Ship route combination optimizing method and system based on standard route

Abstract

The invention relates to a ship route combination optimizing method and system based on a standard route, and the method comprises a data input step, a voyage number operation income calculation step, a problem abstract modeling step, a dynamic planning solving step and a data output step. The operation deadline, the route operation duration and the voyage operation income are mapped to the capacity, the weight and the value of a complete backpack problem, a dynamic planning algorithm is used for solving and filling the technical blank, the optimal route combination scheme can be obtained within the given operation deadline, the voyage operation income is maximized, and the method has the advantages of being efficient and accurate and has a wide application prospect. The transportation cost can be effectively reduced, the transportation efficiency is improved, and scientific decision support is provided for shipping enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of digital transformation and intelligent development of the shipping industry, and particularly relates to a method and system for optimizing ship route combinations based on standard routes. Background Art

[0002] In modern shipping operations, given standard routes and an operating period, the selection of routes has a direct impact on the voyage operating revenue. Generally speaking, the longer the route operation time, the more time it occupies in the operating period, and the greater the voyage operating revenue tends to be. However, how to construct combined routes to obtain the maximum voyage operating revenue within the limited period is a highly valuable and urgently needed problem to be solved in the modern shipping industry.

[0003] Currently, in route planning, the arrangement is generally carried out by manual experience. This method has obvious defects. It lacks systematicness and scientificity, and it is also impossible to reasonably explain and prove the optimality of the formulated plan.

[0004] The knapsack problem is one of the classic problems in combinatorial optimization. In recent years, with the development of computer technology and optimization algorithms, it has been widely applied in many fields such as logistics, resource allocation, and portfolio investment. Its core idea is to optimize the goal by selecting different combinations of items under the condition of given resource constraints. Although the knapsack problem is widely used in combinatorial optimization, it has not been effectively applied in the shipping field.

[0005] Based on the above background, the present invention aims to fill this technical gap, provide a more scientific and efficient route planning tool, and thus improve the overall operating efficiency and economic benefits of the shipping industry. Summary of the Invention

[0006] In order to solve the problem that the route combination optimization based on standard routes in existing shipping operations relies on manual experience and lacks scientificity and optimality guarantee, the present invention proposes a new method for optimizing ship route combinations based on standard routes. By abstracting the route combination problem into a complete knapsack problem model and using the dynamic programming algorithm to solve it, the technical gap is filled. It can obtain the optimal route combination plan within the given operating period, thereby maximizing the voyage operating revenue. It has the characteristics of high efficiency and accuracy, can effectively reduce the transportation cost, improve the transportation efficiency, and provide scientific decision-making support for shipping enterprises. The present invention also relates to a system for optimizing ship route combinations based on standard routes.

[0007] The technical solution of the present invention is as follows:

[0008] A method for optimizing ship route combinations based on standard routes, characterized by comprising the following steps:

[0009] Data input step: Call the basic data tables of multiple standard routes and the ship speed and fuel consumption data tables from the ship database, and obtain the ship's no-load and full-load speeds, oil price index, and operation period input by the user. The basic data tables include route name, standard route operation duration, no-load / full-load voyage distance, voyage income, and port charges. The ship speed and fuel consumption data tables include the fuel consumption of the ship at different speeds;

[0010] Voyage operation revenue calculation step: According to the ship's no-load speed and full-load speed input by the user, combined with the no-load / full-load voyage distance in the ship speed and fuel consumption data tables and the standard route basic data tables, calculate the fuel consumption of the ship in the no-load and full-load states; According to the calculated fuel consumption and the oil price index input by the user, calculate the fuel cost of the ship; Then, based on the voyage income in the standard route basic data tables, deduct the cost expenditures including fuel cost and port charges to calculate the voyage operation revenue of each standard route;

[0011] Problem abstraction and modeling step: Abstract the route combination problem into a complete knapsack problem model. Among them, the operation period is mapped to the knapsack capacity, the standard route operation duration is mapped to the item weight, the calculated voyage operation revenue is mapped to the item value, and the characteristic that the route supports multiple operations is mapped to the characteristic that the item supports repeated selection;

[0012] Dynamic programming solution step: Use the dynamic programming algorithm to solve the complete knapsack problem model. Decompose the original problem of the maximum voyage operation revenue of selecting standard routes within the total operation period into several sub-problems of the maximum voyage operation revenue of selecting some standard routes at each time point within the total operation period through the state transition equation, calculate the optimal solution of each sub-problem, and gradually construct the optimal solution of the original problem through the backtracking process to obtain the optimal route combination plan;

[0013] Data output step: Calculate the total voyage operation revenue of the optimal route combination plan and output the result of the total voyage operation revenue.

[0014] Preferably, in the voyage operation revenue calculation step, based on the no-load speed of the ship input by the user, the fuel consumption per unit time corresponding to the no-load speed of the ship is determined by combining with the ship speed and fuel consumption data table, and the no-load sailing time is calculated according to the ratio of the no-load and full-load distances in the standard route basic data table to the no-load speed of the ship. Then, the product of the fuel consumption per unit time corresponding to the no-load speed of the ship and the no-load sailing time is calculated to obtain the fuel consumption of the ship in the no-load state; based on the full-load speed of the ship input by the user, the fuel consumption per unit time corresponding to the full-load speed of the ship is determined by combining with the ship speed and fuel consumption data table, and the full-load sailing time is calculated according to the ratio of the full-load distance in the standard route basic data table to the full-load speed of the ship. Then, the product of the fuel consumption per unit time corresponding to the full-load speed of the ship and the full-load sailing time is calculated to obtain the fuel consumption of the ship in the full-load state.

[0015] Preferably, in the voyage operation revenue calculation step, the cost expenditures deducted also include several combinations of depreciation costs, maintenance costs, labor costs, and environmental costs from the basic data table or input by the user.

[0016] Preferably, in the problem abstraction and modeling step, on the basis of mapping the operation period to the knapsack capacity, the standard route operation duration to the item weight, and the voyage operation revenue to the item value in the complete knapsack problem model, constraints on the route combination are also incorporated. The constraints on the route combination include the maximum allowable number of standard route constraints, the priority constraints of specific standard routes, and / or the mutual dependence constraints between standard routes.

[0017] Preferably, in the dynamic programming solution step, the dynamic programming algorithm is used to solve the complete knapsack problem model. First, a two-dimensional array dp[i][j] is defined to represent the maximum voyage operation revenue under the condition that the total sailing time of the first i standard routes does not exceed j, where i is the standard route number and j is each time point within the total operation period; and the boundary conditions of the two-dimensional array dp[i][j] are dynamically planned. The boundary conditions are that when there is no standard route or the total sailing time is 0, the maximum voyage operation revenue is 0.

[0018] Preferably, in the dynamic programming solution step, the state transition equation is:

[0019] When j ≥ t i then dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - t i + c i );

[0020] When j < t i then dp[i][j] = dp[-1][j];

[0021] wherein, t i is the standard route operation duration of the i-th route, and c i is the voyage operation revenue of the i-th standard route.

[0022] Preferably, in the dynamic programming solution step, the top K optimal route combination schemes are obtained, where K is a positive integer; in the data output step, these K optimal route combination schemes are output, the total voyage operation revenue of each optimal route combination scheme is calculated and output, and at the same time, the route names, standard route operation durations, voyage revenues, and cost expenditures of each standard route included in each optimal route combination scheme are output.

[0023] A ship route combination optimization system based on standard routes, characterized in that it includes a data input module, a voyage operation revenue calculation module, a problem abstraction and modeling module, a dynamic programming solution module, and a data output module connected in sequence, wherein,

[0024] The data input module calls the basic data tables of multiple standard routes and the ship speed and fuel consumption data tables from the ship database, and obtains the ship's no-load and full-load speeds, oil price index, and operation period input by the user. The basic data tables include route names, standard route operation durations, no-load and full-load distances, voyage revenues, and port usage fees. The ship speed and fuel consumption data tables include the fuel consumption of the ship at different speeds;

[0025] The voyage operation revenue calculation module calculates the fuel consumption of the ship in no-load and full-load states according to the ship's no-load speed and full-load speed input by the user, combined with the no-load and full-load distances in the ship speed and fuel consumption data tables and the standard route basic data tables; calculates the fuel cost of the ship according to the calculated fuel consumption and the oil price index input by the user; and then calculates the voyage operation revenue of each standard route based on the voyage revenue in the standard route basic data table, after deducting the cost expenditures including fuel cost and port usage fees.

[0026] The problem abstraction and modeling module abstracts the route combination problem into a complete knapsack problem model, wherein the operation period is mapped to the knapsack capacity, the standard route operation duration is mapped to the item weight, the calculated voyage operation revenue is mapped to the item value, and the characteristic that the route supports multiple operations is mapped to the characteristic that the item supports repeated selection;

[0027] The dynamic programming solution module uses the dynamic programming algorithm to solve the complete knapsack problem model. By means of the state transition equation, the original problem of the maximum voyage operation revenue of selecting standard routes within the total operation period is decomposed into several sub-problems of the maximum voyage operation revenue of selecting partial standard routes at each time point within the total operation period, calculates the optimal solutions of each sub-problem, and gradually constructs the optimal solution of the original problem through the backtracking process, so as to obtain the optimal route combination plan;

[0028] The data output module calculates the total voyage operation revenue of the optimal route combination plan and outputs the result of the total voyage operation revenue.

[0029] Preferably, in the problem abstraction and modeling module, on the basis of mapping the operation period to the knapsack capacity, the standard route operation duration to the item weight, and the voyage operation revenue to the item value, the complete knapsack problem model also incorporates the constraint conditions for the route combination. The constraint conditions for the route combination include the maximum allowable number of standard routes constraint, the priority constraint of specific standard routes, and / or the mutual dependence constraint between standard routes.

[0030] Preferably, in the dynamic programming solution module, when using the dynamic programming algorithm to solve the complete knapsack problem model, first define a two-dimensional array dp[i][j] to represent the maximum voyage operation revenue of the first i standard routes under the condition that the total sailing time does not exceed j, where i is the standard route number and j is each time point within the total operation period; and dynamically program the boundary conditions of the two-dimensional array dp[i][j]. The boundary conditions are that when there are no standard routes or the total sailing time is 0, the maximum voyage operation revenue is 0; then use the state transition equation to decompose the problem and iteratively calculate and update the values of the two-dimensional array, and finally backtrack to obtain the optimal route combination plan. The state transition equation is:

[0031] When j≥t i , dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - t i + c i );

[0032] When j < t i , dp[i][j] = dp[-1][j];

[0033] Where t i is the standard route operation duration of the i-th route, and c i is the voyage operation revenue of the i-th standard route.

[0034] The technical effects of the present invention are as follows:

[0035] The present invention relates to a method for optimizing ship route combinations based on standard routes. In the data input step, fixed parameters of standard routes, or in other words, basic data (such as route names, standard route operation durations, empty / full load voyage distances, voyage revenues, port charges, etc.) and ship speed and fuel consumption data tables are obtained from a ship database. At the same time, real-time data input by the user, or in other words, dynamic parameters (empty / full load speeds, oil price indices, operation periods, etc.) are received to ensure the accuracy and integrity of the algorithm input data. The standard route data obtained from the ship database provides a unified benchmark for subsequent calculations, realizes standardization, reduces manual input errors, and the dynamic parameters can be adjusted in real time, enabling the solution to be adaptable, capable of adapting to market changes, and avoiding decision-making biases caused by data lag. These data provide the necessary basic data for subsequent steps, ensuring that the algorithm can be optimized based on actual operating conditions. In the voyage operation revenue calculation step, the voyage operation revenue of each standard route is accurately calculated, taking into account actual operating cost expenditures such as fuel costs and port charges, ensuring that the calculated voyage operation revenue reflects the actual economic benefits and providing accurate benefit values for the subsequent model. These values will directly affect the optimization result of the route combination and are dynamic. The calculated fuel cost changes in real time with the ship speed and oil price index, ensuring that the voyage operation revenue reflects the real operating scenario. The operation revenues of all routes are uniformly quantified into monetary indicators, also providing a fair comparison basis for subsequent optimization. In the problem abstraction and modeling step, the complex route combination problem is transformed into a knapsack problem. By mapping the operation period, route operation duration, and voyage operation revenue to the capacity, weight, and value of the knapsack problem, since routes can be operated multiple times, the corresponding items can be selected repeatedly, which constitutes a complete knapsack problem, establishing a clear mathematical model, simplifying the complex into the simple, making the solution of the problem clearer and more efficient, providing a theoretical basis for the application of subsequent dynamic programming algorithms, ensuring the scientificity and effectiveness of the optimization process. The model supports the extension of constraint conditions (such as route priorities, maximum number of routes, etc.), enhancing flexibility and being adaptable to the route combination requirements of different ship types and market conditions.Steps for solving by dynamic programming: The dynamic programming algorithm is used to efficiently solve the complete knapsack problem model. The original problem - selecting routes within the total operation period to achieve the maximum voyage operation revenue - is decomposed into several sub-problems through the state transition equation, that is, selecting some routes at each time point within the total operation period to achieve the maximum voyage operation revenue. Calculate the optimal solutions of each sub-problem, and gradually construct the optimal solution of the original problem through the backtracking process to ensure finding the global optimal solution within the operation period, thereby obtaining the optimal route combination plan. The dynamic programming algorithm sets a state array and uses the state transition equation to update the array values. By continuously iterating and comparing, it calculates the optimal solutions of each sub-problem, and can efficiently handle optimization problems with optimal substructure and overlapping sub-problems. The original problem is decomposed into multiple sub-problems to ensure the accuracy and reliability of the optimization results. Finally, the optimal solution is gradually constructed through the backtracking mechanism to find the route combination that can achieve the maximum voyage operation revenue within the given operation period. This plan not only ensures maximizing the operation revenue under the premise of meeting the time limit but also solves the problem that the optimality cannot be verified by manual experience. The data output step calculates the total voyage operation revenue of the optimal route combination plan and outputs the total voyage operation revenue result and the operation information of each standard route, providing detailed operation information for users, including route name, standard route operation duration, empty / full load voyage distance, voyage income, and cost expenditure, etc., to help users comprehensively understand the optimization results. In the form of a visual report, etc., users can intuitively understand the advantages and details of the optimization plan and have traceability, recording the operation information of each route for subsequent review and optimization of model parameters. The implementation steps of the present invention are clear. From data input, voyage operation revenue calculation, problem abstraction and modeling, dynamic programming solution to data output, each step aims to ensure the real-time and accuracy of ship route combination optimization, can effectively reduce transportation costs and improve transportation efficiency, not only solves the key technical problems in the shipping industry but also provides strong technical support for shipping enterprises, helping to promote the technological progress and efficiency improvement of the entire industry.

[0036] The ship route combination optimization method based on standard routes of the present invention optimizes the route combination to ensure maximizing the voyage operation revenue within a given operation period and improving the overall efficiency of ship operation; it takes into account actual cost expenditures such as fuel costs, port charges, depreciation costs, and maintenance costs. By optimizing the route combination, it maximizes the revenue of the ship within the operation period, reduces time and fuel waste, effectively reduces operation costs, and improves resource utilization rate; it provides an optimization solution based on a scientific model (complete knapsack model) and an algorithm (dynamic programming algorithm) to ensure the global optimality of the solution, breaks through the limitations of traditional manual experience, helps shipping enterprises make more scientific decisions, and enhances competitiveness; it quantifies the decision-making process, transforms shipping decision-making into a data-driven process, reduces subjective judgment errors, provides a reusable route optimization framework, and also promotes the transformation of the shipping industry from "experience-driven" to "data-driven"; the dynamic programming algorithm can significantly shorten the solution time, support real-time or quasi-real-time route optimization, and can output multiple alternative solutions, providing a wider selection space for users. At the same time, the model and algorithm can be extended and adjusted according to actual needs, having technical scalability. The model can integrate other constraint conditions to support future multi-objective optimization; the entire solution is based on actual operation data and scientific models, having strong practicality and operability, and can be directly applied to the daily operation of shipping enterprises.

[0037] The present invention also relates to a ship route combination optimization system based on standard routes. This system corresponds to the above-mentioned ship route combination optimization method based on standard routes and can be understood as a system for implementing the above-mentioned ship route combination optimization method based on standard routes. It includes a data input module, a voyage operation revenue calculation module, a problem abstraction and modeling module, a dynamic programming solution module, and a data output module. Each module works collaboratively. By abstracting the route combination problem into a complete knapsack problem model and calling the dynamic programming algorithm for solution, it obtains the optimal route combination plan, fills the technical gap, analogizes and maps the complex route combination problem with the classic knapsack problem, and transforms the actual shipping business scenario into a mathematical model. Such abstraction enables the originally complex route planning problem to be solved using the mature knapsack problem algorithm, providing a theoretical basis for subsequent efficient calculation. The dynamic programming algorithm updates the array values by setting a state array and using the state transition equation, and finally backtracks to obtain the optimal route combination, ensuring obtaining the optimal route combination plan under the premise of meeting the time limit, thereby maximizing the voyage operation revenue. It has the characteristics of high efficiency and accuracy, can effectively reduce transportation costs, improve transportation efficiency, and provide scientific decision-making support for shipping enterprises. Description of the Drawings

[0038] Figure 1 It is a flowchart of the ship route combination optimization method based on standard routes of the present invention.

[0039] Figure 2 This is a schematic diagram of the data output interface for the data output step / module of the present invention. Detailed implementation manners

[0040] The present invention will be described below with reference to the accompanying drawings.

[0041] The present invention relates to a method for optimizing ship route combinations based on standard routes, and the core is the abstraction and solution of the route combination problem. The present invention abstracts the route combination problem into a knapsack problem, makes an analogy and mapping between the complex route combination problem and the classical knapsack problem, transforms the actual shipping business scenario into a mathematical model - a complete knapsack problem model, and then uses the dynamic programming algorithm to solve it, search for a route combination plan, and ensure the optimality of the plan. Through this method, the route optimization combination problem can be effectively solved, and efficient and accurate decision support can be realized in a complex and changeable environment to achieve the optimization goal of maximizing operating income. Its flowchart is as Figure 1 shown, including:

[0042] I. Data input step: Call the basic data tables of multiple standard routes and the ship speed and fuel consumption data tables from the ship database, and obtain the ship's no-load and full-load speeds, oil price index, and operation period input by the user. The basic data tables include route name, standard route operation duration, no-load and full-load distances, voyage income, and port usage fees. The ship speed and fuel consumption data table includes the fuel consumption of the ship at different speeds.

[0043] The standard route has a fixed starting port, loading port, unloading port, and navigation path, and has relatively accurate expected loading and unloading times and berthing waiting times. In addition, the no-load and full-load distances, port usage fees, income, etc. of the standard route are clear. Therefore, the standard route operation duration and operation income have a small floating range and are relatively accurate, which has practical auxiliary significance for the combined optimization decision based on the standard route.

[0044] Multiple route basic data tables: The inherent attributes of the standard route, called from the ship database, as shown in Table 1, including basic information such as route name, starting port, loading port, unloading port, standard route operation duration, loading time, loading berthing waiting time, unloading time, unloading berthing waiting time, no-load distance, full-load distance, standard shipment volume, voyage income, and port usage fees;

[0045] Table 1

[0046] Starting Port Loading Port Discharging Port Standard Route Operation Duration Loading Time Loading Berthing Waiting Time Discharging Time Discharging Berthing Waiting Time Empty Load Voyage Distance Full Load Voyage Distance Standard Shipping Volume Port Dues

[0047] Ship speed and fuel consumption data table: Called from the ship database, including the fuel consumption of the ship at different speeds;

[0048] Ship no-load and full-load speeds: User input data;

[0049] Oil price index: user input data;

[0050] Operation period: user input data.

[0051] II. Steps for calculating the voyage operation revenue. According to the no-load speed and full-load speed of the ship input by the user, combined with the no-load and full-load distances in the ship speed and fuel consumption data table and the standard route basic data table, calculate the fuel consumption of the ship in no-load and full-load states; according to the calculated fuel consumption and the oil price index input by the user, calculate the fuel cost of the ship; then, based on the voyage revenue in the standard route basic data table, deduct the cost expenditures including fuel cost and port charges, and calculate the voyage operation revenue of each standard route.

[0052] Among them, the specific calculation of the fuel consumption of the ship in no-load and full-load states is as follows:

[0053] According to the no-load speed of the ship input by the user, combined with the ship speed and fuel consumption data table, determine the fuel consumption per unit time corresponding to the no-load speed of the ship, and calculate the no-load sailing time according to the ratio of the no-load and full-load distances in the standard route basic data table to the no-load speed of the ship. Then, multiply the fuel consumption per unit time corresponding to the no-load speed of the ship by the no-load sailing time to calculate the fuel consumption of the ship in no-load state;

[0054] According to the full-load speed of the ship input by the user, combined with the ship speed and fuel consumption data table, determine the fuel consumption per unit time corresponding to the full-load speed of the ship, and calculate the full-load sailing time according to the ratio of the full-load distance in the standard route basic data table to the full-load speed of the ship. Then, multiply the fuel consumption per unit time corresponding to the full-load speed of the ship by the full-load sailing time to calculate the fuel consumption of the ship in full-load state.

[0055] Furthermore, the steps for calculating the voyage operation revenue are to deduct the cost expenditures, including fuel cost and port charges, from the voyage revenue in the standard route basic data table. The cost expenditures can also include, for example, depreciation cost, maintenance cost, labor cost, environmental cost, etc. from the basic data table or input by the user, and further calculate the voyage operation revenue of each standard route.

[0056] III. Steps for abstracting and modeling the problem. Abstract the route combination problem into a complete knapsack problem model, where the operation period is mapped to the knapsack capacity, the operation duration of the standard route is mapped to the item weight, the calculated voyage operation revenue is mapped to the item value, and the characteristic that the route supports multiple operations is mapped to the characteristic that the item supports repeated selection.

[0057] This step transforms the complex route combination problem into a knapsack problem. By mapping the operation period, route operation duration, and voyage operation revenue to the capacity, weight, and value of the knapsack problem, the route combination problem is transformed into an optimal item selection problem under the limited capacity of the knapsack. Since a route can be operated multiple times, the corresponding item can be selected repeatedly, which constitutes a complete knapsack problem. A clear mathematical model - the complete knapsack problem model - is established, simplifying the complex problem. By analogy and mapping the complex route combination problem with the classic knapsack problem, the actual shipping business scenario is transformed into a mathematical model. Such abstraction enables the use of mature knapsack problem algorithms to solve the originally complex route planning problem, providing a theoretical basis for the application of subsequent dynamic programming algorithms and ensuring the scientificity and effectiveness of the optimization process.

[0058] Furthermore, the complete knapsack problem model supports the extension of constraint conditions (such as route priority, maximum number of routes, etc.). That is to say, on the basis of mapping the operation period to the knapsack capacity, the standard route operation duration to the item weight, and the voyage operation revenue to the item value, the complete knapsack problem model can also incorporate constraint conditions for route combination. The constraint conditions for route combination include the maximum allowable number of standard route constraints, the priority constraints of specific standard routes, and / or the mutual dependence constraints between standard routes, enhancing flexibility and adapting to the route combination requirements under different ship types and market conditions.

[0059] IV. Steps of solving by dynamic programming: Use the dynamic programming algorithm to solve the complete knapsack problem model. The original problem of selecting the maximum voyage operation revenue of standard routes within the total operation period is decomposed into several sub-problems of selecting the maximum voyage operation revenue of partial standard routes at each time point within the total operation period through the state transition equation. Calculate the optimal solution of each sub-problem, and gradually construct the optimal solution of the original problem through the backtracking process to obtain the optimal route combination plan.

[0060] Dynamic programming is an effective method for solving optimization problems, especially suitable for problems with overlapping sub-problems and optimal sub-structure properties. The knapsack problem is exactly such a problem. The dynamic programming algorithm decomposes the original problem - selecting the maximum voyage operation revenue of standard routes within the total operation period - into sub-problems - selecting the maximum voyage operation revenue of partial standard routes at each time point within the total operation period, and uses the optimal solutions of the sub-problems to construct the optimal solution of the original problem. Denote the knapsack capacity, i.e., the operation period, as T. Each route corresponds to an item, and each item has two attributes: the route operation duration t i and the voyage operation revenue c t .

[0061] In the dynamic programming algorithm of the present invention, the states are first defined. Let i be the first i shipping lines under consideration, that is, the standard shipping line numbers, and j be the current sailing time limit, that is, each time point within the total operation period. A two-dimensional array dp[i][j] is defined, representing the maximum operating profit of the first i shipping lines under the condition that the total sailing time does not exceed j.

[0062] At the same time, the boundary conditions of the two-dimensional array dp[i][j] are set. A two-dimensional array dp[i][j] of size (n + 1)×(T + 1) is created and initialized to zero, that is, when there is no standard shipping line or the sailing time is zero, the maximum operating profit value is zero:

[0063]

[0064] That is to say, when there are no shipping lines to choose from (i = 0), regardless of the total sailing time, the maximum operating profit is zero, that is, dp[0][j] = 0. When the total sailing time is zero (j = 0), regardless of the number of shipping lines available, the maximum operating profit is zero, that is, dp[i][0] = 0.

[0065] Each shipping line and each time limit are traversed in turn, and the two-dimensional array dp[i][j] is updated using the state transition equation. The state transition equation is used to describe how to construct the solution of the original problem from the solutions of sub-problems. Specifically, the state transition equation is:

[0066] If the current sailing time limit j ≥ t i : dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - t i + c i )

[0067] If the current sailing time limit j < t i : dp[i][j] = dp[i - 1][j]

[0068] Where t i is the standard sailing operation duration of the i-th shipping line, and c i is the voyage operation profit of the i-th standard shipping line.

[0069] If the i-th shipping line is not selected, the maximum operating profit value is dp[i - 1][j]. If the i-th shipping line is selected, the maximum operating profit value is dp[i - 1][j - t i + c i , that is, the time limit is reduced by t i , and the operating profit value is increased by c i .

[0070] Finally, for the optimal solution of the backtracking process, based on the two-dimensional array dp[i][j], backtrack to obtain the selected route combination to ensure that the operating revenue value is maximized under the premise of meeting the time limit.

[0071] The steps for solving this dynamic programming problem are as follows: After completing the problem abstraction and modeling, use the dynamic programming algorithm to solve the constructed complete knapsack problem model. The dynamic programming algorithm can efficiently handle problems with optimal substructure and overlapping subproblems. By continuously iterating and comparing, find the route combination that can achieve the maximum voyage operating revenue within the given operating period. Finally, obtain the optimal route combination plan. Further, the top K optimal route combination plans can be obtained, where K is a positive integer. For example, obtain the top two or top three optimal route combination plans.

[0072] It should be noted that when the complete knapsack problem model supports extended constraint conditions and incorporates constraint conditions for route combinations, the specific operation when using the dynamic programming algorithm to solve this complete knapsack problem model is as follows:

[0073] ① Constraint on the maximum allowable number of standard routes: Add a count of the number of selected routes in the state transition equation. When the number of selected routes reaches the maximum allowable number, restrict the selection of new routes.

[0074] ② Priority constraint for specific standard routes: During the state transition process, give priority to specific standard routes with higher priorities. For example, when calculating the maximum operating revenue, assign a higher weight to the operating revenue of high-priority routes or first determine whether they can be selected.

[0075] ③ Mutual dependence constraint between standard routes: According to the mutual dependence relationship between standard routes (such as the selection of route A depends on the selection of route B), set corresponding conditional judgments in the state transition equation, and only allow the selection of relevant routes when the dependence relationship is met.

[0076] V. Data output step: Calculate the total voyage operating revenue of the optimal route combination plan and output the result of the total voyage operating revenue.

[0077] When the dynamic programming solution step obtains K optimal route combination plans, this data output step outputs these K optimal route combination plans, calculates the total voyage operating revenue of each optimal route combination plan, and outputs the result of the total voyage operating revenue. At the same time, it can also output the route names, standard route operating durations, voyage revenues, and cost expenditures of each standard route included in each optimal route combination plan. For example, Figure 2Schematic diagram of the data output interface, which outputs the top three optimal route combination plans, namely the best route combination (TOP1 route combination), TOP2 route combination, and TOP3 route combination, and displays the total voyage operating revenue of each plan and the information of each standard route. Among them, the total voyage operating revenue of each plan is displayed by TCE. TCE is an indicator used in the shipping industry to measure the profitability of ship transportation. Here, the total voyage operating revenue is converted into the daily rental level equivalent in the time charter market, so as to provide a more intuitive revenue reference for shipowners. It also outputs and displays information such as the route name (for example, in the best route combination, the route of leg 1: Middle East - Europe / North Sea side, the route of leg 2: US Gulf / Caribbean - Far East), the operating duration of the standard route (such as 42.62 operating days), the TCE of the leg, the no-load speed, the voyage income (total income), and the cost expenditure (total cost), etc., and supports parameter adjustment. This output interface can help users comprehensively understand the optimization results. Through forms such as visual reports, users can intuitively understand the advantages and details of the optimization plan, providing a scientific, reasonable, and operable reference for shipping decisions.

[0078] The present invention also relates to a ship route combination optimization system based on standard routes. This system corresponds to the above-mentioned ship route combination optimization method based on standard routes and can be understood as a system for implementing the above-mentioned ship route combination optimization method based on standard routes. The system includes a data input module, a voyage operation revenue calculation module, a problem abstraction and modeling module, a dynamic programming solution module, and a data output module that are connected in sequence. Among them, the data input module calls the basic data tables of multiple standard routes and the ship speed and fuel consumption data tables from the ship database, and obtains the ship's no-load and full-load speeds, oil price index, and operation period input by the user. The basic data tables include route names, standard route operation durations, no-load and full-load distances, voyage revenues, and port usage fees. The ship speed and fuel consumption data tables include the fuel consumption of the ship at different speeds. The voyage operation revenue calculation module calculates the fuel consumption of the ship in the no-load and full-load states according to the ship's no-load speed and full-load speed input by the user, in combination with the no-load and full-load distances in the ship speed and fuel consumption data tables and the standard route basic data tables. According to the calculated fuel consumption and the oil price index input by the user, it calculates the fuel cost of the ship. Then, based on the voyage revenue in the standard route basic data table, it deducts the cost expenditures including fuel cost and port usage fees to calculate the voyage operation revenue of each standard route. The problem abstraction and modeling module abstracts the route combination problem into a complete knapsack problem model. Among them, the operation period is mapped to the knapsack capacity, the standard route operation duration is mapped to the item weight, the calculated voyage operation revenue is mapped to the item value, and the characteristic that the route supports multiple operations is mapped to the characteristic that the item supports repeated selection. The dynamic programming solution module uses the dynamic programming algorithm to solve the complete knapsack problem model. It disassembles the original problem of selecting the maximum voyage operation revenue of standard routes within the total operation period into several sub-problems of selecting the maximum voyage operation revenue of partial standard routes at each time point within the total operation period through the state transition equation, calculates the optimal solutions of each sub-problem, and gradually constructs the optimal solution of the original problem through the backtracking process to obtain the optimal route combination plan. The data output module calculates the total voyage operation revenue of the optimal route combination plan and outputs the total voyage operation revenue result. For reference, see Figure 2 。

[0079] Further, in the problem abstraction and modeling module, on the basis of mapping the operation period to the knapsack capacity, the standard route operation duration to the item weight, and the voyage operation revenue to the item value, the complete knapsack problem model also incorporates constraint conditions for the route combination. The constraint conditions for the route combination include the maximum allowable number of standard route constraints, the priority constraints of specific standard routes, and / or the mutual dependence constraints between standard routes.

[0080] Further, in the dynamic programming solution module, the dynamic programming algorithm is used to solve the complete knapsack problem model. First, a two-dimensional array dp[i][j] is defined to represent the maximum voyage operation revenue of the first i standard routes under the condition that the total voyage time does not exceed j, where i is the standard route number and j is each time point within the total operation period. Then, the boundary conditions of the two-dimensional array dp[i][j] are dynamically programmed. The boundary conditions are that when there is no standard route or the total voyage time is 0, the maximum voyage operation revenue is 0. Next, the state transition equation is used to decompose the problem and iteratively calculate and update the values of the two-dimensional array. Finally, the optimal route combination plan is obtained through backtracking. The state transition equation is as follows:

[0081] When j ≥ t i , dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - t i + c i );

[0082] When j < t i , dp[i][j] = dp[-1][j];

[0083] Wherein, t i is the standard voyage operation duration of the i-th route, and c i is the voyage operation revenue of the i-th standard route.

[0084] The method and system for optimizing ship route combinations based on standard routes according to the present invention abstract the route combination problem into a complete knapsack problem model, call the dynamic programming algorithm to solve it, and obtain the optimal route combination plan, filling the technical gap. The complex route combination problem is analogized and mapped to the classic knapsack problem, and the actual shipping business scenario is transformed into a mathematical model - the complete knapsack problem model. The dynamic programming algorithm updates the array values by setting the state array and finally obtains the optimal route combination through backtracking, ensuring that the optimal route combination plan is obtained under the premise of meeting the time limit, thereby maximizing the voyage operation revenue. It has the characteristics of high efficiency and accuracy, can effectively reduce the transportation cost, improve the transportation efficiency, and provide scientific decision-making support for shipping enterprises.

[0085] It should be noted that the above specific implementation manners can enable those skilled in the art to understand the present invention more comprehensively, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail with reference to the drawings and embodiments, those skilled in the art should understand that the present invention can still be modified or equivalently replaced. In short, all technical solutions and their improvements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the patent of the present invention.

Claims

1. A method for optimizing ship route combinations based on standard routes, characterized in that, Including the following steps: Data input step: Call the basic data tables of multiple standard shipping lines and the ship speed and fuel consumption data tables from the ship database, and obtain the ship's no-load and full-load speeds, oil price index, and operation period input by the user. The basic data tables include the shipping line name, standard shipping line operation duration, no-load and full-load distances, voyage income, and port usage fees. The ship speed and fuel consumption data tables include the fuel consumption of the ship at different speeds; Voyage operation income calculation step: According to the ship's no-load speed and full-load speed input by the user, combined with the ship speed and fuel consumption data table and the no-load and full-load distances in the standard shipping line basic data table, calculate the fuel consumption of the ship in the no-load and full-load states; According to the calculated fuel consumption and the oil price index input by the user, calculate the fuel cost of the ship; Then, based on the voyage income in the standard shipping line basic data table, deduct the cost expenditures including fuel cost and port usage fees to calculate the voyage operation income of each standard shipping line; Problem abstraction and modeling step: Abstract the shipping line combination problem into a complete knapsack problem model. Among them, the operation period is mapped to the knapsack capacity, the standard shipping line operation duration is mapped to the item weight, the calculated voyage operation income is mapped to the item value, and the characteristic that the shipping line supports multiple operations is mapped to the characteristic that the item supports repeated selection; Dynamic programming solution step: Use the dynamic programming algorithm to solve the complete knapsack problem model. Decompose the original problem of the maximum voyage operation income of selecting standard shipping lines within the total operation period into several sub-problems of the maximum voyage operation income of selecting partial standard shipping lines at each time point within the total operation period through the state transition equation, calculate the optimal solutions of each sub-problem, and gradually construct the optimal solution of the original problem through the backtracking process to obtain the optimal shipping line combination plan; Data output step: Calculate the total voyage operation income of the optimal shipping line combination plan and output the result of the total voyage operation income.

2. The method for optimizing the combination of ship routes based on standard routes according to claim 1, wherein, In the voyage operation income calculation step, according to the ship's no-load speed input by the user, combine the ship speed and fuel consumption data table to determine the fuel consumption per unit time corresponding to the ship's no-load speed, and calculate the no-load sailing time according to the ratio of the no-load and full-load distance in the standard shipping line basic data table to the ship's no-load speed. Then, multiply the fuel consumption per unit time corresponding to the ship's no-load speed by the no-load sailing time to calculate the fuel consumption of the ship in the no-load state; According to the ship's full-load speed input by the user, combine the ship speed and fuel consumption data table to determine the fuel consumption per unit time corresponding to the ship's full-load speed, and calculate the full-load sailing time according to the ratio of the full-load distance in the standard shipping line basic data table to the ship's full-load speed. Then, multiply the fuel consumption per unit time corresponding to the ship's full-load speed by the full-load sailing time to calculate the fuel consumption of the ship in the full-load state.

3. The method for optimizing the combination of ship routes based on standard routes according to claim 2, wherein In the voyage operation income calculation step, the cost expenditures deducted also include several combinations of depreciation cost, maintenance cost, labor cost, and environmental cost from the basic data table or input by the user.

4. The method for optimizing the ship route combination based on the standard route according to any one of claims 1 to 3, characterized in that, In the step of abstract modeling of the problem, based on mapping the operation period to the knapsack capacity, the standard route operation duration to the item weight, and the voyage operation revenue to the item value, the full knapsack problem model also incorporates constraint conditions for the route combination. The constraint conditions for the route combination include the constraint on the maximum allowed number of standard routes, the priority constraint for specific standard routes, and / or the mutual dependence constraint between standard routes.

5. The method for optimizing a ship route combination based on a standard route according to any one of claims 1 to 3, characterized in that In the step of solving by dynamic programming, the dynamic programming algorithm is used to solve the full knapsack problem model. First, a two-dimensional array dp[i][j] is defined to represent the maximum voyage operation revenue of the first i standard routes under the condition that the total sailing time does not exceed j, where i is the standard route number and j is each time point within the total operation period. And the boundary conditions of the two-dimensional array dp[i][j] are dynamically planned. The boundary conditions are that when there are no standard routes or the total sailing time is 0, the maximum voyage operation revenue is 0.

6. The method for optimizing the combination of ship routes based on standard routes according to claim 5, wherein In the step of solving by dynamic programming, the state transition equation is: When j ≥ t i dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - t i + c i ); When j < t i dp[i][j] = dp[i - 1][j]; Among them, t i is the standard route operation duration of the i-th route, and c i is the voyage operation revenue of the i-th standard route.

7. The method for optimizing the combination of ship routes based on standard routes according to any one of claims 1 to 3, characterized in that In the step of solving by dynamic programming, the first K optimal route combination schemes are obtained, where K is a positive integer. The data output step outputs these K optimal route combination schemes, calculates the total voyage operation revenue of each optimal route combination scheme and outputs it, and at the same time outputs the route names, standard route operation durations, voyage revenues, and cost expenditures of each standard route included in each optimal route combination scheme.

8. A ship route combination optimization system based on standard routes, characterized in that, It includes a data input module, a voyage operation revenue calculation module, a problem abstract modeling module, a dynamic programming solution module, and a data output module connected in sequence. Among them, The data input module calls the basic data tables of multiple standard routes and the ship speed and fuel consumption data tables from the ship database, and obtains the ship's no-load and full-load speeds, oil price index, and operation period input by the user. The basic data table includes the route name, standard route operation duration, no-load and full-load distances, voyage revenue, and port usage fees. The ship speed and fuel consumption data table includes the fuel consumption of the ship at different speeds. The voyage operation revenue calculation module calculates the fuel consumption of the ship in the no-load and full-load states according to the ship's no-load speed and full-load speed input by the user, in combination with the no-load and full-load distances in the ship speed and fuel consumption data table and the standard route basic data table. According to the calculated fuel consumption and the oil price index input by the user, it calculates the fuel cost of the ship. Then, based on the voyage revenue in the standard route basic data table, it deducts the cost expenditures including the fuel cost and port usage fees to calculate the voyage operation revenue of each standard route. The problem abstract modeling module abstracts the route combination problem into a full knapsack problem model. Among them, the operation period is mapped to the knapsack capacity, the standard route operation duration is mapped to the item weight, the calculated voyage operation revenue is mapped to the item value, and the characteristic that the route supports multiple operations is mapped to the characteristic that the item supports repeated selection. The dynamic programming solution module uses the dynamic programming algorithm to solve the complete knapsack problem model. By means of the state transition equation, the original problem of the maximum voyage operation revenue of selecting standard routes within the total operation period is decomposed into several sub-problems of the maximum voyage operation revenue of selecting partial standard routes at each time point within the total operation period, calculates the optimal solutions of each sub-problem, and gradually constructs the optimal solution of the original problem through the backtracking process, so as to obtain the optimal route combination plan; The data output module calculates the total voyage operation revenue of the optimal route combination plan and outputs the result of the total voyage operation revenue.

9. The ship route combination optimization system based on standard routes according to claim 8, wherein, In the problem abstraction and modeling module, on the basis of mapping the operation period to the knapsack capacity, the operation duration of the standard route to the item weight, and the voyage operation revenue to the item value, the complete knapsack problem model also incorporates the constraint conditions for the route combination. The constraint conditions for the route combination include the constraint on the maximum allowable number of standard routes, the priority constraint of specific standard routes, and / or the mutual dependence constraint between standard routes.

10. The ship route combination optimization system based on standard routes according to claim 8 or 9, characterized in that, In the dynamic programming solution module, the dynamic programming algorithm is used to solve the complete knapsack problem model. First, a two-dimensional array dp[i][j] is defined to represent the maximum voyage operation revenue of the first i standard routes under the condition that the total sailing time does not exceed j, where i is the standard route number and j is each time point within the total operation period; and the boundary conditions of the two-dimensional array dp[i][j] are dynamically programmed. The boundary conditions are that when there are no standard routes or the total sailing time is 0, the maximum voyage operation revenue is 0; then the state transition equation is used for problem decomposition and iterative calculation to update the values of the two-dimensional array, and finally the optimal route combination plan is obtained through backtracking. The state transition equation is: When j ≥ t i dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - t i + c i ); When j < t i dp[i][j] = dp[i - 1][j]; where t i is the standard route operation duration of the i-th route, and c i is the voyage operation revenue of the i-th standard route.

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