Lane transportation sailing decision-making method and system, and readable storage medium

Abstract

The invention provides a liner transportation navigation decision-making method and system and a readable storage medium, and the method comprises the steps: obtaining route data, a port position and ECA region division, constructing a liner transportation service network total operation cost minimization model according to the route data, the port position and the ECA region division, and generating a total objective function; according to an epsilon external approximation estimation method, representing the fuel consumption cost in the total objective function through the sum of a plurality of piecewise linear functions; and calculating the total operation cost of the liner transportation service network based on the liner transportation service network total operation cost minimization model, the fuel consumption cost and the initial port combination, and performing comparison to obtain a sailing decision optimal solution under the target of the minimum total operation cost. The sailing decision optimal solution comprises a ship deployment optimal solution, a shipping schedule design optimal solution and a sailing speed optimization decision optimal solution. By means of the technical scheme, liner transportation navigation can be optimized, changing market requirements can be met more flexibly, and the operation cost is saved.

Description

Technical Field

[0001] The present application relates to the technical field of navigation decision-making, and in particular to a liner shipping navigation decision-making method and system, and a readable storage medium. Background Art

[0002] In the global shipping market, with the implementation of the sulfur emission control area (SECA) and emission control area (ECA) regulations by the International Maritime Organization (IMO) worldwide, the shipping industry faces strict environmental protection requirements. In response to these regulations, ship operators usually adopt emission reduction measures such as fuel switching, installation of exhaust gas scrubbers and use of LNG (Liquefied Natural Gas). However, considering the technical maturity, investment cost and energy supply, liner companies currently mainly adopt fuel switching strategies to meet ECA regulations. The existing decision-making systems and methods for complex liner shipping still have the following shortcomings: The problem of liner shipping service network planning with transshipment function under fuel switching has not received much attention in existing research. On the theoretical level, the operational management problem of integrating ship deployment on the basis of ship schedule design and speed optimization is a large comprehensive problem, and the solution is unlikely to be achieved due to complex strategic planning and large scale of the problem. Although previous studies have focused on economic goals, the existing network operation decision optimization model is complex and fails to fully consider the resource constraints of liner companies. Too many adjustments to operational decisions will lead to corresponding cost increases, which may lead to one-sided decisions. Existing studies cannot determine the handover of liner ships at transshipment ports. Some models have roughly set the transshipment time, but cannot guarantee transshipment operations that are in line with reality. There is a lack of methods for comprehensive integration of ship schedule design, speed optimization, and ship deployment. Summary of the invention

[0003] This application aims to solve or improve the above technical problems.

[0004] To this end, the first objective of the present application is to provide a liner shipping navigation decision method.

[0005] The second objective of the present application is to provide a liner shipping navigation decision system.

[0006] The third objective of the present application is to provide a liner shipping navigation decision system.

[0007] The fourth object of the present application is to provide a readable storage medium.

[0008] To achieve the first purpose of the present application, the technical solution of the first aspect of the present application provides a liner shipping navigation decision-making method, including: obtaining route data, port locations and ECA area divisions, constructing a total operating cost minimization model for a liner shipping service network according to the route data, port locations and ECA area divisions, and generating a total objective function, wherein the total operating cost minimization model for the liner shipping service network includes a nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network; based on the speed variables inside and outside the ECA region, according to the ε outer approximation estimation method, the fuel consumption cost in the total objective function is represented by the sum of multiple piecewise linear functions; the initial port combination configuration is adjusted and optimized by a backtracking algorithm and / or an enumeration method to obtain an initial port combination; the total operating cost of the liner shipping service network is calculated based on the total operating cost minimization model for the liner shipping service network, the fuel consumption cost and the initial port combination, and the optimal solution for the navigation decision under the goal of minimizing the total operating cost is obtained by comparison, and the optimal solution for the navigation decision includes the optimal solution for ship deployment, the optimal solution for ship schedule design and the optimal solution for speed optimization decision.

[0009] According to the liner shipping navigation decision method provided by the present application, firstly, the route data, port location and ECA regional division are obtained, and the total operating cost minimization model of the liner shipping service network is constructed according to the route data, port location and ECA (Emission Control Area) regional division, and the total objective function is generated. The total operating cost minimization model of the liner shipping service network includes a nonlinear mixed integer programming MINLP (Mixed-Integer Nonlinear Programming) model for minimizing the total operating cost of the liner shipping service network. Establishing a new nonlinear mixed integer programming MINLP model can enable liner companies to start from the easy-to-operate speed optimization, taking into account the influence of ECA regulations and transit time windows, and by determining the variable initial port combination and the necessary increase in the number of liner ships, the original liner shipping service network is reshaped. Then, based on the speed variables inside and outside the ECA region, the ε outer approximation estimation method is adopted, and the fuel consumption cost in the objective function is approximated by the sum of many piecewise linear functions. The initial port combination configuration is adjusted and optimized by the backtracking algorithm and / or enumeration method to obtain the initial port combination. Finally, the total operating cost of the liner shipping service network is calculated based on the total operating cost minimization model of the liner shipping service network, the fuel consumption cost and the initial port combination. The costs corresponding to different initial port combinations are compared to obtain the optimal solution of ship deployment, ship schedule design and speed optimization decision under the goal of minimizing the total operating cost. The liner shipping navigation decision method provided in this application effectively reduces the total operating cost by integrating the three major operational decisions of ship deployment, ship schedule design and speed optimization. Compared with the existing technology, it significantly improves the feasibility when dealing with complex strategic plans and large-scale problems, avoids the limitations of traditional methods that are difficult to achieve due to the large scale of the problem, and reveals the fine-tuning strategy for existing network operation decisions. By optimizing the initial port combination and speed configuration, the liner company enhances its ability to respond to market changes and improves the competitiveness of the enterprise. Speed ​​optimization not only reduces fuel consumption costs, but also further achieves operating cost savings by moderately relaxing the transit time window restrictions. When responding to fuel price fluctuations, speed optimization effectively alleviates cost pressure by giving priority to deploying ships on routes with longer voyages and reducing speed.

[0010] In addition, the technical solution provided by this application may also have the following additional technical features:

[0011] In some technical solutions, optionally, the liner shipping navigation decision method also includes: feeding back the optimal solution of ship deployment, the optimal solution of ship schedule design, the optimal solution of speed optimization decision and the initial port setting into the shipping network for visualization, so as to obtain the shipping network corresponding to the optimal liner shipping decision.

[0012] In this technical solution, the liner shipping navigation decision-making method also includes feeding back the optimal solution of ship deployment, the optimal solution of ship schedule design, the optimal solution of speed optimization decision and the initial port setting into the shipping network for visualization, so as to obtain the shipping network corresponding to the optimal liner shipping decision.

[0013] In some technical solutions, optionally, the formula of the overall objective function is:

[0014] minC=C V +C P +C F ;

[0015] Where C is the total operating cost of the liner shipping service network, C V is the ship deployment cost, C P is the transfer penalty cost, C F is the fuel consumption cost, and min is the minimum value.

[0016] In this technical solution, the total operating cost of the liner shipping service network can be derived based on the ship deployment cost, transshipment penalty cost and fuel consumption cost.

[0017] In some technical solutions, optionally, the calculation formula of the ship deployment cost is:

[0018]

[0019] Where R is the set of routes r, m r represents the number of liner ships deployed on route r, represents the weekly ship deployment cost on route r;

[0020] The calculation formula for the transshipment penalty cost is:

[0021]

[0022] Where H is the set of backward segments h of route r∈R after transshipment operation, λ h is a 0-1 decision variable, which determines whether the liner ship completes the handover within the transshipment time window during the transshipment operation. If yes, it is 1, otherwise it is 0. represents the transshipment penalty cost incurred by the liner ship for the backward leg h when it fails to perform transshipment operations within the transshipment time window;

[0023] The calculation formula for fuel consumption cost is:

[0024]

[0025] Among them, P E , P N Respectively represent the fuel prices used by liner ships in ECA and NECA, ∈I r , I r represents the set of segments i on route r∈R, They represent the flight segments i∈I of route r∈R respectively r The range of ECA and NECA, Q(v 0 ) represents the fuel consumption rate at the design speed of the liner ship, They represent the speed of ECA and NECA (nitrogen emission control area) on the route r∈R on the segment i∈Ir, v represents the speed of the ship, and v 0 represents the design speed of the liner ship, θ represents the power exponent of the fuel consumption rate with respect to the speed function, and Q(v) represents the fuel consumption rate.

[0026] In this technical solution, the ship deployment cost, transshipment penalty cost and fuel consumption cost are obtained by calculation.

[0027] In some technical solutions, optionally, the constraints of the overall objective function include:

[0028] Initial port settings constraints:

[0029]

[0030] Among them, f ri is a 0-1 decision variable, determining the flight segment i∈I r Whether the first port is set as the initial port of route r∈R, 0 if yes, 1 if no, N r is the number of flight segments of route r∈R, R is the set of routes r;

[0031] Port stop time constraints:

[0032]

[0033] in, The ship on the route r∈R is in the segment i∈I r Port call time at the first port, They represent the ship on route r∈R in segment i∈I r Arrival and departure times of the first port, f ri is a 0-1 decision variable, determining the flight segment i∈I r Whether the first port is set as the initial port of route r∈R, 0 if yes, 1 if no, R is the set of routes r, I r represents the set of flight segments i on route r∈R;

[0034] The time constraints for deploying ships require:

[0035]

[0036] Where R is the set of routes r, I r represents the set of segments i on route r∈R, They represent the flight segments i∈I of route r∈R respectively r The flight distance of ECA and NECA, They represent the flight segments i∈I of route r∈R respectively r The speed of ECA and NECA, The ship on the route r∈R is in the segment i∈I r Port call time at the first port, m r represents the number of liner ships deployed on route r;

[0037] Speed ​​constraints for liner ships:

[0038]

[0039] in, They represent the minimum and maximum speeds of ships on route r∈R, They represent the flight segments i∈I of route r∈R respectively r The speed of ECA and NECA, R is the set of routes r, I r represents the set of flight segments i on route r∈R;

[0040] Port visit frequency constraint requirements:

[0041]

[0042] in, They represent the ship on route r∈R in segment i∈I r The arrival and departure weeks of the first port, They represent the ship on route r∈R in segment i∈I r The arrival and departure times of the first port, R is the set of routes r, I r represents the set of flight segments i on route r∈R;

[0043] The adjustment setting constraints of the transit time window are:

[0044]

[0045] Where H is the set of backward segments h of route r∈R after transshipment operation, L is the set of backward segments l of route r∈R after transshipment operation to route s∈R, l={(s, j)│(r, i)}, j∈I s , i∈I r , and r, s∈R, r≠s, R is the set of routes r, Ir represents the set of segments i on route r∈R, I s represents the set of flight segments j on route s∈R, represents the departure time of the liner ship at the first port of the voyage segment h∈H, represents the arrival time of the liner ship at the first port of the voyage segment l∈L, represents the number of weeks that the liner ship leaves the port at the first end of the route h∈H, T represents the number of weeks that a liner ship arrives at the first port of the route l∈L, min 、T max Respectively represent the upper and lower limits of the transit time window, where T max =24h and T min =8h, is a 0-1 decision variable, which determines whether the departure time of the next liner ship at the transshipment port that meets the transshipment time window requirement is in the current week during the transshipment operation. If yes, it is 0, otherwise it is 1. The value of includes one of the following: or β h is a 0-1 decision variable, which determines whether the arrival time of the liner ship at the transshipment port meets the transshipment time window requirement and its visit time is between [0, 168]. If yes, it is 0, otherwise, it is 1.

[0046] Decision constraints for transfer operations:

[0047]

[0048] in, represents the departure time of the liner ship at the first port of the voyage segment h∈H, represents the arrival time of the liner ship at the first port of the voyage segment l∈L, represents the number of weeks that the liner ship leaves the port at the first end of the route h∈H, represents the number of weeks that a liner ship arrives at the first port of the route l∈L, λ h is a 0-1 decision variable, which determines whether the liner ship completes the transfer within the transfer time window during the transfer operation. If yes, it is 1, otherwise it is 0. h is a 0-1 decision variable, which determines whether the arrival time of the liner ship at the transshipment port meets the transshipment time window requirement and its visit time is between [0, 168]. If yes, it is 0, otherwise, it is 1. is a 0-1 decision variable, which determines whether the departure time of the next liner ship at the transshipment port that meets the transshipment time window requirement is in the current week during the transshipment operation. If yes, it is 0, otherwise it is 1. The value of includes one of the following: or Tmin , T max Respectively represent the upper and lower limits of the transit time window, where T max =24h and T min =8h.

[0049] In this technical solution, the constraints of the model include the setting constraints of the initial port, the port berthing time constraints, the available time constraints for deploying ships, the speed constraints of liner ships, the port visit frequency constraints, the adjustment setting constraints of the transshipment time window and the determination constraints of the transshipment operation.

[0050] In some technical solutions, optionally, according to the ε outer approximation estimation method, the fuel consumption cost in the total objective function is represented by the sum of multiple piecewise linear functions, including: introducing substitution variables to linearize nonlinear terms; approximating the fuel consumption cost in the objective function by the sum of multiple piecewise linear functions; introducing variables to transform the nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network into a second nonlinear mixed integer programming MINLP model.

[0051] In this technical solution, according to the ε outer approximation estimation method, the fuel consumption cost in the total objective function is represented by the sum of multiple piecewise linear functions. Specifically, a substitution variable is first introduced to linearize the nonlinear term. Then, the fuel consumption cost in the objective function is approximated by the sum of multiple piecewise linear functions. Finally, a variable is introduced to transform the nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network into a second nonlinear mixed integer programming MINLP model.

[0052] In some technical solutions, optionally, the initial port combination configuration is adjusted and optimized by a backtracking algorithm and / or an enumeration method to obtain an initial port combination, including: generating an initial port combination of a large-scale shipping network by a backtracking algorithm; and / or obtaining an initial port combination of a medium and smaller-scale shipping network by an enumeration method.

[0053] In this technical solution, the initial port combination configuration is adjusted and optimized by a backtracking algorithm and / or an enumeration method to obtain an initial port combination, specifically, the initial port combination of a large-scale shipping network is generated by a backtracking algorithm. And / or the initial port combination of a medium- and smaller-scale shipping network is obtained by an enumeration method. The initial port combination of a large-scale shipping network is generated by a backtracking algorithm, which can solve the problem of low efficiency that may be faced by the enumeration method in solving the initial port combination of a large-scale instance.

[0054] To achieve the second purpose of the present application, the technical solution of the second aspect of the present application provides a liner shipping navigation decision system, including: a network construction module, which is used to obtain route data, port locations and ECA area divisions, and construct a total operating cost minimization model for the liner shipping service network according to the route data, port locations and ECA area divisions to generate a total objective function. The total operating cost minimization model for the liner shipping service network includes a nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network; a fuel consumption cost calculation module, which is used to represent the fuel consumption cost in the total objective function by the sum of multiple piecewise linear functions based on the speed variables inside and outside the ECA area according to the ε outer approximation estimation method; an initial port combination generation module, which is used to adjust and optimize the initial port combination configuration through a backtracking algorithm and / or an enumeration method to obtain an initial port combination; a liner shipping decision optimization module, which is used to calculate the total operating cost of the liner shipping service network based on the total operating cost minimization model for the liner shipping service network, the fuel consumption cost and the initial port combination, and compare to obtain the optimal solution of the navigation decision under the goal of minimizing the total operating cost, and the optimal solution of the navigation decision includes the optimal solution for ship deployment, the optimal solution for ship schedule design and the optimal solution for speed optimization decision.

[0055] The liner shipping navigation decision system provided by the present application includes a network construction module, a fuel consumption cost calculation module, an initial port combination generation module and a liner shipping decision optimization module. Among them, the network construction module is used to obtain route data, port locations and ECA regional divisions, and construct a total operating cost minimization model for the liner shipping service network based on the route data, port locations and ECA regional divisions to generate a total objective function. The total operating cost minimization model for the liner shipping service network includes a nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network. The fuel consumption cost calculation module is used to represent the fuel consumption cost in the total objective function by the sum of multiple piecewise linear functions based on the speed variables inside and outside the ECA region according to the ε outer approximation estimation method. The initial port combination generation module is used to adjust and optimize the initial port combination configuration through a backtracking algorithm and / or an enumeration method to obtain an initial port combination. The liner shipping decision optimization module is used to calculate the total operating cost of the liner shipping service network based on the total operating cost minimization model of the liner shipping service network, the fuel consumption cost and the initial port combination, and compare to obtain the optimal solution of the navigation decision under the goal of minimizing the total operating cost. The optimal solution of the navigation decision includes the optimal solution of ship deployment, the optimal solution of ship schedule design and the optimal solution of speed optimization decision. The liner shipping navigation decision system provided by this application effectively reduces the total operating cost by integrating the three major operational decisions of ship deployment, ship schedule design and speed optimization. Compared with the prior art, it significantly improves the feasibility when dealing with complex strategic plans and large-scale problems, avoids the limitations of traditional methods that are difficult to achieve due to the large scale of the problem, and reveals the fine-tuning strategy for the existing network operation decision. By optimizing the initial port combination and speed configuration, the liner company enhances the ability to respond to market changes and improves the competitiveness of the enterprise. Speed ​​optimization not only reduces the fuel consumption cost, but also further realizes the saving of operating costs by moderately relaxing the transit time window limit. When responding to fuel price fluctuations, speed optimization effectively alleviates cost pressure by preferentially deploying ships on routes with longer voyages and reducing the speed.

[0056] To achieve the third purpose of the present application, the technical solution of the third aspect of the present application provides a liner shipping navigation decision system, including: a memory and a processor, wherein the memory stores programs or instructions that can be run on the processor, and when the processor executes the program or instruction, the liner shipping navigation decision method of any one of the technical solutions of the first aspect is implemented, so it has the technical effect of any one of the technical solutions of the first aspect above, which will not be repeated here.

[0057] In order to achieve the fourth purpose of the present application, the technical solution of the fourth aspect of the present application provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by the processor, the steps of the liner shipping navigation decision method of any one of the technical solutions of the first aspect are implemented, and thus the technical effects of any one of the technical solutions of the first aspect are obtained, which will not be repeated here.

[0058] Additional aspects and advantages of the present application will become apparent in the following description or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0060] Figure 1 This is a schematic diagram of the steps of a liner shipping navigation decision method according to an embodiment of the present application;

[0061] Figure 2 This is a schematic diagram of the steps of a liner shipping navigation decision method according to an embodiment of the present application;

[0062] Figure 3 This is a schematic diagram of the steps of a liner shipping navigation decision method according to an embodiment of the present application;

[0063] Figure 4 This is a schematic diagram of the steps of a liner shipping navigation decision method according to an embodiment of the present application;

[0064] Figure 5 This is a schematic block diagram of the structure of a liner shipping navigation decision system according to an embodiment of the present application;

[0065] Figure 6 This is a schematic block diagram of the structure of a liner shipping navigation decision system according to an embodiment of the present application;

[0066] Figure 7 This is a schematic diagram of the steps of a liner shipping navigation decision method according to an embodiment of the present application;

[0067] Figure 8 A schematic diagram of tangent generation for outer approximation estimation of a fuel consumption rate function in a liner shipping navigation decision method according to an embodiment of the present application;

[0068] FIG9( a ) is a schematic diagram of speed comparison of a liner shipping navigation decision method according to an embodiment of the present application;

[0069] FIG9( b ) is a schematic diagram of speed comparison of a liner shipping navigation decision method according to an embodiment of the present application;

[0070] FIG9( c ) is a schematic diagram of speed comparison of a liner shipping navigation decision method according to an embodiment of the present application;

[0071] FIG. 10( a ) is a schematic diagram of the optimization of the speed of each segment of all routes under the change of fuel price in a liner shipping navigation decision method according to an embodiment of the present application;

[0072] FIG10( b ) is a schematic diagram of the optimization of the speed of each segment of all routes under the change of fuel price in the liner shipping navigation decision method according to an embodiment of the present application;

[0073] FIG10( c ) is a schematic diagram of the optimization of the speed of each segment of all routes under the change of fuel price in the liner shipping navigation decision method according to an embodiment of the present application;

[0074] FIG. 11( a ) is a schematic diagram of the optimization of the speed of each segment of all routes under the change of the transit time window of the liner shipping navigation decision method according to an embodiment of the present application;

[0075] FIG. 11( b ) is a schematic diagram of the optimization of the speed of each segment of all routes under the change of the transit time window of the liner shipping navigation decision method according to an embodiment of the present application;

[0076] FIG11( c ) is a schematic diagram of the speed optimization of each segment of all routes under the change of the transit time window of the liner shipping navigation decision method according to an embodiment of the present application.

[0077] in, Figure 5 and Figure 6 The corresponding relationship between the reference numerals and component names in the figure is:

[0078] 10: liner shipping navigation decision system; 110: network construction module; 120: fuel consumption cost calculation module; 130: initial port combination generation module; 140: liner shipping decision optimization module; 20: liner shipping navigation decision system; 300: memory; 400: processor. DETAILED DESCRIPTION

[0079] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0080] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0081] Refer to the following Figure 1 to Figure 11(c) Describe the liner shipping navigation decision method and system and readable storage medium of some embodiments of the present application.

[0082] like Figure 1 As shown, the embodiment of the first aspect of the present application provides a liner shipping navigation decision method, comprising the following steps:

[0083] Step S102: obtaining route data, port locations and ECA regional divisions, constructing a total operating cost minimization model for a liner shipping service network according to the route data, port locations and ECA regional divisions, and generating a total objective function, wherein the total operating cost minimization model for a liner shipping service network includes a nonlinear mixed integer programming MINLP model for minimizing the total operating cost of a liner shipping service network;

[0084] Step S104: Based on the speed variables inside and outside the ECA area, according to the ε outer approximation estimation method, the fuel consumption cost in the total objective function is expressed by the sum of multiple piecewise linear functions;

[0085] Step S106: adjusting and optimizing the initial port combination configuration by a backtracking algorithm and / or an enumeration method to obtain an initial port combination;

[0086] Step S108: Calculate the total operating cost of the liner shipping service network based on the total operating cost minimization model of the liner shipping service network, the fuel consumption cost and the initial port combination, and compare to obtain the optimal solution of the navigation decision under the goal of minimizing the total operating cost. The optimal solution of the navigation decision includes the optimal solution of ship deployment, the optimal solution of ship schedule design and the optimal solution of speed optimization decision.

[0087] According to the liner shipping navigation decision method provided by this embodiment, firstly, the route data, port location and ECA regional division are obtained, and the total operating cost minimization model of the liner shipping service network is constructed according to the route data, port location and ECA (Emission Control Area) regional division, and the total objective function is generated. The total operating cost minimization model of the liner shipping service network includes a nonlinear mixed integer programming MINLP (Mixed-Integer Nonlinear Programming) model for minimizing the total operating cost of the liner shipping service network. Establishing a new nonlinear mixed integer programming MINLP model can enable liner companies to start from the easy-to-operate speed optimization, taking into account the influence of ECA regulations and transit time windows, and by determining the variable initial port combination and the necessary increase in the number of liner ships, the original liner shipping service network is reshaped. Then, based on the speed variables inside and outside the ECA region, the ε outer approximation estimation method is adopted to approximate the fuel consumption cost in the objective function by the sum of many piecewise linear functions. The initial port combination configuration is adjusted and optimized by the backtracking algorithm and / or enumeration method to obtain the initial port combination. Finally, the total operating cost of the liner shipping service network is calculated based on the total operating cost minimization model of the liner shipping service network, the fuel consumption cost and the initial port combination. The costs corresponding to different initial port combinations are compared to obtain the optimal solution of ship deployment, ship schedule design and speed optimization decision under the goal of minimizing the total operating cost. The liner shipping navigation decision method provided in this application effectively reduces the total operating cost by integrating the three major operational decisions of ship deployment, ship schedule design and speed optimization. Compared with the existing technology, it significantly improves the feasibility when dealing with complex strategic plans and large-scale problems, avoids the limitations of traditional methods that are difficult to achieve due to the large scale of the problem, and reveals the fine-tuning strategy for existing network operation decisions. By optimizing the initial port combination and speed configuration, the liner company enhances its ability to respond to market changes and improves the competitiveness of the enterprise. Speed ​​optimization not only reduces fuel consumption costs, but also further achieves operating cost savings by moderately relaxing the transit time window restrictions. When responding to fuel price fluctuations, speed optimization effectively alleviates cost pressure by giving priority to deploying ships on routes with longer voyages and reducing speed.

[0088] like Figure 2 As shown, the liner shipping navigation decision method according to an embodiment of the present application further includes the following steps:

[0089] Step S202: Feedback the optimal solution of ship deployment, optimal solution of ship schedule design, optimal solution of speed optimization decision and initial port setting to the shipping network for visualization, and obtain the shipping network corresponding to the optimal liner transportation decision.

[0090] In this embodiment, the liner shipping navigation decision method also includes feeding back the optimal solution of ship deployment, the optimal solution of ship schedule design, the optimal solution of speed optimization decision and the initial port setting into the shipping network for visualization, so as to obtain the shipping network corresponding to the optimal liner shipping decision.

[0091] In some embodiments, optionally, the formula of the overall objective function is:

[0092] minC=C V +C P +C F ;

[0093] Where C is the total operating cost of the liner shipping service network, C V is the ship deployment cost, C P is the transfer penalty cost, C F is the fuel consumption cost, and min is the minimum value. The total operating cost of the liner shipping service network can be obtained based on the ship deployment cost, transshipment penalty cost and fuel consumption cost.

[0094] In some embodiments, optionally, the calculation formula of the ship deployment cost is:

[0095]

[0096] Where R is the set of routes r, m r represents the number of liner ships deployed on route r, represents the weekly ship deployment cost on route r;

[0097] The calculation formula for the transshipment penalty cost is:

[0098]

[0099] Where H is the set of backward segments h of route r∈R after transshipment operation, λ h is a 0-1 decision variable, which determines whether the liner ship completes the handover within the transshipment time window during the transshipment operation. If yes, it is 1, otherwise it is 0. represents the transshipment penalty cost incurred by the liner ship for the backward leg h when it fails to perform transshipment operations within the transshipment time window;

[0100] The calculation formula for fuel consumption cost is:

[0101]

[0102] Among them, P E , P N Respectively represent the fuel prices used by liner ships in ECA and NECA, ∈I r , I rrepresents the set of segments i on route r∈R, They represent the flight segments i∈I of route r∈R respectively r The range of ECA and NECA, Q(v 0 ) represents the fuel consumption rate at the design speed of the liner ship, They represent the flight segments i∈I of route r∈R respectively r The speed of ECA and NECA, v represents the speed of the ship, and v 0 represents the design speed of the liner ship, θ represents the power exponent of the fuel consumption rate with respect to the speed function, and Q(v) represents the fuel consumption rate. The ship deployment cost, transshipment penalty cost and fuel consumption cost are obtained by calculation.

[0103] In some embodiments, optionally, the constraints of the overall objective function include:

[0104] Initial port settings constraints:

[0105]

[0106] Among them, f ri is a 0-1 decision variable, determining the flight segment i∈I r Whether the first port is set as the initial port of route r∈R, 0 if yes, 1 if no, N r is the number of flight segments of route r∈R, R is the set of routes r;

[0107] Port stop time constraints:

[0108]

[0109] in, The ship on the route r∈R is in the segment i∈I r Port call time at the first port, They represent the ship on route r∈R in segment i∈I r Arrival and departure times of the first port, f ri is a 0-1 decision variable, determining the flight segment i∈I r Whether the first port is set as the initial port of route r∈R, 0 if yes, 1 if no, R is the set of routes r, I r represents the set of flight segments i on route r∈R;

[0110] The time constraints for deploying ships require:

[0111]

[0112] Where R is the set of routes r, I r represents the set of segments i on route r∈R, They represent the flight segments i∈I of route r∈R respectively r The flight distance of ECA and NECA, They represent the flight segments i∈I of route r∈R respectively r The speed of ECA and NECA, The ship on the route r∈R is in the segment i∈I r Port call time at the first port, m r represents the number of liner ships deployed on route r;

[0113] Speed ​​constraints for liner ships:

[0114]

[0115] in, They represent the minimum and maximum speeds of ships on route r∈R, They represent the flight segments i∈I of route r∈R respectively r The speed of ECA and NECA, R is the set of routes r, I r represents the set of flight segments i on route r∈R;

[0116] Port visit frequency constraint requirements:

[0117]

[0118] in, They represent the ship on route r∈R in segment i∈I r The arrival and departure weeks of the first port, They represent the ship on route r∈R in segment i∈I r The arrival and departure times of the first port, R is the set of routes r, I r represents the set of flight segments i on route r∈R;

[0119] The adjustment setting constraints of the transit time window are:

[0120]

[0121] Where H is the set of backward segments h of route r∈R after transshipment operation, L is the set of backward segments l of route r∈R after transshipment operation to route s∈R, l={(s, j)│(r, i)}, j∈I s , i∈I r , and r, s∈R, r≠s, R is the set of routes r, I r represents the set of segments i on route r∈R, I s represents the set of flight segments j on route s∈R, represents the departure time of the liner ship at the first port of the voyage segment h∈H, represents the arrival time of the liner ship at the first port of the voyage segment l∈L, represents the number of weeks that the liner ship leaves the port at the first end of the route h∈H, T represents the number of weeks that a liner ship arrives at the first port of the route l∈L, min 、T max Respectively represent the upper and lower limits of the transit time window, where T max =24h and T min =8h, is a 0-1 decision variable, which determines whether the departure time of the next liner ship at the transshipment port that meets the transshipment time window requirement is in the current week during the transshipment operation. If yes, it is 0, otherwise it is 1. The value of includes one of the following: or β h is a 0-1 decision variable, which determines whether the arrival time of the liner ship at the transshipment port meets the transshipment time window requirement and its visit time is between [0, 168]. If yes, it is 0, otherwise, it is 1.

[0122] Decision constraints for transfer operations:

[0123]

[0124] in, represents the departure time of the liner ship at the first port of the voyage segment h∈H, represents the arrival time of the liner ship at the first port of the voyage segment l∈L, represents the number of weeks that the liner ship leaves the port at the first end of the route h∈H, represents the number of weeks that a liner ship arrives at the first port of the route l∈L, λ h is a 0-1 decision variable, which determines whether the liner ship completes the transfer within the transfer time window during the transfer operation. If yes, it is 1, otherwise it is 0. h is a 0-1 decision variable, which determines whether the arrival time of the liner ship at the transshipment port meets the transshipment time window requirement and its visit time is between [0, 168]. If yes, it is 0, otherwise, it is 1. is a 0-1 decision variable, which determines whether the departure time of the next liner ship at the transshipment port that meets the transshipment time window requirement is in the current week during the transshipment operation. If yes, it is 0, otherwise it is 1. The value of includes one of the following: or T min 、T max Respectively represent the upper and lower limits of the transit time window, where T max=24h and T min =8h. The constraints of the model include the setting constraints of the initial port, the port berthing time constraints, the available time constraints of the deployed ships, the speed constraints of the liner ships, the port visit frequency constraints, the adjustment setting constraints of the transshipment time window and the judgment constraints of the transshipment operation.

[0125] like Figure 3 As shown, according to an embodiment of the liner shipping navigation decision method proposed in the present application, the fuel consumption cost in the total objective function is represented by the sum of multiple piecewise linear functions according to the ε outer approximation estimation method, which specifically includes the following steps:

[0126] Step S302: introducing a substitute variable to perform linearization processing on the nonlinear term;

[0127] Step S304: Approximately representing the fuel consumption cost in the objective function by the sum of multiple piecewise linear functions;

[0128] Step S306: introducing variables to transform the nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network into a second nonlinear mixed integer programming MINLP model.

[0129] In this embodiment, according to the ε outer approximation estimation method, the fuel consumption cost in the total objective function is represented by the sum of multiple piecewise linear functions, specifically, firstly, a substitution variable is introduced to linearize the nonlinear term. Then, the fuel consumption cost in the objective function is approximately represented by the sum of multiple piecewise linear functions. Finally, a variable is introduced to transform the nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network into a second nonlinear mixed integer programming MINLP model.

[0130] like Figure 4 As shown, according to a liner shipping navigation decision method according to an embodiment of the present application, the initial port combination configuration is adjusted and optimized by a backtracking algorithm and / or an enumeration method to obtain an initial port combination, and the following steps are also included:

[0131] Step S402: Generate an initial port combination of a large-scale shipping network by a backtracking algorithm; and / or obtain an initial port combination of a medium-sized and smaller-scale shipping network by an enumeration method.

[0132] In this embodiment, the initial port combination configuration is adjusted and optimized by a backtracking algorithm and / or an enumeration method to obtain an initial port combination, specifically, the initial port combination of a large-scale shipping network is generated by a backtracking algorithm. And / or the initial port combination of a medium-sized and smaller-scale shipping network is obtained by an enumeration method. The initial port combination of a large-scale shipping network is generated by a backtracking algorithm, which can solve the problem of low efficiency that may be faced by the enumeration method in solving the initial port combination of a large-scale instance.

[0133] like Figure 5 As shown, the embodiment of the second aspect of the present application provides a liner shipping navigation decision system 10, including: a network construction module 110, which is used to obtain route data, port locations and ECA area divisions, and construct a liner shipping service network total operating cost minimization model based on the route data, port locations and ECA area divisions to generate a total objective function. The liner shipping service network total operating cost minimization model includes a nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network; a fuel consumption cost calculation module 120, which is used to estimate the fuel consumption cost based on the speed variables inside and outside the ECA area according to the ε outer approximation. The fuel consumption cost in the total objective function is represented by the sum of multiple piecewise linear functions; the initial port combination generation module 130 is used to adjust and optimize the initial port combination configuration through a backtracking algorithm and / or an enumeration method to obtain an initial port combination; the liner shipping decision optimization module 140 is used to calculate the total operating cost of the liner shipping service network based on the total operating cost minimization model of the liner shipping service network, the fuel consumption cost and the initial port combination, and compare to obtain the optimal solution of the navigation decision under the goal of minimizing the total operating cost, and the optimal solution of the navigation decision includes the optimal solution of ship deployment, the optimal solution of ship schedule design and the optimal solution of speed optimization decision.

[0134] The liner shipping navigation decision system 10 provided in this embodiment includes a network construction module 110, a fuel consumption cost calculation module 120, an initial port combination generation module 130 and a liner shipping decision optimization module 140. Among them, the network construction module 110 is used to obtain route data, port locations and ECA regional divisions, and construct a total operating cost minimization model for the liner shipping service network based on the route data, port locations and ECA regional divisions to generate a total objective function. The total operating cost minimization model for the liner shipping service network includes a nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network. The fuel consumption cost calculation module 120 is used to represent the fuel consumption cost in the total objective function by the sum of multiple piecewise linear functions based on the speed variables inside and outside the ECA region according to the ε outer approximation estimation method. The initial port combination generation module 130 is used to adjust and optimize the initial port combination configuration through a backtracking algorithm and / or an enumeration method to obtain an initial port combination. The liner shipping decision optimization module 140 is used to calculate the total operating cost of the liner shipping service network based on the total operating cost minimization model of the liner shipping service network, the fuel consumption cost and the initial port combination, and compare to obtain the optimal solution of the navigation decision under the goal of minimizing the total operating cost. The optimal solution of the navigation decision includes the optimal solution of ship deployment, the optimal solution of ship schedule design and the optimal solution of speed optimization decision. The liner shipping navigation decision system provided by the present application effectively reduces the total operating cost by integrating the three major operational decisions of ship deployment, ship schedule design and speed optimization. Compared with the prior art, it significantly improves the feasibility when dealing with complex strategic plans and large-scale problems, avoids the limitations of traditional methods that are difficult to achieve due to the large scale of the problem, and reveals the fine-tuning strategy for the existing network operation decision. By optimizing the initial port combination and speed configuration, the liner company enhances the ability to respond to market changes and improves the competitiveness of the enterprise. Speed ​​optimization not only reduces the fuel consumption cost, but also further realizes the saving of operating costs by moderately relaxing the transit time window limit. When responding to fuel price fluctuations, speed optimization effectively alleviates the cost pressure by preferentially deploying ships on routes with longer voyages and reducing the speed.

[0135] like Figure 6 As shown, an embodiment of the third aspect of the present application provides a liner shipping navigation decision system 20, including: a memory 300 and a processor 400, wherein the memory 300 stores a program or instruction that can be run on the processor 400, and when the processor 400 executes the program or instruction, the steps of the liner shipping navigation decision method of any one of the embodiments of the first aspect are implemented, so it has the technical effect of any one of the embodiments of the first aspect mentioned above, which will not be repeated here.

[0136] The embodiment of the fourth aspect of the present application provides a readable storage medium on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the liner shipping navigation decision method of any one of the embodiments of the first aspect are implemented, so it has the technical effect of any one of the embodiments of the first aspect mentioned above, and will not be repeated here.

[0137] like Figure 7 , Figure 8 As shown in Figures 9(a), 9(b), 9(c), 10(a), 10(b), 10(c), 11(a), 11(b), and 11(c), according to a liner shipping navigation decision-making method of a specific embodiment provided in the present application, the best operation plan is formulated by optimizing the deployment of liner ships, ship schedule design, speed adjustment, and integration of transshipment functions.

[0138] 1. Ship deployment: How many liner ships are deployed on each fixed route. Under a fixed frequency of ship dispatch, this decision represents the cost of ship deployment, which directly affects the average speed of the liner fleet on each route, and thus has two associated effects. First, the average speed determines the amount of fuel consumption and the cost of fuel consumption. Second, it affects the voyage time of the liner ship on the route.

[0139] 2. Schedule design: It determines the voyage time of all legs on the route. In other words, the schedule design allocates the total voyage time (including sea time and port call time) of a group of routes to each branch route. The optimal allocation depends on the sensitivity of the ship deployment cost, fuel consumption cost and possible transshipment penalty cost between different branch routes to the voyage time.

[0140] 3. Speed ​​Optimization: The relationship between the fuel consumption of a segment and the voyage time of that segment can be obtained through this decision. Therefore, for all the segments of each given route, it is necessary to find the speed at which the liner ship performs fuel switching in and out of the ECA.

[0141] Specifically solve the following technical problems:

[0142] 1. Micro-adjustment of existing network operation decisions. A new nonlinear mixed integer programming MINLP model is established, which enables liner companies to start from the easy-to-operate speed optimization, taking into account the impact of ECA regulations and transit time windows, and reshaping the original liner shipping service network by determining the variable initial port combination and the necessary increase in the number of liner ships.

[0143] 2. Development of a two-stage algorithm. For the proposed MINLP model, some linearization techniques are applied to develop an effective two-stage algorithm based on the ε outer approximation estimation method and the enumeration method. In order to solve the problem of low efficiency of the enumeration method in solving the initial port combination of large-scale instances, a backtracking algorithm is provided. The results show that the solution method can obtain the optimal solution in the liner business case. Similarly, it can also be applied to the liner shipping business.

[0144] 3. Decision adjustment and scenario forecasting. By analyzing the impact of the Capacitated Time Window (CTW) and the price difference of different fuels on ship deployment, ship schedule design and speed optimization, we can deepen our understanding of the relationship between different operational decisions and their impact on saving operating costs, and provide preliminary judgments for responding to the volatile fuel market.

[0145] Through this embodiment, the liner shipping service network can be optimized with lower operating costs and higher efficiency under compliance requirements, providing a competitive operating solution.

[0146] Specifically, the liner shipping navigation decision-making method includes:

[0147] S1: Construct a nonlinear mixed integer programming MINLP model to minimize the total operating cost of the liner shipping service network and generate the total objective function;

[0148] S2: Using the ε outer approximation estimation method, the fuel consumption cost in the objective function is approximated by the sum of many piecewise linear functions;

[0149] S3: A backtracking algorithm is used to generate all possible initial port combinations for large-scale shipping networks. For medium and smaller-scale instances, possible initial port combinations for all routes are enumerated to obtain the optimal solution for ship deployment, ship schedule design, and speed optimization decision-making under the goal of minimizing total operating costs.

[0150] In step S1, a nonlinear mixed integer programming MINLP model is established. Starting from the optimization of shipping speed, the influence of ECA regulation and transit time window is considered. By determining the variable initial port combination and the necessary increase in the number of liner ships, the original liner shipping service network is reshaped.

[0151] Furthermore, the model objective function formula is:

[0152] minC=C V +C P +C F (1);

[0153] Where C is the total objective function value, expressed as the total operating cost of the liner shipping service network, C V is the ship deployment cost, CP is the transshipment penalty cost, and the fuel consumption cost C F ;

[0154] Furthermore, the ship deployment cost is calculated by formula (2):

[0155]

[0156] Where R is the set of routes r, m r represents the number of liner ships deployed on route r, represents the weekly vessel deployment cost (capital cost and operating cost) on route r, Represents any route r in the set R;

[0157] Furthermore, the transfer penalty cost is calculated by formula (3):

[0158]

[0159] Where H is the set of backward segments h of route r∈R after transshipment operation; λ h It is a 0-1 decision variable, which determines whether the liner ship completes the handover within the CTW during the transshipment operation. If yes, it is 1, otherwise, it is 0; represents the transshipment penalty cost incurred by the liner ship for the backward leg h when it fails to perform transshipment operations within the CTW;

[0160] Furthermore, the fuel consumption cost is calculated by formula (4):

[0161]

[0162] Where P E , P N Respectively represent the fuel prices used by liner ships in ECA and NECA; ∈I r , I r represents the set of segments i on route r∈R, They represent the ECA and NECA distances on the segment i∈Ir of route r∈R respectively; Q(v 0 ) represents the fuel consumption rate at the design speed of the liner ship; They represent the flight segments i∈I of route r∈R respectively r The speed of ECA and NECA, v represents the speed of the ship, and v 0 represents the design speed of the liner ship; θ represents the power exponent of the fuel consumption rate with respect to the speed function;

[0163] Furthermore, the constraints of the model are:

[0164] Initial port settings constraints:

[0165]

[0166] In the formula, f ri is a 0-1 decision variable, determining the flight segment i∈I r Whether the first port is set as the initial port of route r∈R, if yes, it is 0, otherwise, it is 1; N r is the number of flight segments of route r∈R;

[0167] Port stop time constraints:

[0168]

[0169] In the formula, The ship on the route r∈R is in the segment i∈I r Port call time at the first port, They represent the ship on route r∈R in segment i∈I r Arrival and departure times of the first port;

[0170] The time constraints for deploying ships require:

[0171]

[0172] Speed ​​constraints for liner ships:

[0173]

[0174] In the formula, They represent the minimum and maximum speeds of ships on route r∈R respectively;

[0175] Port visit frequency constraint requirements:

[0176]

[0177] In the formula, They represent the ship on route r∈R in segment i∈I r The arrival and departure weeks of the first port;

[0178] CTW adjustment setting constraints:

[0179]

[0180] Where L is the set of backward segments l from route r∈R to route s∈R after transshipment operation, l={(s, j)│(r, i)}, j∈I s , i∈I r , and r, s∈R, r≠s; T min 、T maxRepresent the upper and lower bounds of CTW respectively, where T max =24h and T min =8h; is a 0-1 decision variable, which determines whether the departure time of the next liner ship at the transshipment port that meets the CTW requirement is in the current week during the transshipment operation. If yes, it is 0, otherwise it is 1. There are three possible values: (i) (ii) (iii) β h is a 0-1 decision variable, which determines whether the arrival time of the liner ship at the transshipment port meets the CTW requirements and its visit time is between [0, 168]. If yes, it is 0, otherwise it is 1. s represents the set of flight segments j on route s∈R, represents the departure time of the container ship at the first port of the voyage segment h∈H, represents the arrival time of the container ship at the first port of the voyage segment l∈L, represents the number of weeks that the container ship leaves the port at the first end of the voyage segment h∈H, It represents the arrival weeks of container ships at the first port of the voyage segment l∈L;

[0181] Decision constraints for transfer operations:

[0182]

[0183] In the formula, λ h It is a 0-1 decision variable, which determines whether the liner ship completes the transfer within the CTW during the transshipment operation. If yes, it is 1, otherwise it is 0;

[0184] In step S1, the nonlinear mixed integer programming MINLP model established is:

[0185]

[0186] Where P E , P N Respectively represent the fuel prices used by liner ships in ECA and NECA;

[0187] In step S2, the steps of using the ε outer approximation estimation method include:

[0188] S21: Introducing substitution variables Linearize nonlinear terms;

[0189] S22: Approximate the fuel consumption cost in the objective function by the sum of many piecewise linear functions;

[0190] S23: Introduce variables and transform model [M1] into model [M2];

[0191] In step S21, the substitution variable is introduced The formula for linearizing nonlinear terms is:

[0192]

[0193] The constraints (8)-(10) are changed to:

[0194]

[0195] Fuel consumption cost C V to:

[0196]

[0197] The objective function of model [M1] is changed to:

[0198]

[0199] In step S22, the linear static outer approximation estimation method is used to process the fuel consumption rate, including:

[0200] S221: predetermine a suitable tolerance level ε to control the error of fuel consumption (t) at different speeds;

[0201] S222: For the actual fuel consumption rate When k = 1, choose right Taking the derivative generates a tangent line;

[0202] S223: The tangent line and the actual fuel consumption rate do not exceed the error range For the interval exceeding the error exist Regenerate a tangent line on the

[0203] S224: Repeat S223 until Satisfy the error, and K tangent outer approximations are generated. The true fuel consumption rate can be approximated by this set of tangent lines;

[0204] S225: transforming model [M1] into model [M2];

[0205] In step S222, the formula is:

[0206]

[0207] In the formula, and intercept1 Respectively represent tangent The slope and intercept of

[0208] In step S223, the formula is:

[0209]

[0210] In step S224, the formula is:

[0211]

[0212] In step S224, the formula includes:

[0213]

[0214] In the formula, the auxiliary variable is introduced

[0215] Further, model [M1] is transformed into model [M2], the formula is:

[0216]

[0217] In step S3, the backtracking algorithm is used to generate all possible initial port combinations for large-scale shipping networks. For medium and small-scale instances, all possible initial port combinations for all routes are enumerated to obtain the optimal solution for ship deployment, ship schedule design, and speed optimization decision under the goal of minimizing the total operating cost. The solution includes:

[0218] S31: Use a backtracking algorithm to generate all possible initial port combinations for a large-scale shipping network;

[0219] S32: For medium and smaller scale instances, enumerate all possible initial port combinations for all routes (f 1i ,f 2i ,...,f Ri )∈{1,2,...,I 1}×{1,2,...,I 2}×...×{1,2,...,I R};

[0220] S33: By comparing the total operating cost C of the smallest liner shipping service network * (f 1i ,f 2i ,...,f Ri ) obtain the optimal solution of model [M2];

[0221] This embodiment also provides a liner shipping navigation decision system, which implements the liner shipping navigation decision method described above. The system includes a network construction module, a fuel consumption cost calculation module, an initial port combination generation module, a liner shipping decision optimization module and a visualization module:

[0222] A network construction module is used to construct a nonlinear mixed integer programming MINLP model to minimize the total operating cost of the liner shipping service network based on route data, port locations and ECA area divisions, and generate an overall objective function;

[0223] Fuel consumption cost calculation module, which is used to calculate the fuel consumption cost based on the speed variables inside and outside the ECA area. It uses the ε outer approximation estimation method to approximate the fuel consumption cost in the objective function through the sum of many piecewise linear functions;

[0224] An initial port combination generation module, which is used to adjust and optimize the initial port combination configuration through a backtracking algorithm and / or an enumeration method to obtain an initial port combination;

[0225] Liner shipping decision optimization module, which calculates the total operating cost of the liner shipping service network based on the total operating cost minimization model of the liner shipping service network, fuel consumption cost and initial port combination, and compares to obtain the optimal solution of navigation decision under the goal of minimizing the total operating cost. The optimal solution of navigation decision includes the optimal solution of ship deployment, the optimal solution of ship schedule design and the optimal solution of speed optimization decision;

[0226] The visualization module is used to feed back decisions such as the number of deployed ships, initial port settings, ship schedule design, and transshipment operations into the shipping network for visualization, so as to obtain the shipping network corresponding to the optimal liner shipping decision.

[0227] This embodiment studies the fuel conversion adopted by liner ships under ECA regulation, and how to achieve flexible design of liner shipping service network by optimizing operational decisions to meet a wide range of freight needs and cope with the changing fuel market. In summary, the beneficial effects of the embodiments of this application are:

[0228] 1. Comprehensive decision optimization: The newly established MINLP model effectively reduces the total operating cost by integrating the three major operational decisions of ship deployment, ship schedule design and speed optimization. This method significantly improves the feasibility of dealing with complex strategic plans and large-scale problems compared with existing technologies, and avoids the limitations of traditional methods that are difficult to implement due to the large scale of the problem.

[0229] 2. Flexible network design: By optimizing the initial port combination and speed configuration, liner companies can respond more flexibly to changing market demands, enabling enterprises to quickly adapt to market changes and improve their competitiveness.

[0230] 3. Cost saving potential: Research shows that speed optimization not only reduces fuel consumption costs, but also further saves operating costs by moderately relaxing CTW restrictions. Specifically, driven by speed optimization, it is expected that total operating cost savings of approximately 1.80% to 4.85% can be achieved, providing practical support for enterprises in profit management.

[0231] 4. Effective strategies for coping with market fluctuations: This embodiment also emphasizes the strategic significance of speed optimization in coping with fuel price fluctuations, especially in periods of high fuel prices. By prioritizing the deployment of ships on longer routes and reducing speed, cost pressure can be effectively alleviated. This strategy can help companies maintain stable operating performance in an uncertain market environment.

[0232] 5. Foresight and adaptability: With the emergence of new ECAs and diversified regulations on ship exhaust emission standards around the world in the future, the adoption of more types of emission reduction technologies to ensure the compliance of several major types of transportation businesses such as tanker transportation, bulk transportation, and liner transportation is a realistic issue that can be continuously explored. The plan takes into account the diversified ECAs and their regulatory changes that may appear in the future, and emphasizes the necessity of continuously exploring emission reduction technologies and operational management strategies. This provides ship operators with ideas for responding to future challenges and ensuring compliance and competitiveness in the context of increasingly stringent environmental regulations.

[0233] In this application, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance; the term "plurality" refers to two or more, unless otherwise expressly defined. Terms such as "installed", "connected", "connected", and "fixed" should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; "connected" can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0234] In the description of the present application, it should be understood that the terms "upper", "lower", "front", "rear", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or module referred to must have a specific direction, be constructed and operated in a specific orientation, and therefore, should not be understood as a limitation on the present application.

[0235] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0236] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A liner shipping navigation decision method, characterized in that: include: Acquire route data, port locations and ECA area divisions, construct a total operating cost minimization model for a liner shipping service network based on the route data, the port locations and the ECA area divisions, and generate a total objective function, wherein the total operating cost minimization model for a liner shipping service network includes a nonlinear mixed integer programming MINLP model for minimizing the total operating cost of a liner shipping service network; Based on the speed variables inside and outside the ECA area, according to the ε outer approximation estimation method, the fuel consumption cost in the total objective function is represented by the sum of multiple piecewise linear functions; Adjust and optimize the initial port combination configuration by backtracking algorithm and / or enumeration method to obtain the initial port combination; The total operating cost of the liner shipping service network is calculated based on the total operating cost minimization model of the liner shipping service network, the fuel consumption cost and the initial port combination, and the optimal solution of the navigation decision under the goal of minimizing the total operating cost is obtained by comparison. The optimal solution of the navigation decision includes the optimal solution of ship deployment, the optimal solution of ship schedule design and the optimal solution of speed optimization decision.

2. The liner shipping navigation decision method according to claim 1, characterized in that: Also includes: The optimal solution of ship deployment, the optimal solution of ship schedule design, the optimal solution of speed optimization decision and the initial port setting are fed back to the shipping network for visualization processing to obtain the shipping network corresponding to the optimal liner transportation decision.

3. The liner shipping navigation decision method according to claim 1, characterized in that: The formula of the overall objective function is: minC=C V +C P +C F ; Where C is the total operating cost of the liner shipping service network, C V is the ship deployment cost, C P is the transfer penalty cost, C F is the fuel consumption cost, and min is the minimum value.

4. The liner shipping navigation decision method according to claim 3, characterized in that: The calculation formula of the ship deployment cost is: Where R is the set of routes r, m r represents the number of liner ships deployed on route r, C r V represents the weekly ship deployment cost on route r; The calculation formula of the transshipment penalty cost is: Where H is the set of backward segments h of route r∈R after transshipment operation, λ h It is a 0-1 decision variable, which determines whether the liner ship completes the handover within the CTW during the transshipment operation. If yes, it is 1, otherwise it is 0. represents the transshipment penalty cost incurred by the liner ship for the backward leg h when it fails to perform transshipment operations within the CTW; The calculation formula of the fuel consumption cost is: Among them, P E , P N Respectively represent the fuel prices used by liner ships in ECA and NECA, ∈I r , I r represents the set of segments i on route r∈R, They represent the flight segments i∈I of route r∈R respectively r The ECA and NECA are the voyage distances, Q(v0) represents the fuel consumption rate at the design speed of the liner ship, They represent the flight segments i∈I of route r∈R respectively r where v is the speed of the ship, v0 is the design speed of the liner ship, θ is the power exponent of the fuel consumption rate with respect to the speed function, and Q(v) is the fuel consumption rate.

5. The liner shipping navigation decision method according to claim 4, characterized in that: The constraints of the overall objective function include: Initial port settings constraints: Among them, f ri is a 0-1 decision variable, determining the flight segment i∈I r Whether the first port is set as the initial port of route r∈R, 0 if yes, 1 if no, N r is the number of flight segments of route r∈R, R is the set of routes r; Port stop time constraints: in, The ship on the route r∈R is in the segment i∈I r Port call time at the first port, They represent the ship on route r∈R in segment i∈I r Arrival and departure times of the first port, f ri is a 0-1 decision variable, determining the flight segment i∈I r Whether the first port is set as the initial port of route r∈R, 0 if yes, 1 if no, R is the set of routes r, I r represents the set of flight segments i on route r∈R; The available time constraints for deploying ships require: Where R is the set of routes r, I r represents the set of segments i on route r∈R, They represent the flight segments i∈I of route r∈R respectively r The flight distance of ECA and NECA, They represent the flight segments i∈I of route r∈R respectively r The speed of ECA and NECA, The ship on the route r∈R is in the segment i∈I r Port call time at the first port, m r represents the number of liner ships deployed on route r; Speed ​​constraints for liner ships: in, They represent the minimum and maximum speeds of ships on route r∈R, They represent the flight segments i∈I of route r∈R respectively r The speed of ECA and NECA, R is the set of routes r, I r represents the set of flight segments i on route r∈R; Port visit frequency constraint requirements: in, They represent the ship on route r∈R in segment i∈I r The arrival and departure weeks of the first port, They represent the ship on route r∈R in segment i∈I r The arrival and departure times of the first port, R is the set of routes r, I r represents the set of flight segments i on route r∈R; The adjustment setting constraints of the transit time window are: Where H is the set of backward segments h of route r∈R after transshipment operation, L is the set of backward segments l of route r∈R after transshipment operation to route s∈R, l={(s, j)│(r, i)}, j∈I s , i∈I r , and r, s∈R, r≠s, R is the set of routes r, I r represents the set of segments i on route r∈R, I s represents the set of flight segments j on route s∈R, represents the departure time of the liner ship at the first port of the voyage segment h∈H, represents the arrival time of the liner ship at the first port of the voyage segment l∈L, represents the number of weeks that the liner ship leaves the port at the first end of the route h∈H, T represents the number of weeks that a liner ship arrives at the first port of the route l∈L, min , T max Respectively represent the upper and lower limits of the transit time window, where T max =24h and T min =8h, is a 0-1 decision variable, which determines whether the departure time of the next liner ship at the transshipment port that meets the transshipment time window requirement is in the current week during the transshipment operation. If yes, it is 0, otherwise it is 1. The value of includes one of the following: or β h is a 0-1 decision variable, which determines whether the arrival time of the liner ship at the transshipment port meets the transshipment time window requirement and its visit time is between [0, 168]. If yes, it is 0, otherwise, it is 1. Decision constraints for transfer operations: in, represents the departure time of the liner ship at the first port of the voyage segment h∈H, represents the arrival time of the liner ship at the first port of the voyage segment l∈L, represents the number of weeks that the liner ship leaves the port at the first end of the route h∈H, represents the number of weeks that a liner ship arrives at the first port of the route l∈L, λ h is a 0-1 decision variable, which determines whether the liner ship completes the transfer within the transfer time window during the transfer operation. If yes, it is 1, otherwise it is 0. h is a 0-1 decision variable, which determines whether the arrival time of the liner ship at the transshipment port meets the transshipment time window requirement and its visit time is between [0, 168]. If yes, it is 0, otherwise, it is 1. is a 0-1 decision variable, which determines whether the departure time of the next liner ship at the transshipment port that meets the transshipment time window requirement is in the current week during the transshipment operation. If yes, it is 0, otherwise it is 1. The value of includes one of the following: or T min , T max Respectively represent the upper and lower limits of the transit time window, where T max =24h and T min =8h.

6. The liner shipping navigation decision method according to any one of claims 1 to 5, characterized in that: The method of using the ε outer approximation estimation method to represent the fuel consumption cost in the total objective function by the sum of multiple piecewise linear functions includes: Introduce substitution variables to linearize nonlinear terms; The fuel consumption cost in the objective function is approximated by the sum of multiple piecewise linear functions; Variables are introduced to transform the nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network into a second nonlinear mixed integer programming MINLP model.

7. The liner shipping navigation decision method according to any one of claims 1 to 5, characterized in that: The initial port combination configuration is adjusted and optimized by backtracking algorithm and / or enumeration method to obtain the initial port combination, including: Generate an initial port portfolio for a large-scale shipping network through a backtracking algorithm; and / or The initial port combination of medium and smaller-scale shipping networks is obtained by enumeration method.

8. A liner shipping navigation decision system, characterized in that: include: A network construction module is used to obtain route data, port locations and ECA area divisions, construct a liner shipping service network total operating cost minimization model based on the route data, the port locations and the ECA area divisions, and generate a total objective function, wherein the liner shipping service network total operating cost minimization model includes a nonlinear mixed integer programming MINLP model for minimizing the total operating cost of the liner shipping service network; A fuel consumption cost calculation module, used for expressing the fuel consumption cost in the total objective function by the sum of multiple piecewise linear functions based on the speed variables inside and outside the ECA area and according to the ε outer approximation estimation method; An initial port combination generation module is used to adjust and optimize the initial port combination configuration through a backtracking algorithm and / or an enumeration method to obtain an initial port combination; The liner shipping decision optimization module is used to calculate the total operating cost of the liner shipping service network based on the total operating cost minimization model of the liner shipping service network, the fuel consumption cost and the initial port combination, and compare to obtain the optimal solution of the navigation decision under the goal of minimizing the total operating cost. The optimal solution of the navigation decision includes the optimal solution of ship deployment, the optimal solution of ship schedule design and the optimal solution of speed optimization decision.

9. A liner shipping navigation decision system, characterized in that: include: A memory and a processor, wherein the memory stores a program or instruction that can be run on the processor, and when the processor executes the program or the instruction, the steps of the liner shipping navigation decision method as described in any one of claims 1 to 7 are implemented.

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

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