Multi-cycle empty container transportation method and system

Abstract

The present invention discloses a multi-period empty container transfer system and method. To address the problem of more efficient container utilization in transportation systems and reduced transportation and storage costs, the present invention addresses the dynamic and real-time nature of empty container transfer, constructing an optimization model for the coordinated transfer of empty containers between railway container freight stations based on Lyapunov optimization. The model is simplified using the Lyapunov optimization method, transforming the global optimization problem into multiple single-period problems. The resulting empty container transfer schemes between container freight stations for each period within the planned cycle can address various unexpected changes in actual operations during empty container transfers between railway container freight stations, avoiding untimely empty container transfers or long, circuitous empty container transfer routes, and substantially reducing transfer costs.

Description

Technical Field

[0001] The present invention relates to a multi-cycle empty container transfer system and method, and in particular to a multi-cycle empty container coordinated transfer system and method between railway container freight stations. Background Art

[0002] Empty container transportation refers to a plan to transport a certain type of cargo by two or more modes of transportation, such as road transportation, rail transportation, air transportation and sea transportation, and to rationally allocate and dispatch empty containers without cargo during the cargo transportation process.

[0003] Existing methods for transferring empty containers by railways only target a single cycle and are generally not practical. In railway container transport, existing methods for transferring empty containers fail to effectively address the low efficiency of multi-cycle transfers, which in turn leads to high overall costs. These methods ignore the dynamic and real-time changes that often occur in the actual application of empty container transfers by railways, resulting in inflexible transfer plans and an inability to cope with complex transportation needs and dynamic changes. This results in untimely empty container transfers or long and circuitous empty container transfer routes, which substantially increases transportation costs. This has become an industry challenge facing the transfer industry. Such technologies, such as existing technology 1, artificially divide the decision cycle T into multiple periods t to obtain a transfer plan that minimizes the total cost of acquiring empty containers within the decision period.

[0004] Existing technology 1: CN111401709B, an optimization method for empty container transportation of China-Europe trains based on container sharing, announcement date: 2020.07.10.

[0005] Therefore, existing technical solutions fail to fully consider the aforementioned challenges faced by multi-cycle empty container transportation and fail to achieve optimal utilization of transportation resources. Rationally and effectively organizing empty container transportation and improving container utilization efficiency are crucial to reducing transportation and storage costs.

[0006] Taking into account the dynamic and real-time nature of the railway container transportation process, in order to be more in line with actual conditions, the present invention designs a multi-cycle empty container transportation method. In view of the actual situation, the present invention can enable the transportation system to use containers more efficiently and reduce transportation costs and storage costs. Summary of the Invention

[0007] In order to alleviate or partially alleviate the above technical problems, the solutions of the present invention are as follows:

[0008] A multi-cycle empty container dispatching system includes: a data acquisition module, wherein the data acquisition module is configured to construct transportation network data based on railway container freight station information; and generate freight data based on the transportation plan and information of each railway container freight station; a model construction module, wherein the model construction module is configured to construct an optimization model for the coordinated dispatching of empty containers between railway container freight stations based on Lyapunov optimization according to the transportation network data constructed by the data acquisition module and the generated freight data; a model solving module, wherein the model solving module is configured to transform the global optimization problem into a single-period problem using the Lyapunov optimization method; and solve the optimization model for the coordinated dispatching of empty containers between railway container freight stations based on Lyapunov optimization; and a dispatching plan generation module, wherein the dispatching plan generation module is configured to generate a dispatching plan according to the solution result obtained by the model solving module.

[0009] In one embodiment, the system further includes a sending module; the sending module is configured to send the transportation plan to each railway container freight station.

[0010] In one embodiment, the data acquisition module is further configured to receive, from each railway container freight station, transportation network data and generated freight data for constructing an empty container collaborative dispatching optimization model.

[0011] In one embodiment, at the end of a period, based on the actual execution of the dispatch plan, the multi-period dispatch system receives updated transportation network data and generated freight data sent by each railway container freight station. Based on the updated transportation network data and generated freight data, as well as the model solving module, the dispatch plan generation module regenerates a new dispatch plan and sends it to each railway container freight station.

[0012] In one embodiment, the model building module is specifically configured to:

[0013] Taking a planning cycle as the total decision period, and given the empty container demand and initial empty container supply, make decisions on the direction and quantity of empty container transportation in each period;

[0014] Taking the minimum total cost of empty container transportation within the planning period as the optimization goal, an optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization is constructed. The specific expression of the coordinated transportation optimization model is as follows:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] x ij (t)≥0,y i (t)≥0,int

[0023] Where T represents the planning period, i.e., the total decision period, t∈{1,2,…,T}; N represents the set of container freight stations in the transportation network, i,j∈N, N={1,2,…,n}; Z represents the total cost of empty container transportation; represents the unit empty container transportation cost between freight stations i and j; C s represents the storage fee per empty container per unit time period; x ij (t) represents the number of empty containers shipped from station i to station j during period t; y i (t) represents the number of empty containers at freight station i at the end of period t and the beginning of period t+1; a i (t) represents the number of loaded containers arriving at station i during period t; b i (t) represents the number of full containers shipped from station i during period t, i.e., the number of empty containers required by freight station i during period t; represents the sending operation capacity of freight station i; represents the arrival operation capacity of freight station i; Indicates the maximum empty container storage capacity of freight station i; int represents x ij (t), y i (t) is an integer.

[0024] In one embodiment, the model solving module performs at least the following steps:

[0025] a) Calculate the change in the Lyapunov function between two adjacent time periods, that is:

[0026] Lyapunov drift in period t:

[0027]

[0028] Lyapunov penalty drift in period t:

[0029]

[0030] in, represents the Lyapunov function in time period t;

[0031] b) Convert the global optimization problem into a single time period problem:

[0032]

[0033] in, is the optimal value of the original model under time averaging; constant Z(t) represents the original objective to be optimized, that is, the penalty function; V is a non-negative weight.

[0034] In one embodiment, the model solving module includes using the Yalmip toolbox to call CPLEX to solve the model.

[0035] A multi-cycle empty container transportation method, the method comprising the following steps:

[0036] Step S1: constructing transportation network data based on railway container freight station information;

[0037] Step S2: Generate freight data based on the transportation plan and information of each container freight station;

[0038] Step S3: Based on the transportation network data constructed in step S1 and the freight data generated in step S2, a Lyapunov optimization-based empty container coordinated transportation optimization model between railway container freight stations is constructed;

[0039] Step S4: Use the Lyapunov optimization method to transform the global optimization problem into a single-period problem;

[0040] Step S5: solving the optimization model for coordinated empty container transportation between railway container freight stations based on Lyapunov optimization;

[0041] Step S6: Generate a transportation plan based on the solution obtained in step S5.

[0042] In one embodiment, the method further comprises the steps of:

[0043] Receive transportation network data and generated freight data from each railway container freight station for building an empty container collaborative dispatch optimization model;

[0044] The transportation plan is sent to each railway container freight station.

[0045] In one embodiment, the method further comprises the steps of:

[0046] At the end of a time period, based on the actual execution of the transportation plan, the multi-cycle transportation system receives the updated transportation network data and generated freight data sent by each railway container freight station. Based on the updated transportation network data and generated freight data, as well as the model solving module, the transportation plan generation module regenerates a new transportation plan and sends it to each railway container freight station.

[0047] In one embodiment, the method constructs an optimization model for coordinated empty container transportation between railway container freight stations based on Lyapunov optimization, including the following steps:

[0048] Taking a planning cycle as the total decision period, and given the empty container demand and initial empty container supply, make decisions on the direction and quantity of empty container transportation in each period;

[0049] Taking the minimum total cost of empty container transportation within the planning period as the optimization goal, an optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization is constructed. The specific expression of the coordinated transportation optimization model is as follows:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] x ij (t)≥0,y i (t)≥0,int

[0058] Where T represents the planning period, i.e., the total decision period, t∈{1,2,…,T}; N represents the set of container freight stations in the transportation network, i,j∈N, N={1,2,…,n}; Z represents the total cost of empty container transportation; represents the unit empty container transportation cost between freight stations i and j; C s represents the storage fee per empty container per unit time period; x ij (t) represents the number of empty containers shipped from station i to station j during period t; y i (t) represents the number of empty containers at freight station i at the end of period t and the beginning of period t+1; a i (t) represents the number of loaded containers arriving at station i during period t; b i(t) represents the number of full containers shipped from station i during period t, i.e., the number of empty containers required by freight station i during period t; represents the sending operation capacity of freight station i; represents the arrival operation capacity of freight station i; Indicates the maximum empty container storage capacity of freight station i; int represents x ij (t), y i (t) is an integer.

[0059] In one embodiment, the method of converting the global optimization problem into a single-period problem using the Lyapunov optimization method includes the following steps:

[0060] a) Calculate the change in the Lyapunov function between two adjacent time periods, that is:

[0061] Lyapunov drift in period t:

[0062]

[0063] Lyapunov penalty drift in period t:

[0064]

[0065] in, represents the Lyapunov function in time period t;

[0066] b) Convert the global optimization problem into a single time period problem:

[0067]

[0068] in, is the optimal value of the original model under time averaging; constant Z(t) represents the original objective to be optimized, that is, the penalty function; V is a non-negative weight.

[0069] The technical solution of the present invention has the following beneficial technical effects:

[0070] The present invention controls the transportation plan in the entire transportation system based on the dynamic and real-time characteristics of empty container transportation, ensuring that the total cost of empty container transportation in the entire transportation network is minimized within the planning period.

[0071] In addition, other beneficial effects of the present invention will be mentioned in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a flow chart of the method for coordinated empty container transportation between railway container freight stations;

[0073] Figure 2 is a transportation network diagram of the example;

[0074] Figure 3 This is a schematic diagram of a multi-cycle empty container transportation system. DETAILED DESCRIPTION

[0075] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0076] In the description of the present invention, unless otherwise specified, “ / ” indicates that the objects associated with each other are in an “or” relationship. For example, A / B can represent A or B. “And / or” in the present invention is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural.

[0077] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two. "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, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0078] In addition, to facilitate a clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or execution order, and the words "first" and "second" do not necessarily mean different.

[0079] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present invention should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplarily" and "for example" is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0080] A method for collaborative empty container movement, specifically, a method for collaborative empty container movement between railway container freight stations, is configured to be executed within a movement system. The movement system is a device configured to process various freight data involved in the collaborative empty container movement method.

[0081] The empty container coordinated transportation method comprises the following steps:

[0082] Step S1: Constructing transportation network data based on railway container freight station information.

[0083] For example, in a certain embodiment, container freight stations in various parts of Hunan Province are selected, and transportation network data is constructed based on the information of railway container freight stations, such as Figure 2 In one embodiment, the constructed transportation network data structure includes at least freight nodes and freight connection relationships between nodes.

[0084] Step S2: Generate freight data based on the transportation plans and information of each container freight station. In one embodiment, the freight information includes the empty container demand of each container freight station, the initial empty container storage capacity of each container freight station, the unit empty container transfer fee between container freight stations, the unit empty container storage fee per time period, the dispatching capacity (boxes) of each container freight station, the arrival capacity (boxes) of each container freight station, and the maximum empty container storage capacity.

[0085] In an exemplary embodiment, the present invention generates freight data according to the transportation plan and information of each container freight station, as shown in Table 1, Table 2, Table 3, and Table 4 respectively.

[0086] Table 1: Loaded container shipments and arrivals at container freight stations during different periods

[0087]

[0088] Table 2: Empty container holdings at various container freight stations at the initial stage of decision-making

[0089]

[0090] Table 3: Unit empty container transportation and storage costs

[0091]

[0092] Table 4: Shipping capacity, arrival capacity and maximum empty container storage capacity

[0093]

[0094] Step S3: Based on the transportation network data constructed in step S1 and the freight data generated in step S2, a Lyapunov-optimized empty container collaborative transportation optimization model between railway container freight stations is constructed.

[0095] Furthermore, the optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization is summarized as follows: based on the transportation network data constructed in step S1 and the freight data generated in step S2, the optimization goal is to minimize the total cost of empty container transportation during the decision period, and the transportation fee and storage fee in empty container transportation are used as influencing factors of the model to construct an optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization.

[0096] Specifically, the model includes the following steps:

[0097] Step S31: Taking a planning cycle as the total decision period, and given that the empty container demand and initial empty container supply are known, make a decision on the direction and quantity of empty container transportation in each period.

[0098] Step S32: Taking the minimum total cost of empty container transportation within the planning period as the optimization goal, an optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization is constructed.

[0099] Furthermore, the optimization model for coordinated empty container transportation between railway container freight stations based on Lyapunov optimization is specifically expressed as follows:

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] x ij (t)≥0,y i (t)≥0,int

[0108] Where T represents the planning period, i.e., the total decision period, t∈{1,2,…,T}; N represents the set of container freight stations in the transportation network, i,j∈N, N={1,2,…,n}; Z represents the total cost of empty container transportation; represents the unit empty container transportation cost between freight stations i and j; Cs represents the storage fee per empty container per unit time period; x ij (t) represents the number of empty containers shipped from station i to station j during period t; y i (t) represents the number of empty containers at freight station i at the end of period t and the beginning of period t+1; a i (t) represents the number of loaded containers arriving at station i during period t; b i (t) represents the number of full containers shipped from station i during period t, i.e., the number of empty containers required by freight station i during period t; represents the sending operation capacity of freight station i; represents the arrival operation capacity of freight station i; Indicates the maximum empty container storage capacity of freight station i; int represents x ij (t), y i (t) is an integer.

[0109] Step S4: Use the Lyapunov optimization method to transform the global optimization problem into a single-period problem.

[0110] Because it is difficult to solve the multi-period model directly, in this invention, the global optimization problem is converted into a single period problem through the Lyapunov optimization method for solution. The principle is that the Lyapunov optimization technique can provide the optimal value of the original problem under time average. Find an upper bound and adjust the size of the parameter V to make Z * Infinitely close to The specific steps include:

[0111] Step S41: define the Lyapunov function:

[0112] The Lyapunov function in time period t is expressed as:

[0113] Step S42: Calculate the change of the Lyapunov function between two adjacent time periods:

[0114] The Lyapunov drift in time period t is expressed as:

[0115]

[0116] The Lyapunov penalty drift in period t is expressed as:

[0117]

[0118] S43. Convert the global optimization problem into a single-period problem:

[0119]

[0120] in, is the optimal value of the original model under time averaging; constant Z(t) represents the original objective to be optimized, i.e., the penalty function; V is a non-negative weight, indicating the importance the decision maker places on the objective function. By adjusting the value of V, the objective function value for the current period can be brought as close to the global optimal value as possible, while also balancing the objective function with network stability—that is, achieving a trade-off between the total cost of empty container transportation by rail and the number of empty containers stored at each container freight station.

[0121] Step S5: Solve the optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization.

[0122] As an example, the Yalmip toolbox is used to call CPLEX to solve the model in the Matlab R2018a environment, and the total cost of obtaining an empty box is 57,480 yuan.

[0123] Step S6: Generate a transportation plan based on the solution obtained in step S5.

[0124] For example, refer to the transportation plan shown in Table 5.

[0125] Table 5: Empty container transportation plan

[0126]

[0127] Furthermore, the transportation method further includes the following step: sending the transportation plan to each railway container freight station.

[0128] Furthermore, the transportation method further includes the following steps: receiving transportation network data and generated freight data for constructing an empty container collaborative transportation optimization model from each railway container freight station.

[0129] Furthermore, at the end of a period, based on the actual execution of the dispatch plan, the multi-period dispatch system receives updated transportation network data and generated freight data from each railway container freight station. Based on this updated transportation network data and generated freight data, as well as the model solving module, the dispatch plan generation module regenerates a new dispatch plan and sends it to each railway container freight station. This embodiment ensures that the entire dispatch system can execute in an optimal dispatch state and is minimally affected by various unexpected factors.

[0130] As another aspect of the present invention, the empty container coordinated transportation system of the present invention includes the following modules:

[0131] a) Data acquisition module, wherein the data acquisition module is configured to:

[0132] Construct transportation network data based on railway container freight station information;

[0133] Generate freight data based on the transportation plans and information of each railway container freight station;

[0134] b) a model building module, wherein the model building module is configured to:

[0135] Based on the transportation network data and freight data generated by the data acquisition module, an optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization is constructed; c) a model solving module, wherein the model solving module is configured to:

[0136] Use Lyapunov optimization method to transform the global optimization problem into a single-period problem;

[0137] Solve the optimization model of coordinated empty container transportation between railway container freight stations based on Lyapunov optimization;

[0138] d) A transport plan generation module, wherein the transport plan generation module is configured to:

[0139] Generate a transportation plan based on the solution results obtained by the model solution module.

[0140] Furthermore, the multi-cycle empty container transportation system further includes:

[0141] e) a sending module, wherein the sending module is configured to:

[0142] The transportation plan is sent to each railway container freight station. Each railway container freight station executes a corresponding transportation plan according to the received transportation plan.

[0143] Furthermore, the data acquisition module is also used to receive transportation network data and generated freight data for building an empty container collaborative transportation optimization model from each railway container freight station.

[0144] Furthermore, at the end of a period, based on the actual execution of the dispatch plan, the multi-period dispatch system receives updated transportation network data and generated freight data from each railway container freight station. Based on this updated transportation network data and generated freight data, as well as the model solving module, the dispatch plan generation module regenerates a new dispatch plan and sends it to each railway container freight station. This embodiment ensures that the entire dispatch system can execute in an optimal dispatch state and is minimally affected by various unexpected factors.

[0145] Furthermore, the model building module is specifically configured to:

[0146] Taking a planning cycle as the total decision period, and given the empty container demand and initial empty container supply, make decisions on the direction and quantity of empty container transportation in each period;

[0147] Taking the minimum total cost of empty container transportation within the planning period as the optimization goal, an optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization is constructed. The specific expression of the coordinated transportation optimization model is as follows:

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155] x ij (t)≥0,y i (t)≥0,int

[0156] Where T represents the planning period, i.e., the total decision period, t∈{1,2,…,T}; N represents the set of container freight stations in the transportation network, i,j∈N, N={1,2,…,n}; Z represents the total cost of empty container transportation; represents the unit empty container transportation cost between freight stations i and j; C s represents the storage fee per empty container per unit time period; x ij (t) represents the number of empty containers shipped from station i to station j during period t; y i (t) represents the number of empty containers at freight station i at the end of period t and the beginning of period t+1; a i (t) represents the number of loaded containers arriving at station i during period t; b i (t) represents the number of full containers shipped from station i during period t, i.e., the number of empty containers required by freight station i during period t; represents the sending operation capacity of freight station i; represents the arrival operation capacity of freight station i; Indicates the maximum empty container storage capacity of freight station i, int represents x ij (t), y i (t) is an integer.

[0157] The model solving module at least performs the following steps:

[0158] a) Calculate the change in the Lyapunov function between two adjacent time periods, that is:

[0159] Lyapunov drift in period t:

[0160]

[0161] Lyapunov penalty drift in period t:

[0162]

[0163] in, represents the Lyapunov function in time period t;

[0164] b) Convert the global optimization problem into a single time period problem:

[0165]

[0166] in, is the optimal value of the original model under time averaging; constant Z(t) represents the original objective to be optimized, that is, the penalty function; V is a non-negative weight.

[0167] The model solving module includes using the Yalmip toolbox to call CPLEX to solve the model.

[0168] In the transportation system of the present invention, the method adopted is consistent with the aforementioned transportation method. These steps are incorporated into the transportation system embodiment by reference and will not be repeated here.

[0169] To better illustrate the present invention, numerous specific details are provided in the detailed description above. Those skilled in the art will appreciate that the present invention can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main purpose of the present invention.

[0170] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A multi-cycle empty container transportation system, characterized in that: The system includes: A data acquisition module, wherein the data acquisition module is configured to: construct transportation network data based on railway container freight station information; generate freight data based on the transportation plan and information of each railway container freight station; A model building module, wherein the model building module is configured to: construct an optimization model for the coordinated dispatch of empty containers between railway container freight stations based on Lyapunov optimization according to the transportation network data constructed by the data acquisition module and the generated freight data; A model solving module configured to: transform the global optimization problem into a single-period problem using a Lyapunov optimization method; and solve an optimization model for the coordinated movement of empty containers between railway container freight stations based on the Lyapunov optimization method. A transport plan generation module, wherein the transport plan generation module is configured to: generate a transport plan according to the solution result obtained by the model solution module; The system also includes a sending module; The sending module is configured to: send the transportation plan to each railway container freight station; The data acquisition module is further configured to receive, from each railway container freight station, transportation network data and generated freight data for constructing an empty container collaborative dispatch optimization model; and at the end of a time period, based on the actual execution of the dispatch plan, the multi-period empty container dispatch system receives updated transportation network data and generated freight data sent by each railway container freight station. Based on the updated transportation network data and generated freight data, as well as the model solving module, the dispatch plan generation module regenerates a new dispatch plan and sends it to each railway container freight station. The model building module is specifically configured as follows: Taking a planning cycle as the total decision period, and given the empty container demand and initial empty container supply, make decisions on the direction and quantity of empty container transportation in each period; Taking the minimum total cost of empty container transportation within the planning period as the optimization goal, an optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization is constructed. The specific expression of the coordinated transportation optimization model is as follows: x ij (t)≥0,y i (t)≥0,int Where T represents the planning period, i.e., the total decision period, t∈{1,2,…,T}; N represents the set of container freight stations in the transportation network, i,j∈N, N={1,2,…,n}; Z represents the total cost of empty container transportation; represents the unit empty container transportation cost between freight stations i and j; C s represents the storage fee per empty container per unit time period; x ij (t) represents the number of empty containers shipped from station i to station j during period t; y i (t) represents the number of empty containers at freight station i at the end of period t and the beginning of period t+1; a i (t) represents the number of loaded containers arriving at station i during period t; b i (t) represents the number of full containers shipped from station i during period t, i.e., the number of empty containers required by freight station i during period t; represents the sending operation capacity of freight station i; represents the arrival operation capacity of freight station i; Indicates the maximum empty container storage capacity of freight station i; int represents x ij (t), y i (t) is an integer; The model solving module at least performs the following steps: a) Calculate the change in the Lyapunov function between two adjacent time periods, that is: Lyapunov drift in period t: Lyapunov penalty drift in period t: in, represents the Lyapunov function in time period t; b) Convert the global optimization problem into a single time period problem: in, is the optimal value of the original model under time averaging; constant Z(t) represents the original objective to be optimized, that is, the penalty function; V is a non-negative weight.

2. The multi-cycle empty container transportation system according to claim 1, characterized in that: The model solving module includes using the Yalmip toolbox to call CPLEX to solve the model.

3. A multi-cycle empty container transportation method, characterized in that: The method comprises the following steps: Step S1: constructing transportation network data based on railway container freight station information; Step S2: Generate freight data based on the transportation plan and information of each container freight station; Step S3: Based on the transportation network data constructed in step S1 and the freight data generated in step S2, a Lyapunov optimization-based empty container coordinated transportation optimization model between railway container freight stations is constructed; Step S4: Use the Lyapunov optimization method to transform the global optimization problem into a single-period problem; Step S5: solving the optimization model for coordinated empty container transportation between railway container freight stations based on Lyapunov optimization; Step S6: Generate a transportation plan based on the solution obtained in step S5; The method further comprises the steps of: Receive transportation network data and generated freight data from each railway container freight station for building an empty container collaborative dispatch optimization model; Sending the transportation plan to each railway container freight station; as well as At the end of a period, based on the actual execution of the dispatch plan, the multi-cycle empty container dispatch system receives updated transportation network data and generated freight data sent by each railway container freight station. Based on the updated transportation network data and generated freight data, as well as the model solving module and the dispatch plan generation module, a new dispatch plan is regenerated and sent to each railway container freight station. This method constructs an optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization, including the following steps: Taking a planning cycle as the total decision period, and given the empty container demand and initial empty container supply, make decisions on the direction and quantity of empty container transportation in each period; Taking the minimum total cost of empty container transportation within the planning period as the optimization goal, an optimization model for the coordinated transportation of empty containers between railway container freight stations based on Lyapunov optimization is constructed. The specific expression of the coordinated transportation optimization model is as follows: x ij (t)≥0,y i (t)≥0,int Where T represents the planning period, i.e., the total decision period, t∈{1,2,…,T}; N represents the set of container freight stations in the transportation network, i,j∈N, N={1,2,…,n}; Z represents the total cost of empty container transportation; represents the unit empty container transportation cost between freight stations i and j; C s represents the storage fee per empty container per unit time period; x ij (t) represents the number of empty containers shipped from station i to station j during period t; y i (t) represents the number of empty containers at freight station i at the end of period t and the beginning of period t+1; a i (t) represents the number of loaded containers arriving at station i during period t; b i (t) represents the number of full containers shipped from station i during period t, i.e., the number of empty containers required by freight station i during period t; represents the sending operation capacity of freight station i; represents the arrival operation capacity of freight station i; Indicates the maximum empty container storage capacity of freight station i; int represents x ij (t), y i (t) is an integer; The Lyapunov optimization method is used to transform the global optimization problem into a single time period problem, including the following steps: a) Calculate the change in the Lyapunov function between two adjacent time periods, that is: Lyapunov drift in period t: Lyapunov penalty drift in period t: in, represents the Lyapunov function in time period t; b) Convert the global optimization problem into a single time period problem: in, is the optimal value of the original model under time averaging; constant Z(t) represents the original objective to be optimized, that is, the penalty function; V is a non-negative weight.

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