A liner alliance resource scheduling method, device, electronic device and storage medium

Abstract

The present application discloses a liner alliance resource scheduling method, device, electronic device and storage medium, which relates to the field of resource scheduling technology. The method includes: constructing a random programming model for liner alliance resource scheduling; calculating the expected revenue of the container to be scheduled as gradient information based on the random programming model and the liner schedule; constructing a mixed integer linear programming subproblem based on the gradient information and solving it to obtain the subproblem solution result; determining the container demand scenario, and solving the deterministic problem of the demand scenario based on the subproblem solution result to obtain a deterministic result; updating the gradient information based on the dual value of the deterministic result; returning to constructing the mixed integer linear programming subproblem based on the gradient information and solving it to obtain the subproblem solution result, until the iteration end condition is reached, and the final subproblem solution result is obtained; and scheduling each container based on the final subproblem solution result. The present application can improve the utilization efficiency of containers and cabin space.

Description

Technical Field

[0001] The present application relates to the technical field of resource scheduling, and in particular to a liner alliance resource scheduling method, device, electronic device and storage medium. Background Art

[0002] Liner alliances are the mainstream form of cooperation in the current shipping market. In regular liner services, since it is impossible to obtain information on all transportation needs in advance, liner companies need to prepare empty containers at the port in advance and reserve space on relevant routes. In the context of liner alliances, alliance members can coordinate and share containers and space to more flexibly dispatch resources and expand the scope of services, but the utilization rate of related technologies for coordinating and dispatching containers and space of liner alliances is low. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a liner alliance resource scheduling method, device, electronic device and storage medium to improve the utilization rate of containers and cabin space.

[0004] To achieve the above-mentioned purpose, an embodiment of the present application provides a liner alliance resource scheduling method in one aspect, the method comprising the following steps:

[0005] Construct a stochastic programming model for liner alliance resource scheduling;

[0006] Calculating the expected revenue of the container to be scheduled as gradient information according to the stochastic programming model and the liner schedule;

[0007] Constructing a mixed integer linear programming subproblem according to the gradient information and solving it to obtain a solution result of the subproblem;

[0008] Determine the demand scenario of the container, and solve the deterministic problem of the demand scenario according to the solution results of the sub-problems to obtain a deterministic result;

[0009] updating the gradient information according to the dual value of the deterministic result;

[0010] Returning to constructing the mixed integer linear programming subproblem according to the gradient information and solving the subproblem to obtain the subproblem solution result, until the iteration end condition is reached, and obtaining the final subproblem solution result;

[0011] The containers are dispatched according to the final solution results of the sub-problems.

[0012] In some embodiments, the step of constructing a stochastic programming model for liner alliance resource scheduling includes the following steps:

[0013] Constructing a space-time network based on the various routes of the liner alliance and the liner schedules;

[0014] A random programming model is constructed according to each of the containers, the number of slots at ports on each of the routes, and the space-time network; wherein the containers include loaded containers and empty containers.

[0015] In some embodiments, constructing a stochastic programming model based on each of the containers, the number of slots at ports on each of the routes, and the spatiotemporal network comprises the following steps:

[0016] The constraints of the heavy containers, the number of cabin spaces, the number of empty containers, the container constraints and the balance constraints of the empty containers are constructed and the stochastic programming model is constructed.

[0017] In some embodiments, constructing the constraint problem of the heavy container, the constraint problem of the number of cabin spaces, the constraint problem of the number of empty containers, the constraint problem of the container, and the balance constraint problem of the empty containers and constructing the stochastic programming model comprises the following steps:

[0018] The constraint problem for constructing the heavy box is:

[0019] ;

[0020] The constraint problem of the number of cabin spaces is constructed as follows:

[0021] ;

[0022] ;

[0023] The problem of constructing the number constraint of the empty boxes is:

[0024] ;

[0025] The constraints for constructing the container are:

[0026] ;

[0027] ;

[0028] ;

[0029] The equilibrium constraint problem of the empty box is constructed as follows:

[0030] ;

[0031] ;

[0032] The stochastic programming model is constructed as:

[0033] ;

[0034] The stochastic programming model is subject to each of the constraint problems and is subject to the following relationship:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] in, is a set of time nodes, and is the parameter representing the time node; is the time set of route r arriving at port i within the planning time; For route collection, and To represent the parameters of the route; A collection of companies for liner alliances, To represent the parameters of the liner company; is the route set of company C in the liner alliance; is the set of ports included in route r, is the parameter representing the port; The set of transport port pairs included in the route r. The set elements are used express; A collection of all ports that provide shipping services for company C; is the set of flight segments contained in route r, and m is the parameter representing the flight segment; is the set of corresponding liner company route numbers and route arrival times when the alliance partner's capacity is borrowed to transport the freight demand from port i to port j on route r at time t; is the number of passengers still on board at time t on route r. The set of transport tasks that are loaded at port i and shipped to port j at time, including the start time of the transport task and the transport port pair; is the starting port code and corresponding loading time set of the empty container unloaded at port i on route r at time t; is the starting port code and corresponding loading time set of the loaded container returned as an empty container to port i at time t on route r; is the unit cost of transporting a loaded container from port i to port j on route r at time t; is the unit cost of transporting an empty container from port i to port j on route r at time t; is the unit storage cost of Company C storing empty containers at Port I; is the sailing time from port i to port j on route r, departing at time t, used to calculate , ; is the average return time of empty containers; To maintain the maximum capacity of the daily operation of route r; is the initial empty container reserve of company C at port i; The company C to which the route R belongs; represents the random freight demand from port i to port j generated on route r at time t; It means judging whether the voyage from port i to port j at time t on route r contains segment m. If so, is 1, otherwise is 0; It means judging whether there is a voyage from port i to port j at time t on route r. If so, is 1, otherwise is 0; Indicates the use of the route to determine r Is it a feasible means of transport to transport the cargo from port i to port j at time t? 1 otherwise is 0; is the number of containers loaded at time t on route r and shipped from port i to port j; is the number of empty containers loaded at time t on route r and transferred from port i to port j; For r route use The space of the route is loaded at time t, and the number of loaded containers shipped from port i to port j; Charter for R route The space of the route is loaded at time t, and the number of empty containers allocated from port i to port j; The number of loaded containers shipped from port i to port j at time t to fulfill the demand for leasing empty containers from company c on route r; To rent empty containers from company C to fulfill the requirements of route R, use The space of the route, loaded at time t, and the number of loaded containers shipped from port i to port j; is the number of empty containers that company c chooses to keep at port i at time t; is the number of routes r in the planning period T. The number of spaces rented on route segment m; It represents the total number of deployed empty containers unloaded at port i and the total number of empty containers returned after completing the previous tasks for all routes of company c at time t; represents the total number of empty containers loaded at port i at time t to meet the needs of company c; It represents the total number of empty containers leased by other liner companies in the alliance at port i for transportation at time t; It represents the total number of empty containers returned to company C at port i at time t after other liner companies in the alliance have completed their transportation tasks by leasing company C's empty containers.

[0043] In some embodiments, the step of calculating the expected revenue of the container to be scheduled as gradient information according to the stochastic programming model and the liner schedule comprises the following steps:

[0044] Determining the loading time and arrival time of each container at each port according to the liner schedule; wherein the containers include loaded containers and empty containers;

[0045] Calculating the difference between the loading time and the arrival time corresponding to each container;

[0046] Calculate the transportation and storage costs of each container according to the difference;

[0047] The expected revenue of each container is calculated according to the stochastic programming model and the transportation and storage costs as the gradient information.

[0048] In some embodiments, constructing a mixed integer linear programming sub-problem according to the gradient information and solving the sub-problem to obtain a solution result of the sub-problem includes the following steps:

[0049] The mixed integer linear programming sub-problem is constructed according to the gradient information as follows:

[0050] ;

[0051] Constructing the EG-SHLA algorithm to solve the mixed integer linear programming subproblem;

[0052] The EG-SHLA algorithm constrains the mixed integer linear programming subproblem to be subject to the following relational expression:

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] = ;

[0058] , ;

[0059] , ;

[0060] in, is the parameter vector of the objective function of one stage; is the decision variable vector of one stage; is the flow balance constraint coefficient matrix of one stage; is the space capacity constraint coefficient matrix of one stage; is the coefficient vector of the right-hand side of the space capacity constraint in one stage; is the demand constraint coefficient matrix of one stage; The coefficient vector of the right-hand side of the demand constraint in the first stage; is a unit coefficient vector, that is, all elements are 1; For the general The port numbers of the ports in the unified order are a set of port numbers. A collection of all ports that provide shipping services for company C; The maximum number of empty containers that can be called upon by each liner company; Number the freight demands generated in the second stage, starting from 0 to the largest freight demand; Indicates the empty box numbers that flow in at different times in the second stage, starting from 0 to the maximum number of empty boxes; is the variable representing container transportation in one stage, which is the container that flows into node i at time t in the second stage; is the time t of the second stage. Whether the empty box flows into the i node, if so, is 1, if so is 0; is the time set when the empty box may be returned to the i-node in the second phase, ; is the number generated in the second stage at the i node. The possible supply arrival time corresponding to the demand and the corresponding empty box sequence number set used to complete the demand ( t,s ), ; (s) is the expected return of the sth box arriving at node i before time t at the kth iteration, ; The expected revenue of the container to be scheduled is calculated according to the stochastic programming model and the liner schedule as the gradient information as the first stage; the mixed integer linear programming sub-problems are constructed according to the gradient information and the sub-problem solution is obtained by solving the sub-problem as the second stage.

[0061] In some embodiments, constructing the EG-SHLA algorithm to solve the mixed integer linear programming subproblem comprises the following steps:

[0062] Adding first-order moment estimation and second-order moment estimation to the EG-SHLA algorithm to obtain a DEG-SHLA algorithm to update the gradient information;

[0063] The DEG-SHLA algorithm is used to solve the mixed integer linear programming subproblem.

[0064] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a liner alliance resource scheduling device, the device comprising:

[0065] Model building unit, used to build a stochastic programming model for liner alliance resource scheduling;

[0066] A gradient calculation unit, used to calculate the expected revenue of the container to be scheduled as gradient information according to the stochastic programming model and the liner schedule;

[0067] A planning and solving unit, used for constructing a mixed integer linear programming sub-problem according to the gradient information and solving the sub-problem to obtain a solution result;

[0068] A demand solving unit, used to determine the demand scenario of the container, and solve the deterministic problem of the demand scenario according to the solution results of the sub-problems to obtain a deterministic result;

[0069] a gradient updating unit, configured to update the gradient information according to the dual value of the deterministic result;

[0070] An iterative updating unit, used for returning the mixed integer linear programming subproblem constructed according to the gradient information and solving the subproblem to obtain a solution result of the subproblem, until an iteration end condition is reached, and a final solution result of the subproblem is obtained;

[0071] A resource scheduling unit is used to schedule each of the containers according to the final solution results of the sub-problems.

[0072] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0073] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0074] The embodiments of the present application include at least the following beneficial effects:

[0075] This application can construct a stochastic programming model for liner alliance resource scheduling; calculate the expected revenue of the container to be scheduled as gradient information based on the stochastic programming model and the liner schedule; construct a mixed integer linear programming subproblem based on the gradient information and solve it to obtain the subproblem solution result; determine the container demand scenario, and solve the deterministic problem of the demand scenario based on the subproblem solution result to obtain a deterministic result; update the gradient information based on the dual value of the deterministic result; return to construct a mixed integer linear programming subproblem based on the gradient information and solve it to obtain the subproblem solution result, until the iteration end condition is reached, and the final subproblem solution result is obtained; schedule each container based on the final subproblem solution result. This application can effectively cope with the scheduling of containers and cabin spaces under different demand scenarios, and can improve the utilization efficiency of containers and cabin spaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0077] Figure 1 An example flow chart of a liner alliance resource scheduling method provided in an embodiment of the present application;

[0078] Figure 2 A schematic diagram of a flow chart of a liner alliance resource scheduling method provided in an embodiment of the present application;

[0079] FIG. 3( a ) is an illustrative diagram of a collection provided in an embodiment of the present application;

[0080] FIG3( b ) is an illustrative diagram of parameters provided in an embodiment of the present application;

[0081] FIG3( c ) is an illustrative diagram of decision variables provided in an embodiment of the present application;

[0082] FIG3( d ) is an illustrative diagram of auxiliary variables provided in an embodiment of the present application;

[0083] Figure 4 Another example diagram for explaining parameter variables provided in the embodiment of the present application;

[0084] Figure 5 An example diagram of the EG-SHLA algorithm provided in an embodiment of the present application;

[0085] Figure 6 An example code diagram of the EG-SHLA algorithm provided in an embodiment of the present application;

[0086] Figure 7 An example code diagram of the DEG-SHLA algorithm provided in the embodiment of the present application;

[0087] FIG8( a ) is an algorithm iteration curve diagram when T=14 provided in an embodiment of the present application;

[0088] FIG8( b ) is an algorithm iteration curve diagram when T=21 provided in an embodiment of the present application;

[0089] FIG8( c ) is an algorithm iteration curve diagram when T=28 provided in an embodiment of the present application;

[0090] Fig. 9 Parameter diagrams for different cabin space mutual leasing decision cycles provided in the embodiments of the present application;

[0091] Fig.10 An empty container supply fluctuation analysis diagram provided in an embodiment of the present application;

[0092] Fig.11 A comparison chart of performance parameters of various algorithms under dynamic decision-making provided in an embodiment of the present application;

[0093] Fig.12 A schematic diagram of the structure of a liner alliance resource scheduling device provided in an embodiment of the present application;

[0094] Fig.13 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0095] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0096] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0097] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0098] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0099] Before describing the embodiments of the present application in detail, some related technologies involved in the embodiments of the present application are first described. The related technologies involved in the embodiments of the present application are applicable to the following explanations:

[0100] The relevant technology focuses on the problem of slot allocation under the slot sharing agreement, that is, using the transportation capacity of alliance partners to complete the full container demand and empty container scheduling. In fact, due to the different strategic deployments of different companies, the supply of empty containers in different ports is different. When the liner company’s empty container supply is insufficient, the alliance partner’s empty container surplus at the port can be rented to meet the freight demand, but the relevant technology cannot optimize the above problems. In addition, the relevant technology generally treats freight demand and empty container repositioning demand in a homogeneous manner. The general implementation method is to independently set a certain demand value for empty container repositioning, which circumvents the essential difficulty of the empty container repositioning problem. In summary, because the relevant technology cannot solve the technical method of empty container scheduling and slot allocation of liner alliances under uncertain demand when considering slot sharing and empty container sharing at the same time. Therefore, this application aims to solve the following technical problems:

[0101] (i) Design of cooperation model for space sharing and empty container sharing;

[0102] (ii) Container freight demand forecast based on historical data;

[0103] (iii) A space allocation method that takes into account both the demand for full containers and the scheduling of empty containers;

[0104] (iv) Evaluation of the cumulative impact of single decisions on alliance revenue management.

[0105] In order to solve at least one of the above technical problems, the embodiments of the present application provide a liner alliance resource scheduling method, device, electronic device and storage medium. The technical solution of the present application includes: constructing a stochastic programming model for liner alliance resource scheduling; calculating the expected revenue of the container to be scheduled as gradient information based on the stochastic programming model and the liner schedule; constructing a mixed integer linear programming subproblem based on the gradient information and solving it to obtain the subproblem solution result; determining the container demand scenario, and solving the deterministic problem of the demand scenario based on the subproblem solution result to obtain a deterministic result; updating the gradient information based on the dual value of the deterministic result; returning to constructing the mixed integer linear programming subproblem based on the gradient information and solving it to obtain the subproblem solution result, until the iteration end condition is reached, and the final subproblem solution result is obtained; scheduling each container according to the final subproblem solution result. The present application can effectively cope with the scheduling of containers and cabin spaces under different demand scenarios, and can improve the utilization efficiency of containers and cabin spaces.

[0106] The embodiment of the present application provides a liner alliance resource scheduling method, which relates to the field of resource scheduling technology. The liner alliance resource scheduling method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the liner alliance resource scheduling method, etc., but is not limited to the above forms.

[0107] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0108] Before describing the embodiments of the present application in detail, an exemplary implementation of the method of the present application is briefly described:

[0109] Reference Figure 1First, according to the shipping network composed of the operating routes of each company in the liner alliance and the fixed liner schedule, a corresponding space-time network is constructed. In the network, the flow of full and empty containers is considered simultaneously based on the number of space restrictions, so as to establish a stochastic programming model for minimizing the cost of the liner alliance. Secondly, according to whether the demand order is known or unknown, the stochastic programming model is divided into a two-stage stochastic programming model. On this basis, in order to more accurately predict future demand, the expected revenue of each standard container flowing into the second stage is divided according to the empty container supply arrival time and cargo loading time provided by the liner schedule, and a mixed integer linear programming sub-problem is constructed based on this. By learning the two-stage optimal resource scheduling scheme under different demand scenarios, the expected revenue is continuously updated and the mixed integer linear programming sub-problem is solved to obtain a higher quality one-stage solution. Finally, according to different optimization methods of the iteration step size, the embodiment of the present application provides two new two-stage random hybrid learning algorithms, which respectively deal with scenarios with small and large demand fluctuations.

[0110] Reference Figure 2 The embodiment of the present application provides a method for scheduling liner alliance resources using an intelligent algorithm based on a pre-trained language model. The method may include but is not limited to S200 to S260, as follows:

[0111] S200: Constructing a stochastic programming model for liner alliance resource scheduling.

[0112] Furthermore, S200 includes S201~S202:

[0113] S201: constructing a space-time network according to the routes of the liner alliance and the liner schedule;

[0114] S202: constructing a stochastic programming model according to each of the containers, the number of slots at the port on each of the routes, and the space-time network; wherein the containers include loaded containers and empty containers.

[0115] Furthermore, S202 includes the following steps:

[0116] The constraints of the heavy containers, the number of cabin spaces, the number of empty containers, the container constraints and the balance constraints of the empty containers are constructed and the stochastic programming model is constructed.

[0117] More specifically, the steps of constructing the stochastic programming model include the following steps:

[0118] The constraint problem for constructing the heavy box is:

[0119] ;

[0120] The constraint problem of the number of cabin spaces is constructed as follows:

[0121] ;

[0122] ;

[0123] The problem of constructing the number constraint of the empty boxes is:

[0124] ;

[0125] The constraints for constructing the container are:

[0126] ;

[0127] ;

[0128] ;

[0129] The equilibrium constraint problem of the empty box is constructed as:

[0130] ;

[0131] ;

[0132] The stochastic programming model is constructed as:

[0133] ;

[0134] The stochastic programming model is subject to each of the constraint problems and is subject to the following relationship:

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] ;

[0141] .

[0142] Specifically, building a stochastic programming model may include:

[0143] During a certain planning period, liner companies (using the set Indicates) often operates several routes in a fixed order of port calls (using a collection ), each route contains a set of ports In a certain route, this embodiment defines the voyage between two pairs of ports with adjacent call orders as a segment, and the set It is used to represent the set of segments of a route. The voyage between any port pair is composed of several segments. The fixed liner schedule can accurately capture the arrival time and thus determine the port call sequence. In this paper, the total planning time is According to the liner schedule, this embodiment regards the ETA time within the planning period as the node where the state changes (i.e. the time when the ship arrives at the port and loads and unloads the cargo). Any port on any route corresponds to several ETA times, and the set express.

[0144] Specifically, is a set of time nodes, and is the parameter representing the time node; is the time set of route r arriving at port i within the planning time; For route collection, and To represent the parameters of the route; A collection of companies for liner alliances, To represent the parameters of the liner company; is the route set of company C in the liner alliance; is the set of ports included in route r, is the parameter representing the port; The set of transport port pairs included in the route r. The set elements are used express; A collection of all ports that provide shipping services for company C; is the set of flight segments contained in route r, and m is the parameter representing the flight segment; is the set of corresponding liner company route numbers and route arrival times when the alliance partner's capacity is borrowed to transport the freight demand from port i to port j on route r at time t; is the number of passengers still on board at time t on route r. The set of transport tasks that are loaded at port i and shipped to port j at time, including the start time of the transport task and the transport port pair; is the starting port code and corresponding loading time set of the empty container unloaded at port i on route r at time t; is the starting port code and corresponding loading time set of the loaded container returned as an empty container to port i at time t on route r; is the unit cost of transporting a loaded container from port i to port j on route r at time t; is the unit cost of transporting an empty container from port i to port j on route r at time t; is the unit storage cost of Company C storing empty containers at Port I; is the sailing time from port i to port j on route r, departing at time t, used to calculate , ; is the average return time of empty containers; To maintain the maximum capacity of the daily operation of route r; is the initial empty container reserve of company C at port i; The company C to which the route R belongs; represents the random freight demand from port i to port j generated on route r at time t; It means judging whether the voyage from port i to port j at time t on route r contains segment m. If so, is 1, otherwise is 0; It means judging whether there is a voyage from port i to port j at time t on route r. If so, is 1, otherwise is 0; Indicates the use of the route to determine r Is it a feasible means of transport to transport the cargo from port i to port j at time t? 1 otherwise is 0; is the number of containers loaded at time t on route r and shipped from port i to port j; is the number of empty containers loaded at time t on route r and transferred from port i to port j; For r route use The space of the route is loaded at time t, and the number of loaded containers shipped from port i to port j; Charter for R route The space of the route is loaded at time t, and the number of empty containers allocated from port i to port j; The number of loaded containers shipped from port i to port j at time t to fulfill the demand for leasing empty containers from company c on route r; To rent empty containers from company C to fulfill the requirements of route R, use The space of the route, loaded at time t, and the number of loaded containers shipped from port i to port j; is the number of empty containers that company c chooses to keep at port i at time t; is the number of routes r in the planning period T. The number of spaces rented on route segment m; It represents the total number of deployed empty containers unloaded at port i and the total number of empty containers returned after completing the previous tasks for all routes of company c at time t; represents the total number of empty containers loaded at port i at time t to meet the needs of company c; It represents the total number of empty containers leased by other liner companies in the alliance at port i for transportation at time t; It represents the total number of empty containers returned to company C at port i at time t after other liner companies in the alliance have completed their transportation tasks by leasing company C's empty containers.

[0145] For detailed description of parameter variables, please refer to Figure 3(a) to Figure 3(d) , where FIG3(a) is an example diagram for explaining a set, FIG3(b) is an example diagram for explaining a parameter, FIG3(c) is an example diagram for explaining a decision variable, and FIG3(d) is an example diagram for explaining an auxiliary variable.

[0146] Container shipping mainly allocates space to the demand for freight full containers and the demand for empty containers to be loaded and deployed to a port for possible future freight demand. Assume that the demand for OD pairs is generated according to different routes and the corresponding arrival time nodes. Freight demand is a random parameter, using It means that, depending on whether it is profitable, the liner company can decide the form and quantity of completing the transportation task from two aspects: whether to rent empty containers from alliance partners or whether to use space on other routes (including capacity sharing between different routes of the same company), using decision variables For the empty container allocation demand, this embodiment does not set any clear demand value for it. This is because the driving force of empty container allocation comes from the revenue brought by completing the freight demand of other ports with empty container shortages. The specific allocation quantity depends on whether to rent space on other companies' routes. In addition, the conditions for using the space of this route to complete the freight demand include: only when the loading time of the route of the used space is later than or equal to the loading time of the original route, and the unloading time is earlier than or equal to the unloading time of the original route. That is, for the demand , possible routes And the corresponding loading time Needs to be satisfied , this time limit uses It means that only when When is a feasible means of transportation. , this embodiment can introduce 01 parameter To further determine whether the corresponding transportation is feasible. Therefore, the freight demand constraint can be created as:

[0147] ;

[0148] The ship's space will be used to meet its own transportation needs or leased to alliance partners. Obviously, the space available on each route cannot exceed the maximum capacity of the ship. In other words, in a voyage with a leg as the unit, this embodiment needs to limit the number of containers loaded on any leg at any time to not exceed the upper limit of the capacity configured for the route. First, this embodiment needs to distinguish the legs of voyages departing at different times on different routes. Therefore, this embodiment introduces the 01 parameter To determine whether the voyage of route r from port i to port j at time t includes segment m. The time t is introduced in this parameter because in practice a route may visit a port repeatedly, so the segments from port i to port j at different times may be different. The liner schedule determines the time at which , the total amount of in-transit demand that has been loaded but has not yet arrived at the destination port for unloading. This embodiment introduces the collection Therefore, the capacity constraint including space sharing is:

[0149] ;

[0150] This constraint limits the route at any time. The shipping capacity will only be leased to other liner companies, and the routes of other liner companies Chartered routes The number of containers transported by the space cannot exceed the total rental volume on the corresponding route. Infeasible variables can be eliminated;

[0151] ;

[0152] The above constraints mean that for the route For each flight segment, the total number of spaces used by the liner company to meet its own transportation needs and leased to alliance partners at any time shall not exceed the maximum capacity of the flight segment.

[0153] For empty container flow, this embodiment sets an initial empty container reserve at each port for each company in the entire route network, denoted by , and then based on the flow balance principle, the optimal solution for container flow is determined. The number of containers kept as empty container reserves at the port at different times is determined using the decision variable In addition, in order to achieve a clearer model construction, this embodiment introduces auxiliary variables , , , which represent the amount of returned owned containers, loaded containers, and the number of empty containers leased and returned by alliance partners based on different routes, different times, and different ports.

[0154] In order to represent all possible empty container returns, this embodiment can accumulate the empty containers returned to the port at any time. First, introduce the set To obtain the starting port and loading time of the OD pair unloaded at port i on route r at time t, specifically, . Second, use represents the lag time of empty container return. The loading time set considering the lag can be used Based on this, the relevant constraints can be constructed as:

[0155] ;

[0156] The first and second items of the above constraints represent the loaded containers and empty containers unloaded by C company's R route using its own space. The loaded containers need to consider the delayed return time. Collection, and empty box deployment can be used immediately after unloading, so use The third and fourth items represent containers transported using space on other routes.

[0157] ;

[0158] Indicates the container load, and The settings are the same.

[0159] ;

[0160] ;

[0161] By summing constraint (6), we can obtain the total number of empty containers leased from liner company c at port i and loaded at time t. Constraint (7) can obtain the total number of empty containers leased from liner company c at different ports for cargo transportation and returned to port i at time t.

[0162] So far, this embodiment can construct the empty container resource flow balance constraints of different liner companies at any time and in different ports as follows:

[0163] ;

[0164] The left side of the constraint represents the empty containers that are chosen to be kept at port i at time t-1, the empty containers returned by other liner companies at time t, the empty containers returned by the consignee at time t, and the empty containers allocated from other ports. The right side of the constraint represents the empty containers that are still chosen to be kept at port i at time t, the empty containers leased by other liner companies at time t, and the empty containers loaded at time t.

[0165] For the flow balance at the beginning of the planning period, this embodiment additionally sets the following constraints:

[0166] ;

[0167] For any port on each liner company's route, the sum of the empty containers stored at port i and the empty containers loaded at the beginning of the planning period is equal to the initial empty container supply .

[0168] The unit revenue of meeting the demand between a pair of ports on a certain route is relatively fixed in liner services, and is related to the sailing distance, loading and unloading costs, etc. For the two types of demand involved in this article, namely freight demand and empty container demand, we can use Indicates the difference between their costs and benefits. represents the container holding cost. Since the mutual leasing costs within the alliance can offset each other under the overall optimal goal, there is no need to consider the mutual leasing costs in the cost calculation. In summary, the stochastic programming model can be expressed as:

[0169] ;

[0170] The stochastic programming model is subject to the above constraints and the following relational expressions:

[0171] ;

[0172] ;

[0173] ;

[0174] ;

[0175] ;

[0176] ;

[0177] .

[0178] S210: Calculate the expected revenue of the container to be scheduled as gradient information according to the stochastic programming model and the liner schedule.

[0179] Furthermore, S210 includes S211 to S214:

[0180] S211: Determine the loading time and arrival time of each container at each port according to the liner schedule; wherein the containers include loaded containers and empty containers;

[0181] S212: Calculate the difference between the loading time and the arrival time corresponding to each container;

[0182] S213: Calculating the transportation and storage costs of each container according to the difference;

[0183] S214: Calculate the expected revenue of each container as the gradient information according to the stochastic programming model and the transportation and storage costs.

[0184] S220: Construct a mixed integer linear programming subproblem according to the gradient information and solve it to obtain a solution result of the subproblem.

[0185] Further, S220 includes the following steps S221-S222:

[0186] S221: Construct the mixed integer linear programming sub-problem according to the gradient information as follows:

[0187] ;

[0188] S222: constructing an EG-SHLA algorithm to solve the mixed integer linear programming subproblem;

[0189] The EG-SHLA algorithm constrains the mixed integer linear programming subproblem to be subject to the following relational expression:

[0190] ;

[0191] ;

[0192] ;

[0193] ;

[0194] = ;

[0195] , ;

[0196] , .

[0197] Furthermore, S222 includes the following steps:

[0198] Adding first-order moment estimation and second-order moment estimation to the EG-SHLA algorithm to obtain a DEG-SHLA algorithm to update the gradient information;

[0199] The DEG-SHLA algorithm is used to solve the mixed integer linear programming subproblem.

[0200] For ease of understanding, the overall description of S210 and S220 is as follows:

[0201] Specifically, the empty containers that complete a transportation task in the first stage and are returned in the second stage and the empty containers that are selected to be kept at the port in the first stage can be used to complete the transportation task in the second stage. The above variables are recorded as In order to take the above variables into consideration in resource scheduling, this embodiment can determine the return time of empty containers in the second stage and the deadline for loading the second stage freight demand according to the liner schedule. The series of parameters introduced can refer to Figure 4 .

[0202] It should be noted that this embodiment provides a specific example to illustrate how to set ,like Figure 5 In this embodiment, firstly, all the possible demands in the second stage are numbered, and the expected benefits and loading time of the demands are recorded, such as Figure 5 The numbers in the boxes represent the number, expected revenue and loading time in sequence. Only empty container supplies that arrive before the loading time can meet the transportation task for the day. Figure 5 The circle columns in represent the empty container supplies that arrive on the 7th and 9th days respectively. Since the loading time of the first box is on the 8th day, the empty container supply that arrives on the 9th day cannot be used. Therefore, box 1 is linked to the circle column on the 7th day, and the linking rules for the remaining boxes are the same. According to the difference between the loading time of the box linked to circle c and the supply arrival time of the circle, the corresponding empty container storage cost can be calculated, and thus the expected profit of using the supply that arrives on a certain day to meet the demand generated on a certain day can be calculated. Finally, the circle column is sorted according to the size of the profit (the number in the circle represents the number of the linked box). Each circle in the column is marked with the corresponding profit sorting (from large to small) number and the expected profit after considering the storage cost (i.e. - (s)). Since all supplies arriving earlier than the loading time can be used to meet the demand, it is possible that a box links multiple circles, such as Figure 5 In the random programming model, each box selects at most one link, and the array (t, s) of time and benefit sorting numbers of the circles corresponding to each box is used as the set storage.

[0203] Therefore, the Mixed-Integer Linear Programming (MILP) subproblem can be expressed as:

[0204] ;

[0205] The above subproblems are subject to the following relational expressions:

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] = ;

[0211] , ;

[0212] , ;

[0213] in, is the parameter vector of the objective function of one stage; is the decision variable vector of one stage; is the flow balance constraint coefficient matrix of one stage; is the space capacity constraint coefficient matrix of one stage; is the coefficient vector of the right-hand side of the space capacity constraint in one stage; is the demand constraint coefficient matrix of one stage; The coefficient vector of the right-hand side of the demand constraint in the first stage; is a unit coefficient vector, that is, all elements are 1; For the general The port numbers of the ports in the unified order are a set of port numbers. A collection of all ports that provide shipping services for company C; The maximum number of empty containers that can be called upon by each liner company; Number the freight demands generated in the second stage, starting from 0 to the largest freight demand; Indicates the empty box numbers that flow in at different times in the second stage, starting from 0 to the maximum number of empty boxes; is the variable representing container transportation in one stage, which is the container that flows into node i at time t in the second stage; is the time t of the second stage. Whether the empty box flows into the i node, if so, is 1, if so is 0; is the time set when the empty box may be returned to the i-node in the second phase, ; is the number generated in the second stage at the i node. The possible supply arrival time corresponding to the demand and the corresponding empty box sequence number set used to complete the demand ( t,s ), ; (s) is the expected return of the sth box arriving at node i before time t at the kth iteration, The algorithm provided in this embodiment can solve the mixed integer linear programming sub-problem. The algorithm is denoted as EG-SHLA (Enhanced gradient stochastic hybrid learning algorithm). The example code of the EG-SHLA algorithm is as follows: Figure 6 shown.

[0214] Stochastic Subgradient in EG-SHLA Algorithm and All come from different This may lead to the following two problems: 1) If the scenario The difference is large, and the iteration process is very unstable; 2) In the natural step size Under the convergence rule, the final effect of the algorithm is strongly related to the scene quality at the initial iteration. In order to improve the above problems, this embodiment provides a new gradient optimization method based on the Adam optimization method, and further adds first-order moment estimation and second-order moment estimation to the EG-SHLA algorithm to adaptively optimize the gradient, which is recorded as the DEG-SHLA algorithm. The sample code of the DEG-SHLA algorithm is as follows: Figure 7 shown.

[0215] S230: Determine the demand scenario of the container, and solve the deterministic problem of the demand scenario according to the solution results of the sub-problems to obtain a deterministic result.

[0216] S240: Update the gradient information according to the dual value of the deterministic result.

[0217] S250: Return to constructing the mixed integer linear programming sub-problem according to the gradient information and solving the sub-problem to obtain the sub-problem solution result, until the iteration end condition is reached, and the final sub-problem solution result is obtained.

[0218] S260: Dispatching each of the containers according to the final solution results of the sub-problems.

[0219] The beneficial effects of this application include:

[0220] (1) The constructed two-stage stochastic programming model and two solution algorithms can effectively deal with the liner alliance resource scheduling problem under different levels of demand fluctuations;

[0221] (2) It takes into account the heterogeneity of full container demand and empty container relocation and optimizes the alliance cooperation means of space sharing and empty container sharing at the same time, which has stronger practical significance;

[0222] (3) It can evaluate the cumulative impact of alliance resource scheduling plans over time, providing a more reliable basis for liner alliances to plan their shipping networks.

[0223] Next, the solution of the embodiment of the present application will be introduced and explained in conjunction with specific application examples:

[0224] Illustratively, the liner alliance in this embodiment operates 15 routes in total, including 22 ports.

[0225] (I) Algorithm performance test:

[0226] First, the algorithm performance is tested by a small-scale example. This embodiment conducts experiments to verify the convergence performance of EG-SHLA and DEG-SHLA, where the same type of algorithms SHLA and PB are used as upper and lower bounds, as mentioned above. Figure 7 As shown. In order to more clearly observe the changing trends of the three curves with the number of iterations, EG-SHLA and DEG-SHLA use the y-left axis label, and SHLA uses the y-right axis label. The experimental results show that the iteration process of SHLA and EG-SHLA using natural step size obviously fluctuates greatly. Since the random subgradients obtained from different instances may have a large gap, as long as the ranking of the expected cost in the objective function of the algorithm subproblem changes, completely different empty box allocation schemes may be formed. EG-SHLA has the highest upper limit, while DEG-SHLA uses the second-order moment estimation to optimize the gradient, diluting the random subgradients that are significantly different from the historical gradients, thereby maintaining a stable convergence process, but the cost is to reduce the improvement of the objective function by good random subgradients.

[0227] In order to further explore the impact of the initial empty container supply on the results and the corresponding convergence process, this example fixes the demand mean to 60 and the planning time to 14 days, and conducts a sensitivity analysis on the initial empty container supply. Figure 6 .exist Figure 6In , the x-axis represents the initial empty box supply, the y-axis represents the number of iterations, and the z-axis represents the total cost of valuation. Figure 6 It can be seen that as the initial empty box supply increases, the costs of SHLA, EG-SHLA and DEG-SHLA all follow the rule of first decreasing and then increasing. The difference is that SHLA gradually decreases when the initial empty box supply reaches 9000, while EG-SHLA and DEG-SHLA both reach the minimum value when the initial empty box supply is 6500. In the range of , the cost that the SHLA algorithm can achieve is always greater than the cost of EG-SHLA and DEG-SHLA. In summary, EG-SHLA and DEG-SHLA can utilize empty container resources more efficiently and have an absolute advantage over SHLA.

[0228] (II) Scrolling horizon experiment:

[0229] This example constructs a rolling horizon environment to test the performance of the uncertainty two-stage model in a more realistic decision-making scenario. First, we briefly explain how to construct a rolling horizon environment. To facilitate the explanation of the problem, a series of parameters are introduced. represents the total planning period length, represents the length of the network in one stage, represents the length of the second-stage network, represents the total starting time of the two-stage problem, Indicates the end time of a stage. Indicates the end time of the second stage. This embodiment studies a dynamic decision-making problem that continuously solves the two-stage model over time until the end of the planning period is reached. The algorithm iteration curve can be referred to Figure 8(a) to Figure 8(c) , where FIG8(a) is an algorithm iteration curve diagram when T=14, FIG8(b) is an algorithm iteration curve diagram when T=21, and FIG8(c) is an algorithm iteration curve diagram when T=28.

[0230] exist Fig. 9 In and This example compares the performance of SHLA, EG-SHLA and DEG-SHLA for the decision cycle of mutual leasing of space. In the experiment, this example sets the uncertain demand to follow Poisson distribution with a mean of 60, the initial empty container supply is 10,000, the total planning period is 49 days, and the first stage time is 7 days. The Gap value between the decision cycle result and the global optimal solution is smaller than Time is the result of the decision cycle. This is contrary to the common sense that the more detailed the decision, the better the effect. Since it is impossible to predict the future completely accurately, frequent decisions will amplify mistakes. For example, the number of space leasing decided in the first week will affect the decision-making in the following weeks because some freight demands occupy the space for a long time. When higher-yield demand appears, the liner company has to rearrange the space or reject the demand. Therefore, this example can conclude that space leasing behavior is more suitable for long-term agreements.

[0231] Fig.10 Compared with The performance of Myopic (a short-sighted algorithm that does not consider the future), SHLA, EG-SHLA and DEG-SHLA, as well as a two-stage deterministic model (denoted as DETM) under the space mutual leasing decision cycle. In DETM, the unknown parameters of the two stages are replaced by sample means. The comparison between the two-stage deterministic problem and the two-stage random problem designed in this embodiment is very important to evaluate the effectiveness of the method provided by this embodiment. EG / DEG-SHLA completely surpasses the performance of DETM, and the longer the planning period, the better the performance. Fig.11 ,When the planning period is 28 days, EG / DEG-SHLA both achieve more than 93% of the global optimal solution.

[0232] Reference Fig.12 The embodiment of the present application further provides a liner alliance resource scheduling device, which can implement the above-mentioned liner alliance resource scheduling method, and the device includes:

[0233] Model building unit, used to build a stochastic programming model for liner alliance resource scheduling;

[0234] A gradient calculation unit, used to calculate the expected revenue of the container to be scheduled as gradient information according to the stochastic programming model and the liner schedule;

[0235] A planning and solving unit, used for constructing a mixed integer linear programming sub-problem according to the gradient information and solving the sub-problem to obtain a solution result;

[0236] A demand solving unit, used to determine the demand scenario of the container, and solve the deterministic problem of the demand scenario according to the solution results of the sub-problems to obtain a deterministic result;

[0237] a gradient updating unit, configured to update the gradient information according to the dual value of the deterministic result;

[0238] An iterative updating unit, used for returning the mixed integer linear programming subproblem constructed according to the gradient information and solving the subproblem to obtain a solution result of the subproblem, until an iteration end condition is reached, and a final solution result of the subproblem is obtained;

[0239] A resource scheduling unit is used to schedule each of the containers according to the final solution results of the sub-problems.

[0240] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0241] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned liner alliance resource scheduling method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, a car computer, etc.

[0242] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0243] See also Fig.13 , Fig.13 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0244] The processor 1301 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0245] The memory 1302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1302 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1302, and the processor 1301 calls and executes the liner alliance resource scheduling method of the embodiment of this application;

[0246] Input / output interface 1303, used to implement information input and output;

[0247] The communication interface 1304 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0248] A bus 1305 that transmits information between various components of the device (e.g., the processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304);

[0249] The processor 1301 , the memory 1302 , the input / output interface 1303 and the communication interface 1304 are connected to each other in communication within the device via a bus 1305 .

[0250] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned liner alliance resource scheduling method is implemented.

[0251] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0252] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0253] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0254] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0255] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0256] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0257] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0258] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0259] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0260] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0261] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0262] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0263] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A liner alliance resource scheduling method, characterized in that: The method comprises the following steps: Construct a stochastic programming model for liner alliance resource scheduling; Calculating the expected revenue of the container to be scheduled as gradient information according to the stochastic programming model and the liner schedule; Constructing a mixed integer linear programming subproblem according to the gradient information and solving it to obtain a solution result of the subproblem; Determine the demand scenario of the container, and solve the deterministic problem of the demand scenario according to the solution results of the sub-problems to obtain a deterministic result; updating the gradient information according to the dual value of the deterministic result; Returning to constructing the mixed integer linear programming subproblem according to the gradient information and solving the subproblem to obtain the subproblem solution result, until the iteration end condition is reached, and obtaining the final subproblem solution result; Dispatching each of the containers according to the final solution results of the sub-problems; The stochastic programming model for liner alliance resource scheduling is constructed, comprising the following steps: Constructing a space-time network based on the various routes of the liner alliance and the liner schedules; A stochastic programming model is constructed according to each of the containers, the number of slots at ports on each of the routes, and the spatiotemporal network; wherein the containers include loaded containers and empty containers; The stochastic programming model is constructed according to each container, the number of port spaces on each route, and the space-time network, comprising the following steps: Constructing the constraint problem of the heavy container, the constraint problem of the number of cabin spaces, the constraint problem of the number of empty containers, the constraint problem of the container and the balance constraint problem of the empty containers and constructing the stochastic programming model; The step of constructing the constraint problem of the heavy container, the constraint problem of the number of cabin spaces, the constraint problem of the number of empty containers, the constraint problem of the container and the balance constraint problem of the empty containers and constructing the stochastic programming model comprises the following steps: The constraint problem for constructing the heavy box is: ； The constraint problem of the number of cabin spaces is constructed as follows: ； ； The problem of constructing the number constraint of the empty boxes is: ； The constraints for constructing the container are: ； ； ； The equilibrium constraint problem of the empty box is constructed as follows: ； ； The stochastic programming model is constructed as: ； The stochastic programming model is subject to each of the constraint problems and is subject to the following relationship: ； ； ； ； ； ； ； in, is a set of time nodes, and is the parameter representing the time node; is the time set of route r arriving at port i within the planning time; For route collection, and To represent the parameters of the route; A collection of companies for liner alliances, To represent the parameters of the liner company; is the route set of company C in the liner alliance; is the set of ports included in route r, is the parameter representing the port; The set of transport port pairs included in the route r. The set elements are used express; A collection of all ports that provide shipping services for company C; is the set of flight segments contained in route r, and m is the parameter representing the flight segment; is the set of corresponding liner company route numbers and route arrival times when the alliance partner's capacity is borrowed to transport the freight demand from port i to port j on route r at time t; is the number of passengers still on board at time t on route r. The set of transport tasks that are loaded at port i and shipped to port j at time, including the start time of the transport task and the transport port pair; is the starting port code and corresponding loading time set of the empty container unloaded at port i on route r at time t; is the starting port code and corresponding loading time set of the loaded container returned as an empty container to port i at time t on route r; is the unit cost of transporting a loaded container from port i to port j on route r at time t; is the unit cost of transporting an empty container from port i to port j on route r at time t; is the unit storage cost of Company C storing empty containers at Port I; is the sailing time from port i to port j on route r, departing at time t, used to calculate , ; is the average return time of empty containers; To maintain the maximum capacity of the daily operation of route r; is the initial empty container reserve of company C at port i; The company C to which the route R belongs; represents the random freight demand from port i to port j generated on route r at time t; It means judging whether the voyage from port i to port j at time t on route r contains segment m. If so, is 1, otherwise is 0; It means judging whether there is a voyage from port i to port j at time t on route r. If so, is 1, otherwise is 0; Indicates the use of the route to determine r Is it a feasible means of transport to transport the cargo from port i to port j at time t? 1 otherwise is 0; is the number of containers loaded at time t on route r and shipped from port i to port j; is the number of empty containers loaded at time t on route r and transferred from port i to port j; For r route use The space of the route is loaded at time t, and the number of loaded containers shipped from port i to port j; Charter for R route The space of the route is loaded at time t, and the number of empty containers allocated from port i to port j; The number of loaded containers shipped from port i to port j at time t to fulfill the demand for leasing empty containers from company c on route r; To rent empty containers from company C to fulfill the requirements of route R, use The space of the route, loaded at time t, and the number of loaded containers shipped from port i to port j; is the number of empty containers that company c chooses to keep at port i at time t; is the number of routes r in the planning period T. The number of spaces rented on route segment m; It represents the total number of deployed empty containers unloaded at port i and the total number of empty containers returned after completing the previous tasks for all routes of company c at time t; represents the total number of empty containers loaded at port i at time t to meet the needs of company c; It represents the total number of empty containers leased by other liner companies in the alliance at port i for transportation at time t; It represents the total number of empty containers returned to company C at port i at time t after other liner companies in the alliance have completed their transportation tasks by leasing company C's empty containers; The step of calculating the expected revenue of the container to be scheduled as gradient information according to the stochastic programming model and the liner schedule includes the following steps: Determining the loading time and arrival time of each container at each port according to the liner schedule; wherein the containers include loaded containers and empty containers; Calculating the difference between the loading time and the arrival time corresponding to each container; Calculate the transportation and storage costs of each container according to the difference; Calculating the expected revenue of each container as the gradient information according to the stochastic programming model and the transportation and storage costs; The step of constructing a mixed integer linear programming subproblem according to the gradient information and solving the subproblem to obtain a solution result of the subproblem includes the following steps: The mixed integer linear programming sub-problem is constructed according to the gradient information as follows: ； Constructing the EG-SHLA algorithm to solve the mixed integer linear programming subproblem; The EG-SHLA algorithm constrains the mixed integer linear programming subproblem to be subject to the following relational expression: ； ； ； ； = ； , ； , ； in, is the parameter vector of the objective function of one stage; is the decision variable vector of one stage; is the flow balance constraint coefficient matrix of one stage; is the space capacity constraint coefficient matrix of one stage; is the coefficient vector of the right-hand side of the space capacity constraint in one stage; is the demand constraint coefficient matrix of one stage; The coefficient vector of the right-hand side of the demand constraint in the first stage; is a unit coefficient vector, that is, all elements are 1; For the general The port numbers of the ports in the unified order are a set of port numbers. A collection of all ports that provide shipping services for company C; The maximum number of empty containers that can be called upon by each liner company; Number the freight demands generated in the second stage, starting from 0 to the largest freight demand; Indicates the empty box numbers that flow in at different times in the second stage, starting from 0 to the maximum number of empty boxes; is the variable representing container transportation in one stage, which is the number of containers flowing into port i at time t in the second stage; is the time t of the second stage. Whether the empty container flows into port i, if so, is 1, if so is 0; is the possible time set for returning empty containers to port i in the second stage, ; For port i, the number generated in the second stage is The possible supply arrival time corresponding to the demand and the corresponding empty box sequence number set used to complete the demand ( t,s ), ; (s) is the expected return of the sth box arriving at port i before time t at the kth iteration, ; The expected revenue of the container to be scheduled is calculated according to the stochastic programming model and the liner schedule as the gradient information as the first stage; the mixed integer linear programming sub-problems are constructed according to the gradient information and the sub-problem solution is obtained by solving the sub-problem as the second stage.

2. A liner alliance resource scheduling method according to claim 1, characterized in that: The construction of the EG-SHLA algorithm to solve the mixed integer linear programming sub-problem includes the following steps: Adding first-order moment estimation and second-order moment estimation to the EG-SHLA algorithm to obtain a DEG-SHLA algorithm to update the gradient information; The DEG-SHLA algorithm is used to solve the mixed integer linear programming subproblem.

3. A liner alliance resource scheduling device, characterized in that: The device comprises: Model building unit, used to build a stochastic programming model for liner alliance resource scheduling; A gradient calculation unit, used to calculate the expected revenue of the container to be scheduled as gradient information according to the stochastic programming model and the liner schedule; A planning and solving unit, used for constructing a mixed integer linear programming sub-problem according to the gradient information and solving the sub-problem to obtain a solution result; A demand solving unit, used to determine the demand scenario of the container, and solve the deterministic problem of the demand scenario according to the solution results of the sub-problems to obtain a deterministic result; a gradient updating unit, configured to update the gradient information according to the dual value of the deterministic result; An iterative updating unit, used for returning the mixed integer linear programming subproblem constructed according to the gradient information and solving the subproblem to obtain a solution result of the subproblem, until an iteration end condition is reached, and a final solution result of the subproblem is obtained; A resource scheduling unit, used for scheduling each of the containers according to the final solution results of the sub-problems; The stochastic programming model for liner alliance resource scheduling is constructed, comprising the following steps: Constructing a space-time network based on the various routes of the liner alliance and the liner schedules; A stochastic programming model is constructed according to each of the containers, the number of slots at ports on each of the routes, and the spatiotemporal network; wherein the containers include loaded containers and empty containers; The stochastic programming model is constructed according to each container, the number of port spaces on each route, and the space-time network, comprising the following steps: Constructing the constraint problem of the heavy container, the constraint problem of the number of cabin spaces, the constraint problem of the number of empty containers, the constraint problem of the container and the balance constraint problem of the empty containers and constructing the stochastic programming model; The step of constructing the constraint problem of the heavy container, the constraint problem of the number of cabin spaces, the constraint problem of the number of empty containers, the constraint problem of the container and the balance constraint problem of the empty containers and constructing the stochastic programming model comprises the following steps: The constraint problem for constructing the heavy box is: ； The constraint problem of constructing the number of cabins is: ； ； The problem of constructing the number constraint of the empty boxes is: ； The constraints for constructing the container are: ； ； ； The equilibrium constraint problem of the empty box is constructed as: ； ； The stochastic programming model is constructed as: ； The stochastic programming model is subject to each of the constraint problems and is subject to the following relationship: ； ； ； ； ； ； ； in, is a set of time nodes, and is the parameter representing the time node; is the time set of route r arriving at port i within the planning time; For route collection, and To represent the parameters of the route; A collection of companies for liner alliances, To represent the parameters of the liner company; is the route set of company C in the liner alliance; is the set of ports included in route r, is the parameter representing the port; The set of transport port pairs included in the route r. The set elements are used express; A collection of all ports that provide shipping services for company C; is the set of flight segments contained in route r, and m is the parameter representing the flight segment; is the set of corresponding liner company route numbers and route arrival times when the alliance partner's capacity is borrowed to transport the freight demand from port i to port j on route r at time t; is the number of passengers still on board at time t on route r. The set of transport tasks that are loaded at port i and shipped to port j at time, including the start time of the transport task and the transport port pair; is the starting port code and corresponding loading time set of the empty container unloaded at port i on route r at time t; is the starting port code and corresponding loading time set of the loaded container returned as an empty container to port i at time t on route r; is the unit cost of transporting a loaded container from port i to port j on route r at time t; is the unit cost of transporting an empty container from port i to port j on route r at time t; is the unit storage cost of Company C storing empty containers at Port I; is the sailing time from port i to port j on route r, departing at time t, used to calculate , ; is the average return time of empty containers; To maintain the maximum capacity of the daily operation of route r; is the initial empty container reserve of company C at port i; The company C to which the route R belongs; represents the random freight demand from port i to port j generated on route r at time t; It means judging whether the voyage from port i to port j at time t on route r contains segment m. If so, is 1, otherwise is 0; It means judging whether there is a voyage from port i to port j at time t on route r. If so, is 1, otherwise is 0; Indicates the use of the route to determine r Is it a feasible means of transport to transport the cargo from port i to port j at time t? 1 otherwise is 0; is the number of containers loaded at time t on route r and shipped from port i to port j; is the number of empty containers loaded at time t on route r and transferred from port i to port j; For r route use The space of the route is loaded at time t, and the number of loaded containers shipped from port i to port j; Charter for R route The space of the route is loaded at time t, and the number of empty containers allocated from port i to port j; The number of loaded containers shipped from port i to port j at time t to fulfill the demand for leasing empty containers from company c on route r; To rent empty containers from company C to fulfill the requirements of route R, use The space of the route, loaded at time t, and the number of loaded containers shipped from port i to port j; is the number of empty containers that company c chooses to keep at port i at time t; is the number of routes r in the planning period T. The number of spaces rented on route segment m; It represents the total number of deployed empty containers unloaded at port i and the total number of empty containers returned after completing the previous tasks for all routes of company c at time t; represents the total number of empty containers loaded at port i at time t to meet the needs of company c; It represents the total number of empty containers leased by other liner companies in the alliance at port i for transportation at time t; It represents the total number of empty containers returned to company C at port i at time t after other liner companies in the alliance have completed their transportation tasks by leasing company C's empty containers; The step of calculating the expected revenue of the container to be scheduled as gradient information according to the stochastic programming model and the liner schedule includes the following steps: Determining the loading time and arrival time of each container at each port according to the liner schedule; wherein the containers include loaded containers and empty containers; Calculating the difference between the loading time and the arrival time corresponding to each container; Calculate the transportation and storage costs of each container according to the difference; Calculating the expected revenue of each container as the gradient information according to the stochastic programming model and the transportation and storage costs; The step of constructing a mixed integer linear programming subproblem according to the gradient information and solving the subproblem to obtain a solution result of the subproblem includes the following steps: The mixed integer linear programming sub-problem is constructed according to the gradient information as follows: ； Constructing the EG-SHLA algorithm to solve the mixed integer linear programming subproblem; The EG-SHLA algorithm constrains the mixed integer linear programming subproblem to be subject to the following relational expression: ； ； ； ； = ； , ； , ； in, is the parameter vector of the objective function of one stage; is the decision variable vector of one stage; is the flow balance constraint coefficient matrix of one stage; is the space capacity constraint coefficient matrix of one stage; is the coefficient vector of the right-hand side of the space capacity constraint in one stage; is the demand constraint coefficient matrix of one stage; The coefficient vector of the right-hand side of the demand constraint in the first stage; is a unit coefficient vector, that is, all elements are 1; For the general The port numbers of the ports in the unified order are a set of port numbers. A collection of all ports that provide shipping services for company C; The maximum number of empty containers that can be called upon by each liner company; Number the freight demands generated in the second stage, starting from 0 to the largest freight demand; Indicates the empty box numbers that flow in at different times in the second stage, starting from 0 to the maximum number of empty boxes; is the variable representing container transportation in one stage, which is the number of containers flowing into port i at time t in the second stage; is the time t of the second stage. Whether the empty container flows into port i, if so, is 1, if so is 0; is the possible time set for returning empty containers to port i in the second stage, ; For port i, the number generated in the second stage is The possible supply arrival time corresponding to the demand and the corresponding empty box sequence number set used to complete the demand ( t,s ), ; (s) is the expected return of the sth box arriving at port i before time t at the kth iteration, ; The expected revenue of the container to be scheduled is calculated according to the stochastic programming model and the liner schedule as the gradient information as the first stage; the mixed integer linear programming sub-problems are constructed according to the gradient information and the sub-problem solution is obtained by solving the sub-problem as the second stage.

4. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 2 when executing the computer program.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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