A Route Dynamic Optimization Method and Device for Multiple Ships

Abstract

The present application relates to a method and device for dynamically optimizing the routes of multiple ships. The method includes: First, constructing a water transportation network based on the original Automatic Identification System (AIS) data of ships; Then, based on the water transportation network, mapping nodes, arcs, waypoints, and route weights, constructing a mixed integer programming model, and defining an objective function and constraint conditions associated with the dynamic optimization of the routes of multiple ships; Finally, using an improved adaptive large neighborhood search algorithm to solve based on the objective function and constraint conditions to obtain the fleet path planning. A mathematical model for optimizing the fleet routes and a heuristic algorithm capable of handling large-scale fleets are proposed. The model will fully consider various factors such as the navigation characteristics of ships, transportation demands, time windows, etc., to ensure that the route planning not only meets economic benefits but also can meet requirements such as safety and efficiency. By deeply mining the interference information in AIS data, analyzing the degree of influence of interference on route planning, and proposing effective preventive and countermeasures.

Description

Technical Field

[0001] The present application relates to the fields of computer and logistics technologies, and particularly to a method and device for dynamically optimizing the routes of multiple ships. Background Art

[0002] With the continuous growth of global trade and the increasing complexity and dynamics of the water transportation industry, and since water transportation is greatly affected by environmental conditions, seasonal changes, and economic activities, traditional ship route planning methods based on experience and general knowledge have become difficult to meet the current safety and efficiency requirements. In recent years, the water transportation industry has encountered many uncertain factors. Especially in the face of emergencies, ship routes will change accordingly according to the actual situation, which makes the original route information often incomplete or inaccurate, and the actual routes taken by ships may be different from the officially released routes. In these scenarios, a method that can intelligently, dynamically, and reliably optimize routes based on actual data is needed. However, existing literature has studied this problem less, and the lack of relevant methods makes it difficult for ships to adapt to the dynamic changes of the environment, thereby affecting navigation efficiency and even navigation safety.

[0003] In the prior art, according to the data sources and the use of data mining technologies, it is generally divided into the following methods: (a) methods that do not rely on Automatic Identification System (AIS) data and data mining technologies; (b) methods that only use AIS data without using data mining technologies; and (c) methods that combine the use of AIS data and use data mining technologies. However, existing route optimization methods usually rely on preset or existing water transportation networks. In reality, water routes are dynamically changing, and a data-driven method that can accurately derive the current water routes from real data is needed. In addition, a dynamic optimization method that can perform large-scale optimization and adapt to environmental changes caused by emergencies is also needed.

[0004] Therefore, in the related technologies, there is an urgent need for a way to improve the accuracy of water route planning. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a method and device for dynamically optimizing the routes of multiple ships that can improve the accuracy of water route planning.

[0006] In a first aspect, the present application provides a method for dynamically optimizing the routes of multiple ships. The method includes:

[0007] Constructing a water transportation network based on the original Automatic Identification System data of ships;

[0008] Based on the above-mentioned water transportation network mapping nodes, arcs, waypoints, and route weights, a mixed-integer programming model is constructed to define the objective function and constraint conditions associated with the dynamic optimization of the routes of multiple vessels.

[0009] The improved adaptive large neighborhood search algorithm is used to solve based on the above-mentioned objective function and constraint conditions to obtain the fleet path planning. Among them, the improved adaptive large neighborhood search algorithm refers to determining the corresponding operator according to the fleet path planning requirements.

[0010] Optionally, in an embodiment of the present application, the objective function is:

[0011]

[0012] Wherein, is the transportation cost parameter, is the transportation time of vessel k on route (i, j), d ij is the distance between ports i and j, q r is the number of containers of order r, indicates whether order r is transported by vessel k through route (i, j), is the service start time of order r by vessel k at port i, a p(r) is the pick-up time of order r, is the waiting time of vessel k at port i, is the delivery delay time of order r at the destination port.

[0013] Optionally, in an embodiment of the present application, the constraint conditions include cargo delivery guarantee constraints, vessel transportation route constraints, loading and unloading time window constraints, vessel departure condition constraints, and sailing time and emergency constraints.

[0014] Optionally, in an embodiment of the present application, the cargo delivery guarantee constraints include served order constraints, departure-arrival and unloading port constraints, and flow conservation constraints of vessels and orders. The vessel transportation route constraints include driving route constraints, sub-tour route constraints, and capacity constraints. The loading and unloading time window constraints include order pick-up time constraints, service start time constraints, and service end time constraints. The vessel departure condition constraints include service completion constraints, order arrival time constraints, and last service constraints. The sailing time and emergency constraints include driving time and distance-speed constraints, waiting time and delay time constraints.

[0015] Optionally, in an embodiment of the present application, the improved adaptive large neighborhood search algorithm includes:

[0016] Select the insertion operator and removal operator according to the fleet path planning requirements to improve the adaptive large neighborhood search algorithm.

[0017] Optionally, in an embodiment of the present application, the insertion operator includes a greedy insertion operator, a capacity utilization insertion operator, a regret insertion operator, a most restricted first insertion operator, and a random insertion operator.

[0018] Optionally, in an embodiment of the present application, the removal operator includes a worst removal operator, a related removal operator, a historical removal operator, a port removal operator, a route removal operator, and a random removal operator.

[0019] In a second aspect, the present application further provides a route dynamic optimization device for multiple ships. The device includes:

[0020] A water transportation network construction module, configured to construct a water transportation network based on original Automatic Identification System (AIS) data of ships;

[0021] A mixed integer programming model construction module, configured to construct a mixed integer programming model based on the mapped nodes, arcs, waypoints, and route weights of the water transportation network, and define an objective function and constraint conditions associated with the route dynamic optimization of multiple ships;

[0022] A fleet path planning module, configured to solve based on the objective function and constraint conditions by using an improved adaptive large neighborhood search algorithm to obtain a fleet path plan, where the improved adaptive large neighborhood search algorithm refers to determining corresponding operators according to the requirements of the fleet path plan.

[0023] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the steps of the methods in the above respective embodiments.

[0024] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the methods in the above respective embodiments are implemented.

[0025] The above multi-vessel route dynamic optimization method and device, first, construct a water transportation network based on the original Automatic Identification System (AIS) data; then, based on the nodes, arcs, waypoints, and route weights of the water transportation network, construct a mixed-integer programming model, and define the objective function and constraints associated with the multi-vessel route dynamic optimization; finally, use an improved adaptive large neighborhood search algorithm to solve based on the objective function and constraints to obtain the fleet path planning, where the improved adaptive large neighborhood search algorithm refers to determining the corresponding operator according to the fleet path planning requirements. That is to say, a mathematical model for fleet route optimization and a heuristic algorithm capable of handling large-scale fleets are proposed. This model will fully consider various factors such as the navigation characteristics of ships, transportation demands, and time windows to ensure that the route planning not only meets economic benefits but also satisfies requirements such as safety and efficiency. By deeply mining the interference information in AIS data, analyzing the impact degree of interference on route planning, and proposing effective prevention and countermeasures. This will help shipping enterprises make rapid adjustments when facing complex and changeable marine environments to ensure the smooth implementation of route planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 FIG. is an application environment diagram of a multi-vessel route dynamic optimization method in an embodiment;

[0027] Figure 2 FIG. is a flowchart of a multi-vessel route dynamic optimization method in an embodiment;

[0028] Figure 3 FIG. is a flowchart of an improved adaptive large neighborhood search algorithm in an embodiment;

[0029] Figure 4 FIG. is a schematic diagram comparing transportation plans with and without interruption interference in an embodiment;

[0030] Figure 5 FIG. is a structural block diagram of a multi-vessel route dynamic optimization device in an embodiment;

[0031] Figure 6 FIG. is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] A multi-vessel route dynamic optimization method provided by an embodiment of the present application can be applied to, for example, Figure 1In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or placed on the cloud or other network servers. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0034] In one embodiment, as Figure 2 shown, a method for dynamically optimizing the routes of multiple ships is provided. Taking the server in Figure 1 as an example, the method includes the following steps:

[0035] S201: Construct a water transportation network based on the original Automatic Identification System (AIS) data of ships.

[0036] In the embodiment of the present application, first, obtain the original AIS data of ships, perform preprocessing, remove noise and abnormal trajectory points, group and segment the trajectories according to the Maritime Mobile Service Identity (MMSI), and then smooth and interpolate to generate high-quality trajectory data. Then, extract waypoints from the data to display the spatio-temporal characteristics of the ships. Finally, perform spatial clustering on the waypoints to form nodes, and determine the weights and directions of the edges according to the navigation statistics and direction characteristics to generate a water transportation network, that is, a directed weighted network of water routes.

[0037] S203: Based on the water transportation network, map nodes, arcs, passing points, and route weights, construct a mixed integer programming model, and define the objective function and constraint conditions associated with the dynamic optimization of the routes of multiple ships.

[0038] In the embodiment of the present application, after constructing the water transportation network, by detailed mapping of nodes, arcs, passing points, and route weights, key data is provided for optimizing the fleet routes. Node N represents key locations such as ports, which is convenient for planning loading and unloading stops. The arcs A connecting these nodes provide various route options with specific characteristics (including distance d ij and typical sailing time ). The passing points further refine these routes, indicating the navigation points that the ships must pass through to ensure safe and efficient navigation. The route weight a ijIt represents the number of voyages, enabling the evaluation and comparison of different shipping routes. By leveraging this comprehensive network information, the fleet can optimize the routes to enhance safety, improve overall control and operational efficiency, and reduce costs. Meanwhile, a mixed-integer programming model is constructed based on this, defining the objective function and constraints associated with the dynamic optimization of the routes of multiple ships.

[0039] Specifically, in an embodiment of the present application, the objective function is:

[0040]

[0041] Where, is the transportation cost parameter, is the transportation time of ship k on route (i, j), d ij is the distance between ports i and j, q r is the number of containers of order r, indicates whether order r is transported by ship k via route (i, j), is the service start time of order r by ship k at port i, a p(r) is the pick-up time of order r, is the waiting time of ship k at port i, is the delivery delay time of order r at the destination port.

[0042] In an embodiment of the present application, the constraints include cargo delivery guarantee constraints, ship transportation route constraints, loading and unloading time window constraints, ship departure condition constraints, navigation time and emergency constraints.

[0043] In an embodiment of the present application, the defined constraints mainly include cargo delivery guarantee constraints, ship transportation route constraints, loading and unloading time window constraints, ship departure condition constraints, navigation time and emergency constraints. Among them, the cargo delivery guarantee constraint means that each received cargo transportation order must be delivered at its designated unloading port. The ship transportation route constraint means ensuring that the ship starts transportation at the designated departure port and ends at the designated arrival port. The loading and unloading time window constraint means that the pick-up time of each cargo transportation order must be within the designated time window. The ship departure condition constraint means ensuring that the ship departs from the current location only after all service tasks are completed. The navigation time and emergency constraint means that under normal circumstances, the navigation time is consistent with the distance and speed traveled, and in case of congestion or collision accidents, the navigation time should conform to the actual situation.

[0044] Specifically, in an embodiment of the present application, the goods delivery guarantee constraints include served order constraints, departure-arrival and discharge port constraints, and flow conservation constraints of ships and orders. The ship transportation route constraints include driving route constraints, sub-tour route constraints, and capacity constraints. The loading and unloading time window constraints include order pick-up time constraints, service start time constraints, and service end time constraints. The ship departure condition constraints include service completion constraints, order arrival time constraints, and last service constraints. The sailing time and emergency constraints include driving time and distance-speed constraints, waiting time, and delay time constraints.

[0045] In an embodiment of the present application, the goods delivery guarantee constraints include served order constraints, departure-arrival and discharge port constraints, and flow conservation constraints of ships and orders. Among them, the served order constraint means ensuring that the number of served requests reaches a predefined threshold, and this threshold can be adjusted through the parameter ε. When order r departs from the origin p(r) by ship k, that is this order is considered served. Specifically, it is shown as the following formula.

[0046]

[0047] where R refers to the set of orders, and an order includes a pick-up port, a delivery port, pick-up and delivery time windows, and the number of containers; N refers to the set of ports, K refers to the set of ships, and p(r) refers to the pick-up port of order r.

[0048] The departure-arrival and discharge port constraints mean ensuring that each received order must be sent to its designated discharge port, and ensuring that the ship starts and ends transportation at the designated departure port and arrival port. It is shown as the following formula.

[0049]

[0050] where d(r) refers to the delivery port of order r, and o(k), o′(k) refer to the set of the starting point and the ending point of ship k.

[0051] The flow conservation constraints of ships and orders are shown as the following formula.

[0052]

[0053] where indicates whether ship k traverses route (i, j), indicates whether order r is transported by ship k through route (i, j).

[0054] The ship transportation route constraints include driving route constraints, sub-tour route constraints, and capacity constraints. Among them, the driving route constraint means ensuring that an order can only be transported when the ship is driving on the relevant route, that is

[0055]

[0056] Among them, A refers to the arc set, and (i, j) ∈ A represents the route from i to j.

[0057] The sub-tour route constraint is as follows, which is used to eliminate the sub-tour route.

[0058]

[0059] Among them, indicates whether port i is before port j in the route of ship k.

[0060] The capacity constraint is as follows:

[0061]

[0062] Among them, q r refers to the number of containers of order r, and u k refers to the capacity of ship k.

[0063] The loading and unloading time window constraints include the order pick-up time constraint, the service start time constraint, and the service end time constraint. Among them, the order pick-up time constraint means ensuring that the pick-up time of the order is within the specified time window, the service start time constraint means ensuring that the service start time is after the container arrival time, and the service end time constraint means setting the service end time equal to the sum of the service start time and the service duration. Specifically, it is as shown in the following formula.

[0064]

[0065] Among them, refers to the service start time of order r by ship k at port p(r), refers to the service completion time of order r by ship k at port p(r), [a p(r) , b p(r) refers to the pick-up time of order r, M is a large positive number, refers to the arrival time of order r by ship k at port i, refers to the service start time of order r by ship k at port i, refers to the service time of order r by ship k at port i, refers to the service completion time of order r by ship k at port i.

[0066] The constraints on the departure conditions of the ship include service completion constraints, order arrival time constraints, and the last service constraint. Among them, the service completion constraint means ensuring that the ship can only depart after all services are completed. The order arrival time constraint means ensuring that the arrival time of the order cannot be earlier than the arrival time of the ship. The last service constraint means setting the start time of the last service of the ship. Specifically, it is shown as follows.

[0067]

[0068] Among them, refers to the departure time of ship k at port i, refers to the arrival time of ship k at port i, refers to the start time of the last service of ship k at port i.

[0069] The constraints on sailing time and emergencies include the constraints on travel time, distance, and speed, and the constraints on waiting time and delay time. Among them, the constraint on travel time, distance, and speed means ensuring that the travel time matches the distance and speed.

[0070]

[0071] Among them, refers to the transportation time of ship k on route (i, j), [[a d(r) , b d(r) refers to the delivery time window of order r, refers to the waiting time of ship k at port i, refers to the delivery delay time of order r at the destination port, refers to the service completion time of order r by ship k at port d(r).

[0072] S205: Solve based on the objective function and constraint conditions by using an improved adaptive large neighborhood search algorithm to obtain the fleet path planning. Among them, the improved adaptive large neighborhood search algorithm refers to determining the corresponding operator according to the fleet path planning requirements.

[0073] In the embodiments of the present application, a meta-heuristic optimization algorithm suitable for solving complex path planning problems with multiple constraints, that is, the adaptive large neighborhood search algorithm ALNS, is used to iteratively explore the solution space and adjust the search strategy according to the quality of the found solutions. As Figure 3 shown, by improving the adaptive large neighborhood search algorithm ALNS, the constructed water transportation network is incorporated into the algorithm, and the corresponding operator is determined according to the fleet path planning requirements. This integration can ensure that the input nodes N, arcs A, and distances d ij reflect the actual path points, nodes, and routes of the water transportation network, consider specific water constraints, such as ship capacity, port handling time, and sailing time, and solve based on the objective function and constraint conditions to obtain the fleet path planning.

[0074] Specifically, in an embodiment of the present application, the improved adaptive large neighborhood search algorithm includes:

[0075] Improve the adaptive large neighborhood search algorithm by selecting insertion operators and removal operators according to the fleet path planning requirements.

[0076] In an embodiment of the present application, as Figure 3 shown, a customized operator is used to optimize the capacity utilization rate of the ship, and at the same time, a port removal operator is used to remove ports that may have an adverse impact on the overall efficiency of the entire transportation plan. By iteratively applying the insertion and removal operators until a predetermined number of iterations is reached, then the optimal solution is selected from all the generated solutions and used as the final fleet transportation plan.

[0077] Specifically, in an embodiment of the present application, the insertion operators include a greedy insertion operator, a capacity utilization insertion operator, a regret insertion operator, a most restricted first insertion operator, and a random insertion operator.

[0078] In an embodiment of the present application, as Figure 3 shown, the greedy insertion operator is used to evaluate all possible solutions and insert the order into the optimal route. The capacity utilization insertion operator is used to preferentially insert the order onto a ship with a higher load rate, aiming to improve the ship capacity utilization rate. The regret insertion operator is used to place the order by evaluating different regret values. It first evaluates all possible positions for placing the order in each route, then calculates the regret value for each option, and finally selects the insertion with the highest regret value. The most restricted first insertion operator is used to sort the orders using a weighted function that considers the distance, load, and time window between the pickup and delivery terminals. The random insertion operator is used to randomly select a ship and a position and insert the order immediately once a feasible solution is found.

[0079] Specifically, in an embodiment of the present application, the removal operators include a worst removal operator, a related removal operator, a historical removal operator, a port removal operator, a route removal operator, and a random removal operator.

[0080] In an embodiment of the present application, as Figure 3As shown, the worst removal operator is used to eliminate the order with the highest cost in each shipping route. The relevant removal operator is used to first randomly remove an order, and then continue to remove similar orders based on factors such as distance, time, load, and the vessels that can serve this order and similar orders simultaneously. The historical removal operator is used to rely on historical cost data to identify and remove sub-optimal orders that may be worse than the historical records (i.e., the current cost is higher). The port removal operator is used to remove all orders related to a specific port to effectively manage port-specific constraints or bottlenecks. The route removal operator is used when the insertion operator cannot find a feasible solution in the case of the least removal, which involves clearing the entire route and returning all its orders to the order pool, aiming to reduce the number of vessels used and optimize capacity utilization. The random removal operator is used to randomly select vessels and remove one order from each vessel.

[0081] In the above method for dynamic optimization of shipping routes of multiple vessels, first, a water transportation network is constructed based on the original Automatic Identification System (AIS) data of vessels; then, based on the nodes, arcs, waypoints, and route weights mapped by the water transportation network, a mixed-integer programming model is constructed, defining the objective function and constraint conditions associated with the dynamic optimization of shipping routes of multiple vessels; finally, an improved adaptive large neighborhood search algorithm is used to solve based on the objective function and constraint conditions to obtain the fleet path planning, where the improved adaptive large neighborhood search algorithm refers to determining the corresponding operator according to the requirements of the fleet path planning. That is to say, a mathematical model for optimizing the shipping routes of the fleet and a heuristic algorithm capable of handling large-scale fleets are proposed. This model will fully consider various factors such as the navigation characteristics of vessels, transportation demands, time windows, etc., to ensure that the route planning not only meets economic benefits but also can meet requirements such as safety and efficiency. By deeply mining the interference information in AIS data, analyzing the impact degree of interference on route planning, and proposing effective preventive and countermeasures. This will help shipping enterprises to make rapid adjustments when facing complex and changeable marine environments and ensure the smooth implementation of route planning.

[0082] In an embodiment of the present application, as Figure 4 shown, under the normal transportation of goods, the proposed optimization model generates the following transportation plan for the fleet. (1) Transportation plan for vessel No. 1: This vessel departs from Port No. 0, goes to Port No. 1 to load Order No. 9, and then sails to Port No. 2 to unload. Then, it goes to Port No. 6 to load Order No. 10 and continues to sail to Port No. 3 to unload. On the way to Port No. 3, this vessel passes through a network node near Port No. 4 without actually docking at Port No. 4. Specifically, as Figure 4As shown. Finally, the ship returns to Port 1, completing the entire transportation task. (2) Transportation plan for Ship 2: The ship departs from Port 0 and loads Orders 1 and 5. Then, it sails to Port 2 to load Orders 3 and 8 while unloading Order 1. After that, the ship heads to Port 5 to unload Order 3, continues to Port 8 to unload Orders 5 and 8, and finally returns to Port 1, completing the transportation task. (3) Transportation plan for Ship 3: The ship departs from Port 3 and loads Order 4. Then, it goes to Port 1 to load Order 2 and continues to Port 5 to load Order 7. After loading is completed, the ship goes to Port 0 to unload Order 2 and returns to Port 6 to unload Orders 7 and 4, and then berths.

[0083] Weights in the transportation network (a ij ) indicate that the route between Port 0 and Port 1 has heavy traffic. Therefore, disruptions may significantly reduce the efficiency of these critical routes. To quantitatively evaluate the impact of these uncertainties on transportation efficiency, the route weight a ij is used to adjust the transportation distances of these busy routes. As shown in the bottom subfigure of Figure 4 , the model optimizes the fleet routes by prioritizing efficient routes rather than the shortest sailing distances, effectively mitigating the negative impact of disruptions on fleet efficiency. The largest adjustments to route optimization are for the busy routes between Port 0 and Port 1 for Ship 1 and Ship 2. However, due to increased disruptions, the adjusted route plan omits Orders 1 and 2. This highlights the significant impact of disruptions on transportation efficiency, which may lead to ineffective routes or undeliverable orders.

[0084] In one embodiment of the present application, port congestion is simulated by increasing the service time of the ship at the port . The service time level is in hours and is denoted as C. Under normal operating conditions (Level 1), the initial service time is set to 1 hour. To explore the impact of mild port congestion, the service time is gradually increased by 0.5 hours, up to a maximum of 2.5 hours. The impact of these service time changes on transportation efficiency and economic costs is shown in Table 1 in detail. In terms of transportation costs, the total cost F increases as the port service time increases. As the number of freight orders increases, the overall cost also shows an upward trend. However, in some scenarios (e.g., when R increases from 60 to 70 and from 80 to 90), a cost reduction is observed, indicating that the operating cost is reduced due to economies of scale. In terms of service efficiency, although the number of freight orders and service time increase, the order completion rate (i.e., the completion rate of freight orders) remains stable, indicating that the proposed optimization model can handle large-scale instances and changes in service time.

[0085] Table 1 Experimental results table under normal operating conditions (Level 1)

[0086]

[0087]

[0088] The best / total time ratio (i.e., the proportion of the time taken to determine the optimal solution to the total computing time) measures the algorithmic efficiency of the proposed optimization model in determining the best route, and the freight order size significantly affects this efficiency. Additionally, as the service time increases, the best / total time ratio for large orders increases less than that for small orders. This indicates that the optimization algorithm can maintain stable performance even in complex transportation planning scenarios.

[0089] Under severe congestion conditions (Level 2), simulated by extending the service time, the service time starts from 2 hours and increases by 1 hour step by step. Four different levels are defined to evaluate the impact of severe congestion conditions, and the comprehensive results are shown in Table 2. It is worth noting that, different from Level 1, extending the service time leads to significant fluctuations and increases in the total cost. As the number of freight orders increases, the service rate (i.e., the percentage of completed delivery orders) decreases significantly, and this decrease is particularly obvious when the number of orders exceeds 90. This result highlights the negative impact of severe port congestion and emphasizes the importance of effective fleet route planning.

[0090] Table 2 Experimental results table under severe congestion conditions (Level 2)

[0091]

[0092]

[0093] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the indications of the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless clearly stated herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily need to be executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential either, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0094] Based on the same inventive concept, an embodiment of the present application further provides a multi-vessel route dynamic optimization device for implementing a multi-vessel route dynamic optimization method involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the multi-vessel route dynamic optimization device provided below can refer to the limitations on the multi-vessel route dynamic optimization method in the above text, and will not be repeated here.

[0095] In one embodiment, as Figure 5 shown, a multi-vessel route dynamic optimization device 500 is provided, including: a water transportation network construction module 501, a mixed integer programming model construction module 503, and a fleet route planning module 505, where:

[0096] The water transportation network construction module 501 is configured to construct a water transportation network based on the original Automatic Identification System (AIS) data of vessels.

[0097] The mixed integer programming model construction module 503 is configured to construct a mixed integer programming model based on the nodes, arcs, waypoints, and route weights of the water transportation network, and define an objective function and constraint conditions associated with the multi-vessel route dynamic optimization.

[0098] The fleet route planning module 505 is configured to solve using an improved adaptive large neighborhood search algorithm based on the objective function and constraint conditions to obtain a fleet route plan, where the improved adaptive large neighborhood search algorithm refers to determining corresponding operators according to the fleet route planning requirements.

[0099] In an embodiment of the present application, the objective function is:

[0100]

[0101] Where is the transportation cost parameter, is the transportation time of vessel k on route (i, j), d ij is the distance between ports i and j, q r is the number of containers of order r, is whether order r is transported by vessel k through route (i, j), is the service start time of order r by vessel k at port i, a p(r) is the pick-up time of order r, is the waiting time of vessel k at port i, is the delivery delay time of order r at the destination port.

[0102] In one embodiment of the present application, the constraint conditions include goods delivery guarantee constraints, ship transportation route constraints, loading and unloading time window constraints, ship departure condition constraints, and navigation time and emergency constraints.

[0103] In one embodiment of the present application, the goods delivery guarantee constraints include served order constraints, departure-arrival and unloading port constraints, and flow conservation constraints of ships and orders. The ship transportation route constraints include driving route constraints, sub-tour route constraints, and capacity constraints. The loading and unloading time window constraints include order pick-up time constraints, service start time constraints, and service end time constraints. The ship departure condition constraints include service completion constraints, order arrival time constraints, and last service constraints. The navigation time and emergency constraints include driving time and distance-speed constraints, waiting time and delay time constraints.

[0104] In one embodiment of the present application, the improved adaptive large neighborhood search algorithm includes:

[0105] Improve the adaptive large neighborhood search algorithm by selecting insertion operators and removal operators according to the requirements of the fleet path planning.

[0106] In one embodiment of the present application, the insertion operators include greedy insertion operators, capacity utilization insertion operators, regret insertion operators, most restricted priority insertion operators, and random insertion operators.

[0107] In one embodiment of the present application, the removal operators include worst removal operators, relevant removal operators, historical removal operators, port removal operators, route removal operators, and random removal operators.

[0108] Each module in the above-mentioned route dynamic optimization device for multiple ships can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0109] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 6As shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for dynamic optimization of the shipping routes of multiple ships. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0110] Those skilled in the art can understand that Figure 6 the structure shown in [the figure] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0111] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented.

[0112] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above-mentioned method embodiments are implemented.

[0113] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above-mentioned method embodiments are implemented.

[0114] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties.

[0115] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, Resistive Random Access Memory (ReRAM), Magnetoresistive Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, etc. Volatile memory can include Random Access Memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0116] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0117] The embodiments described above merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for dynamically optimizing the routes of multiple ships, characterized in that The method includes: Constructing a water transportation network based on the original Automatic Identification System (AIS) data of ships; Constructing a mixed-integer programming model based on the mapped nodes, arcs, waypoints, and route weights of the water transportation network, and defining the objective function and constraints associated with the dynamic optimization of the routes of multiple ships; Using an improved adaptive large neighborhood search algorithm to solve based on the objective function and constraints to obtain the fleet path planning, where the improved adaptive large neighborhood search algorithm refers to determining the corresponding operators according to the fleet path planning requirements; The objective function is: wherein, is the transportation cost parameter, is the transportation time of vessel k on route ; is the distance between port and ; is the number of containers of order ; indicates whether order is transported by vessel via route ; is the service start time of order by vessel at port ; is the pick-up time of order ; is the waiting time of vessel at port ; is the delivery delay time of order at the destination port; The constraints include cargo delivery guarantee constraints, ship transportation route constraints, loading and unloading time window constraints, ship departure condition constraints, and sailing time and emergency constraints; The cargo delivery guarantee constraints include served order constraints, departure-arrival and unloading port constraints, and flow conservation constraints of ships and orders. The ship transportation route constraints include driving route constraints, sub-tour route constraints, and capacity constraints. The loading and unloading time window constraints include order pick-up time constraints, service start time constraints, and service end time constraints. The ship departure condition constraints include service completion constraints, order arrival time constraints, and last service constraints. The sailing time and emergency constraints include driving time and distance-speed constraints, waiting time, and delay time constraints; The improved adaptive large neighborhood search algorithm includes: Selecting insertion operators and removal operators according to the fleet path planning requirements to improve the adaptive large neighborhood search algorithm.

2. The dynamic route optimization method for multiple ships according to claim 1, wherein The insertion operators include greedy insertion operator, capacity utilization insertion operator, regret insertion operator, most restricted first insertion operator, and random insertion operator.

3. A dynamic route optimization method for multiple ships according to claim 1, characterized in that The removal operators include worst removal operator, relevant removal operator, historical removal operator, port removal operator, route removal operator, and random removal operator.

4. An apparatus for implementing a method for dynamically optimizing the routes of multiple ships as described in claim 1, characterized in that, The device includes: A water transportation network construction module for constructing a water transportation network based on the original AIS data of ships; A mixed-integer programming model construction module for constructing a mixed-integer programming model based on the mapped nodes, arcs, waypoints, and route weights of the water transportation network, and defining the objective function and constraints associated with the dynamic optimization of the routes of multiple ships; A fleet path planning module for using an improved adaptive large neighborhood search algorithm to solve based on the objective function and constraints to obtain the fleet path planning, where the improved adaptive large neighborhood search algorithm refers to determining the corresponding operators according to the fleet path planning requirements.

5. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 3.

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