Arctic navigation channel fleet scheduling optimization method based on Beidou third generation

Abstract

The invention provides a Beidou third-generation-based arctic navigation channel fleet scheduling optimization method, which comprises the steps of designing a Beidou third-generation-based arctic navigation channel multi-base station type fleet information service framework, and remodeling an information interaction process between ships and two ends of a ship shore under Beidou service; based on the designed multi-base station type fleet information service framework of the arctic navigation channel, constructing an arctic navigation channel fleet scheduling optimization model based on the Beidou third generation; and improving a differential evolution algorithm, and solving a nondeterministic polynomial problem in a fleet scheduling process. According to the invention, a Beidou-based multi-base station fleet information service framework is designed to provide support for dynamic scheduling of the arctic navigation channel fleet, and the problem that instant information interaction on the arctic navigation channel is difficult is solved. And under the support of a Beidou multi-base station fleet information service framework, the information obtained by the framework is used for solving the dynamic scheduling optimization model of the arctic channel fleet. The model enables a fleet to quickly modify a freight scheduling scheme after navigation environment information is distorted, and economic loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of communications industry and maritime transport industry, and in particular to an Arctic waterway fleet scheduling optimization method based on BeiDou III. Background Art

[0002] In recent years, the global shipping crisis has directly caused a trade loss of 10 billion US dollars per day. Using the Arctic route can effectively alleviate the above difficulties. The number of transit voyages on the Arctic route has increased by an average of 17.2% in the past five years. Under this trend, mainstream shipping companies have begun to use fleets instead of single ships to implement freight plans.

[0003] Compared with traditional routes, the harsh ocean, climate and communication conditions of the Arctic route will lead to high uncertainty in fleet scheduling plans. Fleets often face problems such as distortion of existing communication means such as weather forecasts during navigation. In the past five years, the direct economic losses caused by accidents in the Arctic route have increased by 25.8% month-on-month. With the successful launch of the Beidou-3 satellite in 2022, the Beidou satellite navigation system has achieved full coverage of the Arctic route area. However, the application of the Beidou-3 satellite navigation system to the Arctic route is still in its infancy, and there is a lack of an information service framework adapted to the dispatch of Arctic route fleets. Summary of the invention

[0004] According to the technical problems raised above, a method for optimizing fleet scheduling in the Arctic route based on BeiDou III is provided. The present invention designs a multi-base station fleet information service framework based on the BeiDou III satellite navigation system, provides a top-level design of 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, through which the current spatiotemporal status of the ship and the future ports that can be docked can be updated in real time, and based on the designed BeiDou III information service framework, an optimization model for fleet scheduling in the Arctic route is established, and the differential evolution algorithm is improved to solve the non-deterministic polynomial (NP-hard) problem in the fleet scheduling process.

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

[0006] A method for optimizing fleet scheduling in the Arctic route based on BeiDou III, including:

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

[0008] S2. Based on the designed multi-base station fleet information service framework for the Arctic route, a BeiDou-3-based fleet scheduling optimization model for the Arctic route 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 ocean-going fleet on the Arctic route will carry various types of terminals to send information;

[0012] S12, the BeiDou III satellite receives BeiDou short messages from the terminal and the 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.

[0014] Furthermore, in step S1, the fleet, hydrological and discrete information are integrated 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] According to the Beidou standard, the long packet is divided into sub-packets, that is, the encoded monitoring data is "unpacked" at the sending end, and the 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] 3 bytes of "unpacking 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;

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

[0021] Perform an XOR operation on each 74-byte sub-packet to obtain a redundant check packet, which carries "unpacking 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] Further, step S2 specifically includes:

[0024] S21. With the help of the multi-base station fleet information service framework on the Arctic route, the shore can quickly respond to environmental changes on the fleet's current route. Based on this, a generalized spatiotemporal network for coordinated scheduling between ships in the fleet on the NSR is designed to dynamically update whether a certain ship can sail on a certain route 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] Among them, 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 net profit of the carrier on the arc (i, j); represents a 0-1 variable. If cargo c is loaded from the origin i of the loading node of ship s in the cargo subnet to the destination node j in the cargo subnet, then otherwise S 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 4:

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

[0036] The ship subnet represents the current berthing ports of all ships. In the construction of the ship subnet, it is assumed that the fleet has k ships, and k ship subnets 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 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 period of time.

[0037] 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. Assuming that each cargo task corresponds to a cargo subnet, the cargo subnet can truly reflect the dynamic planning of the fleet's NSR 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, the freight tasks with the same starting port and destination port are grouped, and a group of task groups form 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, using the improved DE / rand-to-best / 1 strategy, perform mutation operations on all ships in the selected chromosome; that is, perform differential operations on two randomly selected individuals and the best individual in the current population. Since the new gene already contains the optimal information, a new mutant can be directly generated through the generated differential vector;

[0042] S34, looping through step S32 and step S33, and when the fitness function of the optimal solution has the same result for four consecutive times, 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 interaction on the Arctic route.

[0045] 2. The present invention provides an Arctic route fleet scheduling optimization method based on Beidou III. With the support of 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 route fleet. The model enables the fleet to quickly modify the freight scheduling plan after the navigation environment information is distorted to reduce economic losses.

[0046] Based on the above reasons, the present invention can be widely promoted in the fields of communication 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 The figure is a flow chart of the method of the present invention.

[0049] Figure 2 This invention provides 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 waterway fleet of the present invention.

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

[0053] Figure 6 The present invention improves the differential evolution algorithm flow chart.

[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 scheme of the present invention, the technical scheme 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 described embodiments 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 creative work should fall within the scope of protection of the present invention.

[0056] It should be noted that the terms "first", "second", etc. in the specification 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 data 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 that are 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 route fleet scheduling optimization method based on BeiDou III, comprising:

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

[0059] S2. Based on the designed multi-base station fleet information service framework for the Arctic route, a BeiDou-3-based fleet scheduling optimization model for the Arctic route 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 carry various types of terminals to complete the transmission of information. Figure 2 ① in the;

[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 route fleet based on BeiDou III, the ship and the 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 route 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 rules and relevant Chinese standards, the shore-based information management platform, ground support system, terminal system, and BeiDou-3 satellite constellation constitute a BeiDou-based multi-base station fleet information service framework for the Arctic route.

[0066] In the 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 message. 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, in the coding design, in order to reduce the probability of interference in information transmission, the size of the message data should be reduced as much as possible. Considering that BCD coding has better channel utilization than ASCII coding and can reduce the overall size of data to a certain extent, this study intends to use BCD coding. The interactive data includes ship information and environmental information. The data coding is shown in Table 1. The short message transmission and reception on the Arctic route is low-frequency, so the requirements for data integrity and security are higher, which requires a separate design of a data packet structure to improve bandwidth utilization and communication reliability.

[0069] Table 1. Information coding design table

[0070]

[0071] The data packet structure design designs a data packet structure (such as 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 data packet of a Beidou ID card terminal does not exceed 77 bytes, it is obviously impossible to transmit all the information at one time. Therefore, according to the Beidou standard, the long packet is divided into sub-packets, that is, the encoded monitoring data is "unpacked" at the sending end, and the 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 "unpacking control" information to correctly and orderly restore the original message; 3 bytes of "unpacking 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; each 74-byte sub-packet is subjected to an exclusive OR (XOR) operation to obtain a redundant check packet with "unpacking control" information, which 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 (FEC) block, the complete monitoring information can be successfully restored. Compared with the existing solutions, it can better deal with the problem of high message distortion rate on the Arctic route.

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

[0073] S21. With the help of the multi-base station fleet information service framework on the Arctic route, the shore can quickly respond to environmental changes on the fleet's current route. Based on this, a generalized spatiotemporal network for coordinated scheduling between ships in the fleet on the NSR is designed to dynamically update whether a certain ship can sail on a certain route 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] Among them, 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 net profit of the carrier on the arc (i, j); represents a 0-1 variable. If cargo c is loaded from the origin i of the loading node of ship s in the cargo subnet to the destination node j in the cargo subnet, then otherwise S 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 4:

[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 the flow conservation in the ship subnet; constraint two is used to constrain the flow conservation in the cargo subnet, that is, the arc flow pointing to a node (including the navigation arc and the transfer 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 each 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 to dock. 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, and 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. It is assumed 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 is heading to port 2 as planned, it cannot navigate due to sudden changes in the route environment, so it sends a Beidou short message to the shore. The shore updates the ship subnet in real time based on the information, deletes the navigable arc from port 1->port 2 in the subnet corresponding to ship 1, and sends subsequent instructions to ship 1. Ship 1 docks at the nearest port 3 and waits according to the information sent back by the shore.

[0094] Ship k is a high ice-class ship that meets the transshipment conditions and is anchored at Port 3. Therefore, the shore sends a short message to Ship k to assist Ship 1 in transshipment. After receiving the transshipment request, Ship k loads the contract cargo that was originally planned to be transported by Ship 1 to Port 2 through Transshipment Arc 2.

[0095] Vessel k continues to perform subsequent tasks and successfully arrives at port 2, completing the transportation of the goods through unloading arc 3. If it does not arrive on the fifth day as stipulated in the contract, it will complete the transportation of the goods through 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 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, from the preliminary experiments, the boundary obtained by using this method is relatively loose and the convergence speed is very slow, so it is considered to use a heuristic algorithm to solve it. 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 the chromosomes of all affected ships can be mutated 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 route fleet. Figure 6 A detailed diagram of the DE algorithm process is provided, showing each key step from initialization to iteration, namely step S3, which includes:

[0097] S31. In order to reduce the search space, the freight tasks with the same starting port and destination port are grouped, and a group of task groups form 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 route environment, the navigation plan of the entire fleet may change. Therefore, all ships in each solution sequence should participate in mutation at the same time to increase the optimization efficiency. Using the improved DE / rand-to-best / 1 strategy, mutation operations are performed 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 through the generated differential vector; the specific operation is as follows Figure 7 As shown, according to the two selected individuals, the common tasks of all enabled ships of the two are traversed to determine the order difference of the tasks, and then the cargo task difference in the task sequence of the two is found and the information is stored. Then, an operation instruction is defined according to the above two differences to form a differential vector. For example, all the same cargo tasks in the No. 1 ship of the two initial solutions have no order difference, and the initial solution 1 needs to insert the corresponding task 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 step S32 and step S33, and when the fitness function of the optimal solution has the same result for four consecutive times, the loop ends.

[0101] Example

[0102] In order to verify the model and solution, this study applied it to a specific example and analyzed the real Arctic shipping case from 2020 to 2021 using the AIS data of DYNAGASLTD (hereinafter referred to as D). D is a maritime transportation company mainly engaged in liquefied natural gas (LNG) and has rich experience in Arctic shipping. The differential evolution algorithm proposed in this embodiment is implemented using VB language and Oracle database, and combined with Python for visualization.

[0103] 1. Data Preparation

[0104] The following data summarizes the details of operating costs and variable costs. Operating costs consist of depreciation and maintenance costs of the ship, which are converted into US dollars per day in 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 price (IFO380, USD / ton): 267.5.

[0108] Icebreaker fee (thousand US dollars / voyage): 870.

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

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

[0111] 2. Model Solution

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

[0113] Table 2. Changes in ice conditions

[0114] Task Destination Port 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] In response to changes in ice conditions, the unexecuted parts of the freight plan are optimized. The differential cost introduced in this embodiment stipulates that both delayed arrival and early arrival will be fined, so the accumulated differential time is the most important factor in measuring the economic benefits in this example. The decision on whether to deliver is made based on the accumulated differential time between the adjusted freight plan and the original freight plan. The final delivery route is shown in Table 3 below:

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

[0117]

[0118] Table 3 above describes 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 the ice conditions during navigation were inconsistent with the plan, 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 ​​of this embodiment are analyzed based on four different scenarios. These scenarios are differentiated according to whether the Beidou information service framework is used and whether differential costs are included. Since the whole process involves a certain degree of randomness, in order to accurately evaluate the performance of the proposed solution, all performance evaluations are based on 10 experiments. The differential cost indicates whether a fine is incurred when the 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 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 penalty was imposed, the fleet gave priority to sailing at economic speed. At the same time, in order to maximize profits, when the carrier faced changes in the route environment that led to disruptions in the sailing plan, the ships almost always adopted the 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 penalty constraints, 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 take differential costs into consideration. However, the freight route in Scenario 2 is somewhat unpredictable. Since the Beidou information service framework is not used, the only way for ships to deal with changes in the navigation environment during navigation is for the captain to choose the subsequent destination based on experience according to the current waterway conditions (in this embodiment, the actual destination port of the actual ship is used to simulate the captain's decision). The results show that this scenario still generates 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, and on this basis, uses the space-time network model to effectively represent this complex problem, and proposes a differential evolution algorithm to solve it. The preset scheme is applied to the real case of Company D from 2020 to 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 freight scheduling plans for Arctic route fleets.

[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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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 the Arctic route based on BeiDou III, characterized in that: include: S1. Design a multi-base station fleet information service framework for the Arctic route based on BeiDou III, and reshape the information interaction process between ships and between ships and shores under BeiDou services; S2. Based on the designed multi-base station fleet information service framework for the Arctic route, a BeiDou-3-based fleet scheduling optimization model for the Arctic route is constructed; S3. Improve the differential evolution algorithm to solve the non-deterministic polynomial problem in the fleet scheduling process.

2. The method for optimizing the dispatching of the Arctic waterway fleet based on BeiDou III according to claim 1 is characterized in that: Step S1 specifically includes: S11. The ocean-going fleet on the Arctic route will carry various types of terminals to send information; S12, the BeiDou III satellite receives BeiDou short messages from the terminal and the 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.

3. The method for optimizing the dispatching of the Arctic waterway fleet based on BeiDou III according to claim 1 is characterized in that: In step S1, the fleet, hydrological and discrete information are integrated 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: The coding design adopts BCD coding, and the interactive data includes ship information and environmental information; The data packet structure design includes: According to the Beidou standard, the long packet is divided into sub-packets, that is, the encoded monitoring data is "unpacked" at the sending end, and the corresponding "unpacking control" is added to identify the uniqueness of each sub-packet; At the receiving end, remove the "unpacking control" from each sub-packet and merge the data according to the information of the "unpacking control" to restore the original message correctly and orderly; 3 bytes of "unpacking 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 "unpacking 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, the complete monitoring information can be successfully restored.

4. The method for optimizing the dispatching of the Arctic waterway fleet based on BeiDou III according to claim 1 is characterized in that: Step S2 specifically includes: S21. With the help of the multi-base station fleet information service framework on the Arctic route, the shore can quickly respond to environmental changes on the fleet's current route. Based on this, a generalized spatiotemporal network for coordinated scheduling between ships in the fleet on the NSR is designed 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 Arctic shipping fleet scheduling optimization model as follows: Among them, 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 net profit of the carrier on the arc (i, j); represents a 0-1 variable. If cargo c is loaded from the origin i of the loading node of ship s in the cargo subnet to the destination node j in the cargo subnet, then otherwise S 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); S23. Set the constraints as follows: constraint- 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; 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 Dc means the receiving place of goods c is in the goods O / D subnet; Oc means the shipping place of goods c is in the goods O / D subnet; represents the unloading subnode in the cargo subnetwork; represents the loading subnode in the cargo subnet; Nc represents the node set in the cargo subnet; constraint Constraint 4: Constraint Five: Where T represents the set of ship types; VAs represents the navigation arc set s in the ship subnet; Constraint Six: Wherein, M represents a large positive number.

5. The method for optimizing the dispatching of the Arctic waterway fleet based on BeiDou III according to claim 4 is characterized in that: In step S21, the designed generalized spatiotemporal network for coordinated scheduling of ships in the NSR fleet includes a ship subnet and a cargo subnet, where: The ship subnet represents the current berthing ports of all ships. In the construction of the ship subnet, it is assumed that the fleet has k ships, and k ship subnets 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 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 period of time. 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. Assuming that each cargo task corresponds to a cargo subnet, the cargo subnet can truly reflect the dynamic planning of the fleet's NSR and the collaborative operation scenarios between ships in the fleet.

6. The method for optimizing the dispatching of the Arctic waterway fleet based on BeiDou III according to claim 1 is characterized in that: Step S3 specifically includes: S31. In order to reduce the search space, the freight tasks with the same starting port and destination port are grouped, and a group of task groups form 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, using the improved DE / rand-to-best / 1 strategy, perform mutation operations on all ships in the selected chromosome; that is, perform differential operations on two randomly selected individuals and the best individual in the current population. Since the new gene already contains the optimal information, a new mutant can be directly generated through the generated differential vector; S34, looping through step S32 and step S33, and when the fitness function of the optimal solution has the same result for four consecutive times, the loop ends.

Images