ③, the shore side immediately generates the subsequent dispatch plan of the fleet and forwards it back to the terminal to ensure the security and efficiency of information exchange between the ship side and the shore side. Figure 2 ④-⑥ in it.

[0065] In the dynamic dispatching scheme of the Arctic waterway fleet based on BeiDou III, the ship and shore need to interact through the multi-base station fleet information service framework based on BeiDou III, and design the real-time dispatching scheme of the Arctic waterway fleet based on the interactive information. In this embodiment, a multi-base station fleet information service framework based on BeiDou is designed to reshape the information interaction process between ships and between ships and shores under BeiDou service (such as Figure 2 Under the premise of complying with international maritime regulations and relevant Chinese standards, the shore-based information management platform, ground support system, terminal system, and BeiDou-3 satellite constellation constitute the BeiDou-based multi-base station fleet information service framework for the Arctic route.

[0066] In specific implementation, as a preferred embodiment of the present invention, in step S1, the integration of fleet, hydrological, and discrete information is completed within the designed Arctic route multi-base station fleet information service framework through Beidou short messages. The design of the short message includes coding design and data packet structure design, specifically including:

[0067] The coding design adopts BCD coding, and the interactive data includes ship information and environmental information;

[0068] In this embodiment, the encoding design aims to minimize message data size to reduce the probability of interference during information transmission. Considering that BCD encoding offers better channel utilization than ASCII and can reduce overall data size to a certain extent, this study proposes to adopt BCD encoding. The exchanged data includes vessel information and environmental information. The data encoding is shown in Table 1. Short messages transmitted and received on Arctic routes are low-frequency, placing higher demands on data integrity and security. This necessitates the design of a separate data packet structure to improve bandwidth utilization and communication reliability.

[0069] Table 1. Information coding design table

[0070]

[0071] The data packet structure design is to design a data packet structure (such as) that combines the complex environmental factors (hydrology, visibility, etc.) faced by the Arctic route fleet during navigation, as well as cargo information, ship position and speed, fuel consumption and other sensor data. Figure 3) to improve communication reliability. Since a BeiDou ID card terminal's data packet does not exceed 77 bytes, it is clearly impossible to transmit all information at once. Therefore, according to the BeiDou standard, long packets are divided into sub-packets. The encoded monitoring data is "unpacked" at the transmitter, and corresponding "unpacking control" information is added to uniquely identify each sub-packet. At the receiver, the "unpacking control" is removed from each sub-packet, and the data is merged based on the "unpacking control" information to correctly and orderly recover the original message. A 3-byte "unpacking control" message is added to the header of each sub-packet, and the remaining 74 bytes of each sub-packet are used for monitoring data. Redundancy calculation is performed when the monitoring data length of the last sub-packet is less than 74 bytes. An exclusive OR (XOR) operation is performed on each 74-byte sub-packet to generate a redundant check packet with "unpacking control" information, which is sent along with the previous three sub-packets. As long as the receiver successfully receives any three data packets in the forward error correction (FEC) block, the complete monitoring information can be successfully recovered. Compared with existing solutions, this solution can better address the high message distortion rate on Arctic shipping routes.

[0072] In specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes:

[0073] S21. With the help of a multi-base station fleet information service framework for the Arctic route, the shore can quickly respond to environmental changes in the fleet's current route. Based on this, a generalized spatiotemporal network for coordinated scheduling between ships in the NSR fleet is designed to dynamically update whether a ship can sail on a certain route segment at a certain time and space.

[0074] S22. Construct the objective function of the BeiDou-3-based Arctic shipping fleet scheduling optimization model as follows:

[0075]

[0076] Where F represents the fitness function; S represents the fleet ship set; C represents the cargo set; UA represents the unloading arc set in the cargo subnet; represents the carrier’s net profit on the arc (i, j); represents a 0-1 variable. If cargo c is loaded from the loading node origin i of ship s in the cargo subnet to the destination node j in the cargo subnet, then otherwise SA s represents the arc set s in the ship subnet; represents the cost of the navigation arc in the ship subnetwork, which includes ship capital, fuel and voyage-related costs; represents a 0-1 variable. If ship s sails along the arc (i, j) in the ship subnet, then otherwise LA represents the set of loading arcs in the cargo subnetwork; represents the loading cost of cargo c on the transportation arc (i, j); represents a 0-1 variable. If cargo c is loaded from the origin i of the cargo subnet to the loading node j of ship s in the cargo subnet, then otherwise N c Represents the set of nodes in the cargo subnet; represents a 0-1 variable. If cargo c is transferred from ship s1 to ship s2 at port i, then otherwise represents a 0-1 variable. If ship s sails along the arc (i, j) in the ship subnetwork and rents an icebreaker, then otherwise CI ij represents the charge of the icebreaker on the arc (i, j);

[0077] S23. Set the constraints as follows:

[0078] constraint-:

[0079] Among them, NW s represents the node set s in the ship subnet; represents a 0-1 variable. If ship s sails along the arc (j, k) in the ship subnet, then otherwise Indicates that the ship's destination is in the ship subnet; Indicates that the ship's origin is in the ship subnet;

[0080] Constraint 2:

[0081] in, represents a 0-1 variable. If cargo c is loaded from the loading node origin i of ship s1 in the cargo subnet to the destination node j in the cargo subnet, then otherwise represents a 0-1 variable. If cargo c is transferred from ship s1 to ship s2 at port j, then otherwise represents a 0-1 variable. If cargo c is sailing along the arc (j, k) on ship s1, then otherwise represents a 0-1 variable. If cargo c is loaded from the loading node origin j of ship s1 in the cargo subnet to the destination node r in the cargo subnet, then otherwise Dc Indicates that the delivery location of cargo c is in the cargo O / D subnet; c Indicates that the shipping location of cargo c is in the cargo O / D subnet; represents the unloading subnode in the cargo subnetwork; Represents the loading subnode in the cargo subnet; N c Represents the set of nodes in the cargo subnet;

[0082] Constraint three:

[0083] Constraint Four:

[0084] Constraint Five:

[0085] Where T represents the set of ship types; VA s represents the navigation arc set s in the ship subnet;

[0086] Constraint Six:

[0087] Wherein, M represents a large positive number.

[0088] In this embodiment, constraint one is used to constrain flow conservation in the ship subnet; constraint two is used to constrain flow conservation in the cargo subnet, that is, the arc flow pointing to a node (including the navigation arc and the transshipment arc pointing to the node) plus the supply of the node (if any) is equal to the sum of the arc flow leaving the node plus the demand of the node (if any); constraints three and four are used to ensure that every piece of cargo is shipped; constraint five is used to constrain the capacity of the ship and limit the amount of cargo that the ship can load; constraint six is ​​used to ensure that the cargo has been carried by the ship.

[0089] In specific implementation, as a preferred embodiment of the present invention, in step S21, the generalized spatiotemporal network for coordinated scheduling of ships in the NSR fleet is designed to include a ship subnet (see Figure 4 ) and cargo subnet (see Figure 5 ),in:

[0090] The ship subnet represents the current available ports for all ships. In the construction of the ship subnet, it is assumed that the fleet has k ships, such as Figure 4 As shown in the figure, k ship subnetworks are established. Each ship has two types of arcs: waiting arc and sailing arc. The waiting arc indicates that the ship is allowed to stay in the port and wait for the next mission; the sailing arc indicates that the ship is allowed to travel from one port to another within a certain period of time.

[0091] The cargo subnet is composed of a cargo flow subnet and a cargo O / D subnet. The cargo flow subnet shows the resource allocation plan of the fleet, and the cargo O / D subnet represents the actual route of the corresponding ship. The cargo subnet can simulate the process of cargo transportation. Assume that each cargo task corresponds to a cargo subnet, such as Figure 5 As shown in the figure, the cargo subnet can truly reflect the dynamic planning of the fleet NSR and the collaborative operation between ships in the fleet (such as cargo transshipment). The specific process is as follows:

[0092] like Figure 5 As shown in the cargo O / D subnet diagram in , assuming that the contract stipulates that a certain cargo is transported from port 1 to port 2, at this time, ship 1 successfully loads the cargo through loading arc 1;

[0093] When ship 1 was heading to port 2 as planned, due to a sudden change in the route environment, it became unnavigable. Therefore, it sent a BeiDou short message to the shore. The shore updated the ship subnet based on this information in real time, deleted the navigable arc from port 1 to port 2 in the subnet corresponding to ship 1, and sent subsequent instructions to ship 1. Based on the information sent back by the shore, ship 1 docked at the nearest port 3 and waited.

[0094] Vessel k is a high-ice-class vessel that meets the transshipment requirements and is docked at Port 3. Therefore, the shore terminal sends a short message to Vessel k requesting assistance with transshipment from Vessel 1. After receiving the transshipment request, Vessel k loads the contract cargo that was originally planned to be transported by Vessel 1 to Port 2 via transshipment arc 2.

[0095] Vessel k continues its subsequent mission and successfully arrives at Port 2, completing the cargo transportation via Unloading Arc 3. If it does not arrive on the fifth day as stipulated in the contract, it will complete the cargo transportation via Unloading Arc 4. At this time, a penalty will be calculated based on the duration of the breach of contract.

[0096] During specific implementation, as a preferred embodiment of the present invention, a solution algorithm is designed for the above-mentioned mathematical model. After analysis, it is known that the mathematical model will evolve into a pure network flow problem after simplifying the constraints, and the solution is an NP-hard problem. Although the method based on Lagrangian relaxation is a solution strategy, preliminary experiments show that the boundaries obtained using this method are relatively loose and the convergence speed is very slow, so it is considered to use a heuristic algorithm for solution. The characteristics of the problem studied in this embodiment are: when the freight plan of a single ship changes, the fleet as a whole adjusts the subsequent freight plan to achieve the global optimum. Some scholars use genetic algorithms to solve this problem, but a single mutation of the genetic algorithm will only adjust the freight plan on one ship, which cannot meet the needs of rapid response to the simultaneous mutation optimization of multiple ships in the fleet. The advantage of the differential evolution algorithm is that it can mutate the chromosomes of all affected ships at the same time. Therefore, this embodiment attempts to design an improved differential evolution algorithm to solve this problem. The algorithm is innovatively designed for the chromosome encoding design and mutation of the dynamic scheduling optimization problem of the Arctic waterway fleet. Figure 6 A detailed diagram of the DE algorithm process is provided, showing each key step from initialization to the iteration process, namely step S3, which specifically includes:

[0097] S31. In order to reduce the search space, freight tasks with the same starting port and destination port are grouped. A group of task groups forms a task sequence. Finally, the freight tasks are converted into task sequences and the task sequences are studied.

[0098] S32, introduce the elite strategy, directly copy the parent individual with the highest fitness value to the next generation, and select the remaining individuals normally;

[0099] S33. After any ship is affected by the Arctic shipping environment, the navigation plan of the entire fleet may change. Therefore, all ships in each solution sequence should participate in the mutation at the same time to increase the optimization efficiency. Using the improved DE / rand-to-best / 1 strategy, all ships in the selected chromosome are mutated separately; that is, two randomly selected individuals are differentially calculated with the best individual in the current population. Since the new gene already contains the optimal information, a new variant can be directly generated through the generated differential vector; the specific operation is as follows Figure 7 As shown, based on the two selected individuals, traverse the common tasks of all enabled ships of the two, determine the order difference of the tasks, then find the difference in the freight tasks in the task sequences of the two and store the information, and then define an operation instruction based on the above two differences to form a differential vector. For example, all the same freight tasks in ship No. 1 of the two initial solutions have no order difference, and initial solution 1 needs to insert corresponding tasks to reduce the difference, so as to record the above exchange and insertion operations into the differential vector. Finally, the differential vector and the variation factor are used to adjust the current optimal individual to generate a new individual, and the fitness of the individual is calculated.

[0100] S34, looping through steps S32 and S33. When the fitness function of the optimal solution has the same result four times in a row, the loop ends.

[0101] Example

[0102] To validate the model and solution, this study applied it to a specific example. A real-world Arctic shipping case from 2020 to 2021 was analyzed using AIS data from DYNAGASLTD (hereinafter referred to as D). D is a maritime shipping company specializing in liquefied natural gas (LNG) and has extensive experience in Arctic shipping. The differential evolution algorithm proposed in this example was implemented using Visual Basic and an Oracle database, combined with Python for visualization.

[0103] 1. Data Preparation

[0104] The following data summarizes the details of operating and variable costs. Operating costs consist of vessel depreciation and maintenance costs, which are converted to USD per day for this article. Icebreaker costs are optional and can be partially avoided through accurate navigation and route planning.

[0105] Operating cost (USD / day): 27617.

[0106] Average fuel consumption (tons / day): 59.7.

[0107] Fuel oil price (IFO380, USD / ton): 267.5.

[0108] Icebreaker fee (USD 1,000 / voyage): 870.

[0109] Port fee ( / entry): 14560.

[0110] Differential cost (boxes / day): 100.

[0111] 2. Model Solution

[0112] Python is used to solve the example. Due to the large fleet scheduling plan, only one of the ships involved in the transshipment, Ship No. 10, is listed for demonstration. The focus of this example is to solve the scenario of using the BeiDou Information Service Framework to obtain real-time information to adjust the distribution plan when forecast errors occur. Therefore, this paper intends to use the actual ice grid data in 2020 and the prediction results of the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the two greenhouse gas emission scenarios RCP4.5 and RCP8.5 for comparison. The simulated sea state changes based on the differences between the two are shown in Table 2.

[0113] Table 2. Changes in ice conditions

[0114] Task Port of destination Change time Planned ice thickness (m) Actual ice thickness (m) <![CDATA[task 21 ]]> Sabetta 4 / 11 / 2020 4.1 6.3 <![CDATA[task 36 ]]> Bergen 4 / 17 / 2020 4.6 6.8

[0115] To address changing ice conditions, the unexecuted portion of the freight plan is optimized. The differential cost introduced in this example stipulates that both late and early arrivals incur penalties, so the cumulative differential time is the most important factor in measuring economic efficiency in this example. The decision to proceed with delivery is based on the cumulative time difference between the adjusted freight plan and the original plan. The resulting delivery route is shown in Table 3 below:

[0116] Table 3. Final dispatch plan for vessel No. 10

[0117]

[0118] Table 3 above details the detailed schedule of cargo ship No. 10, which involves two transshipments. When the ship enters the eastern split port, its own risk factor is lower than the minimum requirement, so it must rent an icebreaker to complete the subsequent cargo plan. In order to effectively reduce the cumulative difference time, the ship also needs to consider transshipment. When No. 10 is executing the task 21 and task 36 When encountering ice conditions that were inconsistent with the plan during navigation, the best transfer point was found with the assistance of the Beidou information service framework. The remaining ships followed the new route according to the updated scheduling plan. The cumulative difference in time between the overall fleet plan and the initial plan was only 2.16 days.

[0119] 3. Comparative Analysis

[0120] The numerical values ​​in this example are analyzed based on four different scenarios, differentiated by whether the Beidou information service framework is used and whether differential costs are included. Because the entire process involves a certain degree of randomness, to accurately evaluate the performance of the proposed solution, all performance evaluations are based on 10 experiments. The differential costs represent whether fines are incurred when goods are delivered early or late. The results are shown in Table 4.

[0121] Table 4. Numerical results for various scenarios

[0122] Scenario Scheduling strategy using the BeiDou-3 information service framework fine Average cumulative time error (days) Optimal Gap 1 × × 7.44 3.2％ 2 × √ 2.29 2.3％ 3 √ × 5.96 2.8％ 4 √ √ 2.16 4.5％

[0123] In Scenario 1 and Scenario 3, since no differential penalties were imposed, the fleet gave priority to sailing at economic speeds. At the same time, in order to maximize profits, when faced with changes in the route environment that disrupted the sailing plan, the carrier almost always adopted a strategy of docking at the nearest port or even waiting at sea. Although the fleet in Scenario 3 was covered by the Beidou information service framework and could receive the optimal sailing plan after the disturbance, due to the lack of differential penalties, the captain tended to adopt a strategy that saved more fuel costs. Therefore, the differential costs of the two scenarios were relatively large.

[0124] Both Scenario 2 and Scenario 4 consider differential costs. However, the freight route in Scenario 2 is somewhat unpredictable. Because the BeiDou information service framework is not used, the only strategy for handling changes in the navigation environment during navigation is for the captain to independently select the subsequent destination based on the current channel conditions and experience (in this example, the captain's decision was simulated using the actual destination port of an actual ship). Results show that this scenario still results in relatively high differential costs.

[0125] In scenario 4, the fleet uses the BeiDou information service framework to transmit information and maintain communications, so the fleet can temporarily change subsequent scheduling plans during the voyage. It can be found that with the assistance of the BeiDou satellite navigation system, the cumulative difference time of the fleet's cargo delivery will be shortened.

[0126] In summary, the present invention takes into account the dynamic scheduling problem of the Arctic route fleet, and designs a multi-base station fleet information service framework serving the Arctic route fleet scheduling based on the Beidou-3 satellite navigation system, which greatly improves the efficiency and accuracy of environmental and scheduling information transmission. On this basis, the space-time network model is used to effectively represent this complex problem, and a differential evolution algorithm is proposed to solve it. The preset scheme is applied to the real case of Company D in 2020-2021 to analyze the feasibility of the business. The results show that the trend of carriers choosing the Arctic route to replace the traditional Asia-Europe route is increasing, but the unpredictable navigation environment will bring the risk of high economic losses, which requires high-frequency modification of existing scheduling plans. Therefore, the scheme proposed in the present invention can serve as an important reference for carriers when considering Arctic route fleet freight scheduling plans.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing fleet scheduling in Arctic waterways based on BeiDou III, characterized in that: include: S1. Design a multi-base station fleet information service framework for Arctic routes based on BeiDou-3, and reshape the information exchange process between ships and between ships and shores under BeiDou services; The BeiDou short message is used to integrate fleet, hydrological, and discrete information within the designed multi-base station fleet information service framework for the Arctic route. The short message design includes coding design and data packet structure design, specifically including: The coding design adopts BCD coding, and the interactive data includes ship information and environmental information; The data packet structure design includes: Divide long packets into sub-packets according to the Beidou standard. That is, the encoded monitoring data is "unpacked" at the sending end, and corresponding "unpacking control" is added to identify the uniqueness of each sub-packet. At the receiving end, the "unpacking control" is removed from each sub-packet, and the data is merged according to the information of the "unpacking control" to restore the original message correctly and orderly; 3 bytes of "depacket control" information are added to the header of each sub-packet, and the remaining 74 bytes of each sub-packet are used for monitoring data; Redundancy calculation is performed when the monitoring data length of the last sub-packet is less than 74 bytes; Perform an XOR operation on each 74-byte sub-packet to obtain a redundant check packet, which carries "depacketization control" information and is sent together with the previous three sub-packets; As long as the receiver successfully receives any three data packets in the forward error correction block, it can successfully recover the complete monitoring information; S2. Based on the designed Arctic waterway multi-base station fleet information service framework, a BeiDou-3-based Arctic waterway fleet scheduling optimization model is constructed. S21. Design a generalized spatiotemporal network for coordinated scheduling between ships in the NSR fleet to dynamically update whether a certain ship can sail on a certain route at a certain time and space; S22. Construct the objective function of the BeiDou-3 based fleet dispatch optimization model for the Arctic route; S23. Set the constraints as follows: Constraint 1 is used to constrain flow conservation in the ship subnetwork; Constraint 2 is used to constrain flow conservation in the cargo subnetwork, that is, the arc flow pointing to a node, including the navigation arc and the transshipment arc pointing to the node, plus the supply of the node is equal to the sum of the arc flow leaving the node plus the demand of the node; Constraints 3 and 4 are used to ensure that every piece of cargo is shipped; Constraint 5 is used to constrain the capacity of the ship, limiting the amount of cargo that the ship can load; Constraint 6 is used to ensure that the cargo is carried by the ship; S3. Improve the differential evolution algorithm to solve the non-deterministic polynomial problem in the fleet scheduling process; S31. In order to reduce the search space, freight tasks with the same starting port and destination port are grouped. A group of task groups forms a task sequence. Finally, the freight tasks are converted into task sequences and the task sequences are studied. S32, introduce the elite strategy, directly copy the parent individual with the highest fitness value to the next generation, and select the remaining individuals normally; S33. Use the improved DE / rand-to-best / 1 strategy to perform mutation operations on all ships in the selected chromosome. That is, two randomly selected individuals are differentially calculated with the best individual in the current population. Since the new gene already contains the optimal information, a new variant can be directly generated using the generated differential vector. S34, looping through steps S32 and S33. When the fitness function of the optimal solution has the same result four times in a row, the loop ends.

2. The method for optimizing the dispatch of Arctic shipping routes based on BeiDou III according to claim 1 is characterized in that: Step S1 specifically includes: S11. The Arctic route ocean-going fleet will be equipped with various terminals to transmit information; S12, the BeiDou III satellite receives BeiDou short messages from the terminal and ground base station and completes the forwarding of the corresponding messages; S13. After receiving the short message from the terminal, the ground support system quickly connects to the shore-based information release and management platform through the Internet. The shore-based system immediately generates the subsequent dispatch plan for the fleet and forwards it back to the terminal, thereby ensuring the security and efficiency of information exchange between the ship and shore sides.

3. The method for optimizing the dispatch of Arctic shipping routes based on BeiDou III according to claim 1 is characterized in that: Step S2 specifically includes: S21. With the help of a multi-base station fleet information service framework for the Arctic route, the shore can quickly respond to environmental changes in the fleet's current route. Based on this, a generalized spatiotemporal network for coordinated scheduling between ships in the NSR fleet is designed to dynamically update whether a ship can sail on a certain route segment at a certain time and space. S22. Construct the objective function of the BeiDou-3-based Arctic shipping fleet scheduling optimization model as follows: in, represents the fitness function; Indicates a collection of fleet ships; C Represents a collection of goods; UA represents the set of unloading arcs in the cargo subnetwork; Indicates that the carrier is on the arc net income on Indicates a 0-1 variable, if the goods c Ship from cargo subnet s Mounting node origin i Loaded to the destination node in the cargo subnet j On, then =1, otherwise =0; Represents the arc set in the ship subnet s ; represents the cost of the navigation arc in the ship subnetwork, which includes ship capital, fuel and voyage-related costs; Indicates a 0-1 variable. If the ship s Along the arcs in the ship subnet Sailing, then =1, otherwise =0; LA represents the set of loading arcs in the cargo subnet; Indicates goods c In the transport arc Loading cost on Indicates a 0-1 variable, if the goods c From the origin of the cargo subnet i Loading into cargo subnet vessel s Mounting Node j On, then =1, otherwise =0; Represents the set of nodes in the cargo subnet; Indicates a 0-1 variable, if the goods c At the port i From the ship Transfer to ship ,but =1, otherwise =0; Indicates a 0-1 variable. If the ship s Along the arcs in the ship subnet If you rent an icebreaker, =1, otherwise =0; Indicates that the icebreaker is on an arc the fees charged on S23. Set the constraints as follows: in, Represents the node set in the ship subnet s ; Indicates a 0-1 variable. If the ship s Along the arcs in the ship subnet Sailing, then =1, otherwise =0; Indicates that the ship's destination is in the ship subnet; Indicates that the ship's origin is in the ship subnet; in, Indicates a 0-1 variable, if the goods c Ship from cargo subnet Mounting node origin i Loaded to the destination node in the cargo subnet j On, then =1, otherwise =0; Indicates a 0-1 variable, if the goods c At the port j From the ship Transfer to ship ,but =1, otherwise =0; Indicates a 0-1 variable, if the goods c On the ship Up along the arc Sailing, then =1, otherwise =0; Indicates a 0-1 variable, if the goods c Ship from cargo subnet Mounting node origin j Loaded to the destination node in the cargo subnet r On, then =1, otherwise =0; Indicates goods c The place of receipt is at the O / D In the subnet; Indicates goods c The place of shipment is O / D In the subnet; represents the unloading subnode in the cargo subnetwork; Represents the loading subnode in the cargo subnetwork; Represents the set of nodes in the cargo subnet; in, T Represents a collection of ship types; Represents the navigation arc set in the ship subnet s ; in, M Represents a large positive number.

4. The method for optimizing the dispatch of Arctic shipping routes based on BeiDou III according to claim 3 is characterized in that: In step S21, the designed generalized spatiotemporal network for coordinated scheduling between ships in the NSR fleet includes a ship subnet and a cargo subnet, where: The ship subnet represents the current available ports for all ships. In the construction of the ship subnet, it is assumed that the fleet has k Ships, established k Each ship has two types of arcs: waiting arc and sailing arc. The waiting arc allows the ship to stay in the port and wait for the next mission; the sailing arc allows the ship to travel from one port to another within a certain period of time. The cargo subnet consists of a cargo flow subnet and a cargo O / D subnet. The cargo flow subnet displays the fleet's resource allocation plan, and the cargo O / D subnet represents the actual route of the corresponding ship. The cargo subnet can simulate the cargo transportation process. Assuming that each cargo task corresponds to a cargo subnet, the cargo subnet can truly reflect the fleet's NSR dynamic planning and the collaborative operation scenarios between ships in the fleet.

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