Multi-ship route dynamic optimization method and device

Abstract

The invention relates to a multi-ship route dynamic optimization method and device. The method comprises the following steps: firstly, constructing a water transportation network based on original ship automatic identification system data; then, on the basis of mapping nodes, arcs, passing points and route weights of the water transportation network, a mixed integer programming model is constructed, and an objective function and constraint conditions associated with dynamic optimization of routes of multiple ships are defined; and finally, solving based on the objective function and the constraint condition by adopting an improved adaptive large-field search algorithm to obtain a fleet path plan. And proposing a fleet route optimization mathematical model and a heuristic algorithm capable of processing a large-scale fleet. According to the model, various factors such as navigation characteristics, transportation requirements and time windows of ships are fully considered, and it is ensured that route planning not only meets economic benefits, but also can meet the requirements of safety, high efficiency and the like. Interference information in AIS data is deeply mined, the influence degree of interference on route planning is analyzed, and effective prevention and response measures are provided.

Description

Technical Field

[0001] The present application relates to the field of computer and logistics technology, and in particular to a method and device for dynamically optimizing routes of multiple ships. Background Art

[0002] With the continuous growth of global trade and the increasing complexity and dynamism of the water transport industry, and the fact that water transport is greatly affected by environmental conditions, seasonal changes and economic activities, the traditional ship route planning method based on experience and general knowledge can no longer meet the current safety and efficiency requirements. In recent years, the water transport industry has been affected by many uncertain factors. Especially in the face of emergencies, the ship route will change accordingly according to the actual situation, which makes the original route information often incomplete or inaccurate, and the actual route taken by the ship may be different from the official route. In these scenarios, a method is needed to optimize the route intelligently, dynamically and reliably based on actual data. However, there is little research on this issue in the existing literature, and the lack of relevant methods makes it difficult for ships to adapt to the dynamic changes of the environment, which in turn affects navigation efficiency and even navigation safety.

[0003] In the prior art, according to the data source and the use of data mining technology, it is generally divided into the following methods: (a) methods that do not rely on ship automatic identification system (Automatic Identification System, referred to as AIS) data and data mining technology; (b) methods that only use AIS data without using data mining technology; and (c) methods that combine the use of AIS data and use data mining technology. However, existing route optimization methods usually rely on preset or existing water transportation networks. In practice, 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 technology, there is an urgent need for a method that can improve the accuracy of water route planning. Summary of the invention

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

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

[0007] Building a water transport network based on raw Automatic Vessel Identification System data;

[0008] Based on the water transport network mapping nodes, arcs, waypoints and route weights, a mixed integer programming model is constructed to define objective functions and constraints associated with dynamic optimization of routes for multiple ships;

[0009] An improved adaptive large-domain search algorithm is used to solve the objective function and constraint conditions to obtain fleet path planning, wherein the improved adaptive large-domain search algorithm refers to determining the corresponding operator according to the fleet path planning requirements.

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

[0011]

[0012] in, 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 for order r, is whether order r is transported by ship k via 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 ship k at port i, is the delivery delay time of order r at the destination port.

[0013] Optionally, in one embodiment of the present application, the constraints include cargo delivery guarantee constraints, ship transport route constraints, loading and unloading time window constraints, ship departure condition constraints, and sailing time and emergency constraints.

[0014] Optionally, in one embodiment of the present application, the cargo delivery guarantee constraints include serviced order constraints, departure, arrival and unloading port constraints, and flow conservation constraints for ships and orders; the ship transport route constraints include driving route constraints, sub-circuit 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.

[0015] Optionally, in one embodiment of the present application, the improved adaptive large-domain search algorithm includes:

[0016] The insertion operator and removal operator are selected according to the fleet path planning requirements to improve the adaptive large-area search algorithm.

[0017] Optionally, in one embodiment of the present application, the insertion operator includes a greedy insertion operator, a capacity utilization insertion operator, a regretful insertion operator, a most constrained first insertion operator, and a random insertion operator.

[0018] Optionally, in one 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 also provides a device for dynamically optimizing routes of multiple ships. The device comprises:

[0020] A water transport network construction module, which is used to construct a water transport network based on the original automatic ship identification system data;

[0021] A mixed integer programming model building module, for building a mixed integer programming model based on the water transport network mapping nodes, arcs, waypoints and route weights, and defining objective functions and constraints associated with dynamic optimization of routes for multiple ships;

[0022] The fleet path planning module is used to adopt an improved adaptive large-field search algorithm to solve the objective function and constraint conditions to obtain the fleet path planning, wherein the improved adaptive large-field search algorithm refers to determining the corresponding operator according to the fleet path planning requirements.

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

[0024] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in each of the above embodiments are implemented.

[0025] The above-mentioned method and device for dynamic optimization of routes of multiple ships, first, construct a water transport network based on the original ship automatic identification system data; then, based on the mapping nodes, arcs, waypoints and route weights of the water transport network, a mixed integer programming model is constructed to define the objective function and constraints associated with the dynamic optimization of routes of multiple ships; finally, an improved adaptive large-field search algorithm is used to solve the objective function and constraints to obtain the fleet path planning, wherein the improved adaptive large-field search algorithm refers to determining the corresponding operator according to the fleet path planning requirements. In other words, a fleet route optimization mathematical model and a heuristic algorithm capable of handling large-scale fleets are proposed. The model will fully consider multiple factors such as the navigation characteristics, transportation needs, time windows, etc. of the ship to ensure that the route planning is both economically efficient and safe and efficient. By deeply mining the interference information in the AIS data, analyzing the degree of influence of interference on route planning, and proposing effective prevention and response measures. This will help shipping companies to make rapid adjustments in the face of complex and changing marine environments to ensure the smooth implementation of route planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 An application environment diagram of a method for dynamically optimizing routes of multiple ships in one embodiment;

[0027] Figure 2 A schematic diagram of a flow chart of a method for dynamically optimizing routes of multiple ships in one embodiment;

[0028] Figure 3 A schematic diagram of a flow chart of an improved adaptive large domain search algorithm in an embodiment;

[0029] Figure 4 A schematic diagram of a comparison of transportation plans with and without interruption interference in one embodiment;

[0030] Figure 5 It is a structural block diagram of a multi-ship route dynamic optimization device in one embodiment;

[0031] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with 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] The method for dynamically optimizing routes of multiple ships provided in the embodiment of the present application can be applied to 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 it can be placed on the cloud or other network servers. Among them, the terminal can be but not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0034] In one embodiment, Figure 2 As shown in FIG. 1 , a method for dynamic optimization of routes of multiple ships is provided. The method is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0035] S201: Construct a water transport network based on the original automatic ship identification system data.

[0036] In the embodiment of the present application, first, the original ship automatic identification system AIS data is obtained, pre-processed, noise and abnormal trajectory points are removed, grouped and segmented according to the maritime mobile service identification code (Maritime Mobile Service Identity, referred to as MMSI), and then smoothed and interpolated to generate high-quality trajectory data. Then, waypoints are extracted from the data to display the spatiotemporal characteristics of the ship. Finally, the waypoints are spatially clustered to form nodes, and the weights and directions of the edges are determined according to the navigation statistics and directional characteristics to generate a water transport network, that is, a directed weighted network of water routes.

[0037] S203: Based on the water transport network mapping nodes, arcs, waypoints and route weights, a mixed integer programming model is constructed to define objective functions and constraints associated with dynamic optimization of routes for multiple ships.

[0038] In the embodiment of the present application, after constructing the water transport network, the key data for optimizing the fleet route is provided by mapping the nodes, arcs, waypoints and route weights in detail. The nodes N represent key locations such as ports, which facilitate the planning of loading and unloading stops. The arcs A connecting these nodes provide specific characteristics (including distance d ij and typical sailing time ) various route options. These routes are further refined by waypoints, indicating navigation points that the ship must pass through to ensure safe and efficient navigation. Route weight a ijRepresents the number of voyages, enabling the evaluation and comparison of different routes. By leveraging this comprehensive network information, the fleet can optimize routes to improve safety, improve overall management and operational efficiency, and reduce costs. At the same time, a mixed integer programming model is constructed based on this to define the objective function and constraints associated with the dynamic optimization of multi-ship routes.

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

[0040]

[0041] in, 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 for order r, is whether order r is transported by ship k via 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 ship k at port i, is the delivery delay time of order r at the destination port.

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

[0043] In one 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, and sailing time and emergency constraints, wherein 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 transportation at the designated arrival port, the loading and unloading time window constraint means that the pickup 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 position only after all service tasks are completed, the sailing time and emergency constraints mean that the sailing time is consistent with the driving distance and speed under normal circumstances, and the sailing time should be consistent with the actual situation when congestion or collision accidents occur.

[0044] Specifically, in one embodiment of the present application, the cargo delivery guarantee constraints include serviced order constraints, departure, arrival and unloading port constraints, and flow conservation constraints for ships and orders; the ship transport route constraints include driving route constraints, sub-circuit 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 the last service constraints; the sailing time and emergency constraints include driving time and distance speed constraints, waiting time, and delay time constraints.

[0045] In one embodiment of the present application, the cargo delivery guarantee constraints include serviced order constraints, departure and arrival and unloading port constraints, and ship and order flow conservation constraints, wherein the serviced order constraint means ensuring that the number of serviced requests reaches a predefined threshold, which can be adjusted by parameter ε. When order r is departed from the starting point p(r) by ship k, that is, The order is deemed to have been served.

[0046]

[0047] Among them, R refers to the order set, which includes the pick-up port, delivery port, pick-up and delivery time window, and container quantity; N refers to the port set, K refers to the ship set, and p(r) refers to the pick-up port of order r.

[0048] The departure, arrival and unloading port constraints ensure that each received order must be delivered to its designated unloading port, and that the ship starts and ends transportation at the designated departure and arrival ports, as shown in the following formula.

[0049]

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

[0051] The flow conservation constraints for ships and orders are shown below.

[0052]

[0053] in, Refers to whether ship k traverses route (i, j), Indicates whether order r is transported by ship k via route (i,j).

[0054] Ship transport route constraints include route constraints, sub-circuit route constraints, and capacity constraints. The route constraints ensure that orders can only be transported when the ship is traveling 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] Sub-tour constraints are shown below and are used to eliminate sub-tours.

[0058]

[0059] in, Refers to whether port i is located before port j in the route of ship k.

[0060] The capacity constraints are as follows:

[0061]

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

[0063] The loading and unloading time window constraints include order pick-up time constraints, service start time constraints, and service end time constraints. The order pick-up time constraint means ensuring that the order pick-up time 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. The specific formula is as follows.

[0064]

[0065] in, It refers to the time when the service of order r by vessel k at port p(r) starts. It refers to the time when order r is completed 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, is the arrival time of order r by ship k at port i, The time when the service of order r by vessel k at port i begins, The service time of order r by vessel k at port i, It refers to the time when order r is completed by vessel k at port i.

[0066] The ship departure condition constraints include service completion constraint, order arrival time constraint, and last service constraint. The service completion constraint means ensuring that the ship can depart only after all services are completed, the order arrival time constraint means ensuring that the order arrival time cannot be earlier than the ship arrival time, and the last service constraint means setting the start time of the ship's last service. The specific details are shown in the following formula.

[0067]

[0068] in, is the departure time of ship k at port i, is the arrival time of ship k at port i, It refers to the last service start time of vessel k at port i.

[0069] The navigation time and emergency constraints include travel time and distance speed constraints, waiting time and delay time constraints, among which the travel time and distance speed constraints ensure that the travel time matches the distance and speed.

[0070]

[0071] in, 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, It refers to the time when order r is completed by vessel k at port d(r).

[0072] S205: Using an improved adaptive large-area search algorithm to solve the objective function and the constraint conditions to obtain a fleet path planning, wherein the improved adaptive large-area search algorithm refers to determining a corresponding operator according to the fleet path planning requirements.

[0073] In the embodiment of the present application, a meta-heuristic optimization algorithm suitable for solving complex path planning problems with multiple constraints, namely the adaptive large-area search algorithm ALNS, is used to iteratively explore the solution space and adjust the search strategy according to the quality of the found solution. Figure 3 As shown in Figure 1, by improving the adaptive large-area search algorithm ALNS, the constructed water transport 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 node N, arc A and distance d ij It reflects the actual path points, nodes and routes of the water transport network, considers specific water constraints such as ship capacity, port handling time and sailing time, and obtains the fleet path planning based on the objective function and constraint conditions.

[0074] Specifically, in one embodiment of the present application, the improved adaptive large-domain search algorithm includes:

[0075] The insertion operator and removal operator are selected according to the fleet path planning requirements to improve the adaptive large-area search algorithm.

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

[0077] Specifically, in one embodiment of the present application, the insertion operator includes a greedy insertion operator, a capacity utilization insertion operator, a regretful insertion operator, a most restricted first insertion operator, and a random insertion operator.

[0078] In one embodiment of the present application, Figure 3 As 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 orders into ships with higher load factors, aiming to improve ship capacity utilization. The regretful insertion operator is used to place orders by evaluating different regret values. It first evaluates all possible positions for placing the order into each route, then calculates the regret value of each option, and finally selects the insertion with the highest regret value. The most constrained 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 ships and locations and insert the order as soon as a feasible solution is found.

[0079] Specifically, in one 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 one embodiment of the present application, Figure 3As shown, the Worst Removal operator is used to eliminate the orders with the highest cost in each route. The Related 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 ships that can serve this order and similar orders at the same time. The Historical Removal operator is used to rely on historical cost data to identify and remove suboptimal orders that may be worse than historical records (i.e., more expensive at the moment). 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 with the least removal, which involves clearing the entire route and returning all its orders to the order pool, with the goal of reducing the number of ships used and optimizing capacity utilization. The Random Removal operator is used to randomly select ships and remove one order from each ship.

[0081] In the above-mentioned method for dynamic optimization of routes of multiple ships, first, a water transport network is constructed based on the original ship automatic identification system data; then, based on the mapping nodes, arcs, waypoints and route weights of the water transport network, a mixed integer programming model is constructed to define the objective function and constraints associated with the dynamic optimization of routes of multiple ships; finally, an improved adaptive large-field search algorithm is used to solve the objective function and constraints to obtain the fleet path planning, wherein the improved adaptive large-field search algorithm refers to determining the corresponding operator according to the fleet path planning requirements. In other words, a fleet route optimization mathematical model and a heuristic algorithm capable of handling large-scale fleets are proposed. The model will fully consider multiple factors such as the navigation characteristics, transportation needs, and time windows of ships to ensure that route planning is both economically efficient and safe and efficient. By deeply mining the interference information in AIS data, analyzing the degree of influence of interference on route planning, and proposing effective prevention and response measures. This will help shipping companies to make rapid adjustments in the face of complex and changing marine environments to ensure the smooth implementation of route planning.

[0082] In one embodiment of the present application, Figure 4 As shown in Figure 1, under normal cargo transportation conditions, the proposed optimization model generates the following transportation plans for the fleet. (1) Transportation plan for ship No. 1: The ship departs from Port 0, goes to Port 1 to load Order No. 9, and then sails to Port 2 to unload. Then, it goes to Port 6 to load Order No. 10, and continues to Port 3 to unload. On the way to Port 3, the ship passes a network node close to Port 4, but does not actually stop at Port 4. Figure 4As shown. Finally, the ship returns to Port 1 and completes the entire transportation task. (2) Transportation plan of Ship No. 2: The ship departs from Port 0 and loads Order No. 1 and No. 5. Then, it sails to Port 2 to load Order No. 3 and No. 8, and unloads Order No. 1 at the same time. After that, the ship goes to Port 5 to unload Order No. 3, continues to Port 8 to unload Order No. 5 and No. 8, and finally returns to Port 1 to complete the transportation task. (3) Transportation plan of Ship No. 3: The ship departs from Port 3 and loads Order No. 4. Then, it goes to Port 1 to load Order No. 2, and continues to Port 5 to load Order No. 7. After completing the loading, the ship goes to Port 0 to unload Order No. 2, and returns to Port 6 to unload Order No. 7 and No. 4, and then anchors.

[0083] The weight in the transportation network (a ij ) shows that the route between Port 0 and Port 1 is heavily trafficked. Therefore, disruptions could significantly reduce the efficiency of these critical routes. To quantitatively assess the impact of these uncertainties on transportation efficiency, the route weights a ij The transport distances on these busy routes have been adjusted. Figure 4 As shown in the bottom sub-figure in Figure , the model effectively mitigates the negative impact of disruptions on fleet efficiency by optimizing fleet routes by prioritizing efficient routes rather than shortest sailing distances. The largest adjustment in route optimization is for the busy route between Port 0 and Port 1 for vessels 1 and 2. However, due to increased disruptions, the adjusted route plan omits orders 1 and 2. This highlights the significant impact of disruptions on shipping efficiency, which may result in ineffective routes or failure to deliver orders.

[0084] In one embodiment of the present application, by increasing the service time of the ship in the port To simulate port congestion, 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 2.5 hours. The impact of these service time changes on transportation efficiency and economic costs are detailed in Table 1. In terms of transportation cost, the total cost F increases with the increase in port service time. The overall cost also shows an upward trend with the increase in the number of freight orders. 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, despite the increase in the number of freight orders and service time, the order completion rate (i.e., the completion rate of freight orders) remains stable, indicating that the proposed optimization model is able to handle large-scale instances and changes in service time.

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

[0086]

[0087]

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

[0089] Under severe congestion conditions (Level 2), the simulation is performed by extending the service time, which starts from 2 hours and gradually increases by 1 hour. Four different levels are defined to evaluate the impact of severe congestion conditions, and the comprehensive results are detailed in Table 2. It is worth noting that, unlike Level 1, extending the service time will lead to significant fluctuations and increases in total costs. As the number of freight orders increases, the service rate (i.e., the percentage of delivered orders that have been completed) decreases significantly, and this decrease is particularly evident when the number of orders exceeds 90. This result highlights the negative impact of severe port congestion and emphasizes the importance of effectively planning fleet routes.

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

[0091]

[0092]

[0093] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0094] Based on the same inventive concept, the embodiment of the present application also provides a multi-ship route dynamic optimization device for implementing the multi-ship route dynamic optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more multi-ship route dynamic optimization device embodiments provided below can refer to the limitations of the multi-ship route dynamic optimization method above, and will not be repeated here.

[0095] In one embodiment, Figure 5 As shown, a multi-ship route dynamic optimization device 500 is provided, comprising: a water transport network construction module 501, a mixed integer programming model construction module 503 and a fleet path planning module 505, wherein:

[0096] The water transport network construction module 501 is used to construct a water transport network based on the original automatic ship identification system data.

[0097] The mixed integer programming model building module 503 is used to build a mixed integer programming model based on the water transport network mapping nodes, arcs, waypoints and route weights, and define the objective function and constraints associated with the dynamic optimization of multiple ship routes.

[0098] The fleet path planning module 505 is used to adopt an improved adaptive large-field search algorithm to solve the objective function and constraint conditions to obtain the fleet path planning, wherein the improved adaptive large-field search algorithm refers to determining the corresponding operator according to the fleet path planning requirements.

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

[0100]

[0101] in, 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 for order r, is whether order r is transported by ship k via 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 ship 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 constraints include cargo delivery guarantee constraints, ship transport route constraints, loading and unloading time window constraints, ship departure condition constraints, and sailing time and emergency constraints.

[0103] In one embodiment of the present application, the cargo delivery guarantee constraints include serviced order constraints, departure, arrival and unloading port constraints, and flow conservation constraints for ships and orders; the ship transport route constraints include driving route constraints, sub-circuit 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.

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

[0105] The insertion operator and removal operator are selected according to the fleet path planning requirements to improve the adaptive large-area search algorithm.

[0106] In one embodiment of the present application, the insertion operator includes a greedy insertion operator, a capacity utilization insertion operator, a regretful insertion operator, a most constrained first insertion operator, and a random insertion operator.

[0107] In one 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.

[0108] Each module in the above-mentioned multi-ship route dynamic optimization device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0109] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The 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, a method for dynamic optimization of routes of multiple ships is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

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

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

[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 a 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, which implements the steps in the above method embodiments when executed by a processor.

[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 used for analysis, stored data, displayed data, etc.) involved in this application are all information and data 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 above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. 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), magnetic 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0116] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for dynamic optimization of routes of multiple ships, characterized in that: The method comprises: Building a water transport network based on raw Automatic Vessel Identification System data; Based on the water transport network mapping nodes, arcs, waypoints and route weights, a mixed integer programming model is constructed to define objective functions and constraints associated with dynamic optimization of routes for multiple ships; An improved adaptive large-domain search algorithm is used to solve the objective function and constraint conditions to obtain fleet path planning, wherein the improved adaptive large-domain search algorithm refers to determining the corresponding operator according to the fleet path planning requirements.

2. A method for dynamic optimization of routes of multiple ships according to claim 1, characterized in that: The objective function is: in, 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 for order r, is whether order r is transported by ship k via 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 ship k at port i, is the delivery delay time of order r at the destination port.

3. A method for dynamic optimization of routes of multiple ships according to claim 1, characterized in that: The constraints include cargo delivery guarantee constraints, ship transport route constraints, loading and unloading time window constraints, ship departure condition constraints, and sailing time and emergency constraints.

4. A method for dynamic optimization of routes of multiple ships according to claim 3, characterized in that: The cargo delivery guarantee constraints include serviced order constraints, departure, arrival and unloading port constraints, and flow conservation constraints of ships and orders. The ship transport route constraints include driving route constraints, sub-circuit 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.

5. The method for dynamic optimization of routes of multiple ships according to claim 1, characterized in that: The improved adaptive large domain search algorithm includes: The insertion operator and removal operator are selected according to the fleet path planning requirements to improve the adaptive large-area search algorithm.

6. A method for dynamic optimization of routes of multiple ships according to claim 5, characterized in that: The insertion operators include a greedy insertion operator, a capacity utilization insertion operator, a regretful insertion operator, a most restricted first insertion operator, and a random insertion operator.

7. A method for dynamic optimization of routes of multiple ships according to claim 5, characterized in that: The removal operators include the worst removal operator, the related removal operator, the history removal operator, the port removal operator, the route removal operator, and the random removal operator.

8. A multi-ship route dynamic optimization device, characterized in that: The device comprises: A water transport network construction module, which is used to construct a water transport network based on the original automatic ship identification system data; A mixed integer programming model building module, for building a mixed integer programming model based on the water transport network mapping nodes, arcs, waypoints and route weights, and defining objective functions and constraints associated with dynamic optimization of routes for multiple ships; The fleet path planning module is used to adopt an improved adaptive large-field search algorithm to solve the objective function and constraint conditions to obtain the fleet path planning, wherein the improved adaptive large-field search algorithm refers to determining the corresponding operator according to the fleet path planning requirements.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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