A heavy haul railway loading end empty car allocation method, device, equipment and medium

Abstract

The application provides a heavy haul railway loading end empty car allocation method, device, equipment and medium, relates to the heavy haul railway transportation organization technical field, and includes obtaining heavy haul railway information from a railway dispatch information system; a heavy haul railway loading end empty car allocation model is established according to the heavy haul railway information, the heavy haul railway loading end empty car allocation model includes a train allocation constraint submodel and a target function calculation submodel; under the condition of meeting the train allocation constraint submodel, the initial solution set of the heavy haul railway loading end empty car allocation model is randomly generated, and the initial solution set includes at least one heavy haul railway loading end empty car allocation initial scheme; the initial solution set is optimized by using a non-dominated sorting genetic algorithm with an elite strategy to obtain an optimized heavy haul railway loading end empty car allocation scheme set. The modeling process is simple and convenient, the modeling standard is unified, the method has high calculation efficiency, and has good operability, universality and reusability.

Description

Technical Field

[0001] This invention relates to the field of heavy-haul railway transportation organization technology, and more specifically, to a method, apparatus, equipment, and medium for dispatching empty cars at the loading end of heavy-haul railways. Background Technology

[0002] The loading end of heavy-haul railways consists of multiple loading stations connecting dedicated lines of coal, ore, and other production enterprises, serving as a central hub for loading bulk cargo. To expedite the loading of these bulk goods produced daily, empty cars unloaded at the unloading end need to be promptly allocated to suitable loading stations. Currently, existing methods for allocating empty cars on heavy-haul railways have the following shortcomings: they primarily focus on reducing the time and energy costs of empty car transportation, or ensuring that as many empty cars as possible arrive at their corresponding loading stations within a reasonable time window, largely neglecting the impact of empty car allocation on the subsequent organization of loaded train transportation. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, equipment, and readable storage medium for dispatching empty cars at the loading end of heavy-haul railways, in order to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0004] Firstly, this application provides a method for dispatching empty cars at the loading end of heavy-haul railways, including:

[0005] Obtain information on heavy-haul railways from the railway dispatching information system;

[0006] Based on the heavy-haul railway information, an empty car allocation model for the loading end of the heavy-haul railway is established. The empty car allocation model for the loading end of the heavy-haul railway includes a train allocation constraint sub-model and an objective function calculation sub-model.

[0007] Under the condition of satisfying the train dispatching constraint sub-model, an initial solution set of the heavy-haul railway loading end empty car dispatching model is randomly generated, and the initial solution set includes at least one initial scheme for heavy-haul railway loading end empty car dispatching.

[0008] Based on the train dispatch constraint sub-model and the objective function calculation sub-model, the initial solution set is optimized using a non-dominated sorting genetic algorithm with an elite strategy to obtain an optimized set of empty car dispatch schemes for the loading end of heavy-haul railways.

[0009] Secondly, this application also provides a device for dispatching empty cars at the loading end of heavy-haul railways, including:

[0010] Acquisition module: Acquires heavy-haul railway information from the railway dispatching information system;

[0011] Model building module: Based on the heavy-haul railway information, a heavy-haul railway loading end empty car allocation model is established. The heavy-haul railway loading end empty car allocation model includes a train allocation constraint sub-model and an objective function calculation sub-model.

[0012] Initial solution set generation module: under the condition of satisfying the train dispatch constraint sub-model, randomly generate the initial solution set of the heavy-haul railway loading end empty car dispatch model, the initial solution set including at least one initial scheme for heavy-haul railway loading end empty car dispatch;

[0013] Optimization module: Based on the train dispatch constraint sub-model and the objective function calculation sub-model, the initial solution set is optimized using a non-dominated sorting genetic algorithm with an elite strategy to obtain an optimized set of empty car dispatch schemes for the loading end of heavy-haul railways.

[0014] Thirdly, this application also provides a heavy-haul railway loading end empty car dispatching device, comprising:

[0015] Memory, used to store computer programs;

[0016] A processor is used to implement the steps of the heavy-haul railway loading end empty car dispatching method when executing the computer program.

[0017] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for dispatching empty cars at the loading end of heavy-haul railways.

[0018] The beneficial effects of this invention are as follows:

[0019] This invention features a convenient and simple modeling process, standardized modeling methods, comprehensive consideration of factors, high computational efficiency, and high reliability. It possesses good operability, versatility, and reusability, providing valuable reference for railway bureaus in developing empty car allocation plans based on their specific circumstances. When constructing the empty car allocation model at the loading end of heavy-haul railways, it also considers the optimization of loaded train combinations, thereby reducing the waiting time for matched train types at forward technical stations during the combination operations of loaded trains, and accelerating freight transportation.

[0020] In terms of solution method design, by designing the dominance relationship of the model solution, it is possible to achieve different empty car allocation schemes based on the different line capacity, with the primary goal of meeting the empty car demand at the loading end. The results can provide the best solution from different perspectives, and managers can choose the scheme according to their own situation.

[0021] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the process for dispatching empty wagons at the loading end of a heavy-haul railway as described in this embodiment of the invention.

[0024] Figure 2 This is a schematic diagram illustrating the encoding format of the solution described in the embodiments of the present invention;

[0025] Figure 3 This is a schematic diagram of the crossover operation of the solution described in the embodiments of the present invention;

[0026] Figure 4 This is a schematic diagram of the mutation operation of the solution described in the embodiments of the present invention;

[0027] Figure 5 This is a schematic diagram of the structure of the empty car dispatching device at the loading end of a heavy-haul railway as described in this embodiment of the invention. Figure 1 ;

[0028] Figure 6 This is a schematic diagram of the structure of the empty car dispatching device at the loading end of a heavy-haul railway as described in this embodiment of the invention. Figure 2 ;

[0029] Figure 7 This is a schematic diagram of the structure of the empty car dispatching equipment at the loading end of the heavy-haul railway as described in this embodiment of the invention.

[0030] Marked in the image:

[0031] 01. Acquisition Module; 011. First Acquisition Unit; 012. Second Acquisition Unit; 013. Third Acquisition Unit; 014. Fourth Acquisition Unit; 015. Fifth Acquisition Unit; 016. Sixth Acquisition Unit; 02. Model Building Module; 021. First Construction Unit; 022. Second Construction Unit; 0221. Third Construction Module; 0222. Fourth Construction Unit; 03. Initial Solution Set Generation Module; 031. First Generation Unit; 032. First Determination Unit; 033. Second Determination Unit; 034. First Judgment Unit; 035, Second Generation Unit; 04, Optimization Module; 041, Third Determination Unit; 042, Third Generation Unit; 0421, Calculation Unit; 0422, Cross Operation Unit; 0423, Mutation Operation Unit; 043, Merging Unit; 044, Fourth Generation Unit; 045, Fifth Generation Unit; 046, Second Judgment Unit; 800, Heavy-Haul Railway Loading End Empty Car Dispatch Equipment; 801, Processor; 802, Memory; 803, Multimedia Component; 804, I / O Interface; 805, Communication Component. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0033] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] Heavy-haul railway transport uses fixed wagon sets, and empty car allocation is carried out on a "train" basis. The types of empty car trains include combined trains and unit trains. Most of the empty car trains returned from heavy-haul railways are combined trains. After being sent to the loading end via the boundary station, some empty car trains need to be disassembled into two trains at the loading end technical station. This ensures that the empty car trains sent to the loading station can meet the loading line's constraints on train type. Therefore, the allocation of empty car trains can be carried out in two ways: direct delivery and disassembly followed by delivery.

[0035] Due to the differences in the types, weights, and arrival times of goods transported by various loading stations, and considering the limitations of the loading line capacity and loading equipment conditions, when allocating empty cars connected to the loading end of heavy-haul railways, the loading stations' requirements for the arrival time, type, and model of empty trains should be met to improve the satisfaction with the arrival of empty trains.

[0036] Further consideration should be given to the conversion relationship between empty and loaded trains after empty trains are loaded at the loading end of heavy-haul railways. Since the throughput capacity of loaded trains on heavy-haul railways is usually quite tight, after empty trains complete loading at various loading stations, some loaded trains need to travel to the next technical station for combination operations to improve the transport capacity of heavy-haul railways, thus combining two loaded trains into one combined train. Considering the actual situation on heavy-haul railways, only some types of loaded trains can be combined in pairs. Therefore, loaded trains that arrive at the technical station first often need to stop at the arrival / departure tracks of the technical station, waiting for the arrival of loaded trains of compatible types to be combined. Therefore, when formulating empty train allocation plans, it is also necessary to consider how to allocate trains of different types so that after completing loading operations, they can facilitate subsequent combined train operations.

[0037] Example 1:

[0038] This embodiment provides a method for dispatching empty cars at the loading end of heavy-haul railways.

[0039] See Figure 1 The figure shows that this method includes:

[0040] S1. Obtain heavy-haul railway information from the railway dispatching information system. The heavy-haul railway information includes loading end line parameters, empty train parameters, loading station loading demand parameters, loading station operation parameters, technical station operation parameters, and different train type combination judgment parameters.

[0041] Specifically, step S1 includes:

[0042] S11. Obtain loading end line parameters, which include loading station set, technical station set, running time of empty trains in the section, running time of loaded trains in the section, section throughput capacity, path information of empty trains in the running direction and path information of loaded trains in the running direction;

[0043] In this embodiment, the specific parameters of the loading end line are as follows:

[0044] Let the set of loading stations be S = {1,2,…,i,…,I};

[0045] Let the set of technical stations be S′={1,2,…,j,…,J};

[0046] Let the travel time of an empty train from station i to station i' be . Station i and station i' are any two stations in the interval, where u is the train type identifier. When the train is a 0.5-ton empty train, u = 1, and when the train is a 1-ton empty train, u = 2.

[0047] Let the travel time of the loaded train from station i to station i′ be . When the train is a 0.5-ton heavy train, u = 1; when the train is a 10,000-ton heavy train, u = 2.

[0048] Let the throughput of the interval (i, i+1) be η. i,i+1 ;

[0049] The path information between OD point pairs in the direction of travel of the empty train is as follows: This indicates whether an empty train traveling from station i′ to station i″ passes through the interval (i, i+1). If so, then... otherwise make This indicates whether an empty train traveling from station i to station i′ passes through technical station j. If so, then... otherwise Order Ξ i,i+1 This represents the set of technical stations behind section (i, i+1) in the direction of travel of the empty train;

[0050] The path information between OD points in the direction of travel of the loaded train is as follows: This indicates whether the loaded train travels from station i' to station i″ through the interval (i, i+1). If so, then... otherwise make This indicates whether the loaded train passes through technical station j on its journey from station i to station i'. If so, then... otherwise Let Ω i,i+1 This represents the set of technical stations ahead of the section (i, i+1) in the direction of travel of the loaded train.

[0051] S12. Obtain empty train parameters, the empty train parameters including empty train access time, empty train access station, empty train type and empty train class;

[0052] In this embodiment, the empty train parameters are the empty train parameters used for loading at the loading station, specifically:

[0053] Let the total number of empty trains that need to be reassigned be A;

[0054] Let the access time of the empty train in column a be TE. a ;

[0055] Let OE be the access station for the empty train in column a. a ;

[0056] Let the type of train in the empty train column a be ve. a Specifically, the types of empty cars commonly used on heavy-haul railways include: C80, C70, C64, etc.

[0057] Let the type of the empty car column in column a be UE. a , where UE a =1 indicates that the empty train in column a has a capacity of 0.5 tons; UE a =2 indicates that the empty train in column a is a 10,000-ton empty train; the technical station will perform a decomposition operation to divide the 10,000-ton empty train into two 5,000-ton empty trains.

[0058] S13. Obtain loading station loading demand parameters, wherein the loading station loading demand parameters include loading demand station, set of satisfactory empty train models, empty train demand type, empty train arrival time window, demand unmet penalty value, empty train early arrival penalty value and empty train late arrival penalty value;

[0059] In this embodiment, the loading station loading requirement parameters are specifically as follows:

[0060] Let B be the total number of empty car requests submitted by each loading station;

[0061] Let the loading station for the empty car demand in item b be DX. b ;

[0062] Let the station for the delivery of empty car demand in item b be dX. b ;

[0063] Let VX be the set of satisfactory empty vehicle models for the empty vehicle demand in item b. b ;

[0064] Let the type of the empty train queue required for the empty train demand in item b be UX. b , among which, UX b =1 indicates that the required empty wagon train type is 0.5 tons empty wagon train, UX b =2 indicates that the required empty wagon train type is a 10,000-ton empty wagon train;

[0065] Let the empty vehicle arrival time window for the empty vehicle demand of item b be [e b E b ], where e b E represents the earliest arrival time of the empty train for the empty train demand in item b. b This indicates the latest arrival time of the empty train for the empty train demand in item b;

[0066] Let the penalty value for the unmet empty vehicle demand in item b be CO. b ;

[0067] Let the penalty for an empty train arriving early be ξ1;

[0068] Let the penalty for an empty train arriving late be ξ2.

[0069] S14. Obtain loading station operation parameters, wherein the loading station operation parameters include empty train loading operation time;

[0070] In this embodiment, let the loading operation time of an empty train of type u at station i be...

[0071] S15. Obtain the technical station operation parameters, which include empty train decomposition time, empty train decomposition capacity, and loaded train combination capacity;

[0072] In this embodiment, the specific operating parameters of the technical station are:

[0073] Let the time for the technical station j to decompose a 10,000-ton combined empty car train be τd. j ;

[0074] Let the capacity of technical station j to decompose a 10,000-ton combined empty train be ηd. j ;

[0075] Let the capacity of the combined 10,000-ton heavy-duty train at technical station j be ηc. j .

[0076] S16. Obtain different vehicle type combination determination parameters, which are used to determine whether trains of different vehicle types can be combined;

[0077] In this embodiment, the parameters for determining different vehicle model combinations are specifically as follows:

[0078] Let the parameter for determining different vehicle model combinations be ρ. v,v′ Where v and v' are different train model identifiers, if a train with model v can be combined with a train with model v', then ρ v,v′ =1, otherwise ρ v,v′ =0.

[0079] Based on the above embodiments, this method further includes:

[0080] S2. Establish a heavy-haul railway loading end empty car allocation model based on the heavy-haul railway information. The heavy-haul railway loading end empty car allocation model includes a train allocation constraint sub-model and an objective function calculation sub-model.

[0081] Specifically, step S2 includes:

[0082] S21. Construct a parameter calculation sub-model based on the heavy-haul railway information. The parameter calculation sub-model is used to calculate the arrival time of empty trains or loaded trains. The parameter calculation sub-model includes a time calculation sub-model for empty trains arriving at each loading station, a time penalty value calculation sub-model for empty trains arriving at each loading station, and a time calculation sub-model for loaded trains arriving at each technical station.

[0083] Specifically, the sub-model for calculating the arrival time of empty trains at each loading station is as follows:

[0084]

[0085] In the formula, This indicates that the empty train a arrives at the loading station DX for the empty train request b. b Time; TE a Indicates the access time of the empty train in column a; OE a DX represents the access station for the empty train in column a; b Indicates the loading station for the empty car demand in item b; UE a Indicates the type of the empty car column in column a; This indicates that the empty train in column a departs from its access station OE. a Run to loading station DX b runtime; UX b This indicates the type of empty vehicle required for item b; This indicates that the empty train in column a departs from its access station OE. a The running time to reach the j-th technical station; τd j This indicates the time required for technical station j to decompose a 10,000-ton combined empty train. This represents the type UX obtained after decomposing the empty car column in column a. b An empty train travels from technical station j to loading station DX. b The runtime.

[0086] Specifically, when allocating empty cars at the loading end of heavy-haul railways, the timeliness requirements for empty car arrivals in the daily loading operations of each loading station must be met, as well as the loading station's requirements for train type. Failure to meet these requirements will affect the normal loading order. Therefore, the sub-model for calculating the arrival penalty value of empty car trains arriving at each loading station is as follows:

[0087]

[0088] In the formula, This represents the penalty value for the arrival of the empty train in column a due to the empty train demand in item b; ve a This indicates the type of train in the empty train column a; VX b This represents the set of satisfactory empty vehicle models for the empty vehicle demand in item b; This indicates that the empty train a arrives at the loading station DX for the empty train request b. b Time; [e b E b ] represents the empty vehicle arrival time window for the empty vehicle demand in item b, e b For item b, the earliest arrival time of the empty train is required. b Let ξ1 represent the latest arrival time of the empty train required for the empty train demand in item b; ξ2 represent the penalty value for the empty train arriving early; and M is the maximum value set according to actual demand.

[0089] Specifically, after the empty train completes loading at the loading station, some of the loaded cars will travel to the next technical station for assembly operations. Therefore, the calculation sub-model for the arrival time of the loaded train at each technical station is as follows:

[0090]

[0091] In the formula, A represents the time when the loaded train of empty wagons, obtained after the empty wagon demand of item b is completed and loaded, arrives at technical station j; A represents the total number of empty wagons that need to be dispatched. This is a 0-1 variable, indicating whether column a (empty vehicle) matches item b (empty vehicle demand). If so, then... otherwise This indicates the time when the empty wagon train in column a matches the empty wagon demand in item b, after the empty wagon demand in item b is loaded, the loaded wagon train arrives at technical station j.

[0092] Among them, the The method for calculating the value is as follows:

[0093]

[0094] In the formula, This indicates that the empty train a arrives at the loading station DX for the empty train request b. b Time; DX b Indicates the loading station for the empty car demand in item b; UX b Indicates the type of empty train required for item b; This indicates that the type of empty vehicle demand in item b is UX. b The empty train at loading station DX b The loading operation time; This indicates that the type obtained after the empty vehicle demand for item b is completed and loaded is UX. b The heavy train from loading station DX b The running time to the j-th technical station; e b This indicates the earliest arrival time of the empty train required for item b. If the arrival time of the empty train is earlier than item e... bEmpty trains need to be loaded at station DX b Loading can only begin when the loading line is free.

[0095] S22. Construct an empty train transport organization constraint sub-model based on the heavy-haul railway information. The empty train transport organization constraint sub-model is used to constrain the transport organization of empty trains. The empty train transport organization constraint sub-model includes the uniqueness constraint of the empty train transport mode, the uniqueness constraint of the empty train matching, the constraint that each car in the empty train is transported to the loading station, the relationship constraint between the number of empty train decompositions and the empty train type, and the empty train running route constraint.

[0096] In this embodiment, empty trains can be transported to the loading station via direct transport, or they can be sent to a technical station for dismantling operations before being transported to the loading station. Since there are multiple technical stations along the empty train's route, the unique constraint of the empty train transport method determines which technical station will perform the dismantling operation. Therefore, the unique constraint of the empty train transport method is expressed as follows:

[0097]

[0098] In the formula, This is a 0-1 variable, indicating whether column a (empty vehicle) matches item b (empty vehicle demand). If so, then... otherwise This is a 0-1 variable, indicating whether column a (empty vehicle) matches the empty vehicle demand in item b (empty vehicle demand) and no further breakdown is performed en route. If so, then... otherwise The variable is 0-1, indicating whether the empty train column in column a matches the empty train demand in item b, and whether the decomposition operation is only performed at technical station j during the journey. If yes... otherwise J represents the total number of technical stations.

[0099] In this embodiment, the uniqueness of the empty vehicle queue matching requires that each empty vehicle request matches only one empty vehicle queue, or only a virtual empty vehicle queue. The virtual empty vehicle queue is a hypothetical empty vehicle queue used to satisfy all vehicle usage requests. Therefore, the uniqueness constraint of the empty vehicle queue matching is expressed as follows:

[0100]

[0101] In the formula, This is a 0-1 variable, indicating whether the empty vehicle demand in item b matches the virtual empty vehicle column. If so, then... otherwise Furthermore, the empty car demand matched with the virtual empty car train is an unmet empty car demand; This is a 0-1 variable, indicating whether column a (empty vehicle) matches item b (empty vehicle demand). If so, then... otherwise A represents the total number of empty trains that need to be reassigned.

[0102] In this embodiment, when delivering empty wagon trains waiting for dispatch at the loading end to their respective loading stations, whether via direct transport or by disassembling them at a technical station before delivery, it should be ensured that every car in the empty wagon train can be transported to the loading station. Therefore, the constraint that every car in the empty wagon train must be transported to the loading station is expressed as:

[0103]

[0104] In the formula, UE a The type of the empty car column in column a; UX b B represents the type of empty train required for item b; B represents the total number of empty train requests submitted by each loading station.

[0105] In this embodiment, the relationship between the number of empty train decompositions and the type of empty train is as follows: when the empty train is a 0.5-ton train (i.e., UE) a =1), no disassembly is performed during transportation; when the empty car is a 10,000-ton train (i.e., UE) a =2), a maximum of one decomposition operation can be performed during transportation. Therefore, the relationship between the number of decompositions of an empty train and the type of empty train is constrained as follows:

[0106]

[0107] In the formula, This is a 0-1 variable, indicating whether the empty train column in column a matches the empty train demand in item b, and whether the decomposition operation only occurs at technical station j during the journey. If so, then... otherwise UE a J represents the type of empty train in column a; J represents the total number of technical stations.

[0108] In this embodiment, an empty train can be decomposed at a technical station if and only if the technical station is on the empty train's route. Therefore, the empty train route constraint is expressed as:

[0109]

[0110] In the formula, OE a DX represents the access station for the empty train in column a; b This indicates the loading station for the empty car demand in item b; Indicates that the empty train is operated by OE a Station operation to DX b Does the site go through technical site j? If so, then otherwise

[0111] Based on the above embodiments, the and All are 0-1 variables:

[0112]

[0113] S23. Construct a heavy-haul train combination constraint sub-model based on the heavy-haul railway information. The heavy-haul train combination constraint sub-model is used to constrain the combination mode of heavy-haul trains. The heavy-haul train combination constraint sub-model includes the relationship constraint between the number of heavy-haul train combinations and the type of heavy-haul train, the heavy-haul train combination vehicle type constraint, and the heavy-haul train running route constraint.

[0114] In this embodiment, the relationship between the number of loaded train combinations and the type of loaded train is as follows: when the loaded train is a 0.5-ton train (i.e., UX) b =1), a maximum of one combined operation can be carried out during transportation; when the loaded car train is a 10,000-ton train (i.e., UX), b =2), no combination operations are performed during transportation; therefore, the relationship between the number of combined trains and the type of combined trains is constrained as follows:

[0115]

[0116] In the formula, Let be a 0-1 variable, indicating whether the loaded train from the empty car demand item b has reached technical station j and will be combined with the loaded train from the empty car demand item b′. If so, then... otherwise UX b J represents the type of empty train required for the empty train demand in item b; J represents the total number of technical stations.

[0117] In this embodiment, since only certain train models can be combined in pairs, such as two 0.5-ton C70 trains or two 0.5-ton C80 trains, the constraints on train model combinations are as follows:

[0118]

[0119] In the formula, A represents the total number of empty trains that need to be dispatched; ve a This indicates the type of train in the empty column a; ve a′ This indicates the type of vehicle in the empty column a′; The vehicle model is indicated as ve a and ve a′ Can the trains be combined for operations? If so, then... otherwise, This indicates whether column a (empty vehicle column) matches item b (empty vehicle demand). This indicates whether column a′ (empty vehicle column) matches the empty vehicle demand item b′.

[0120] In this embodiment, since a loaded train can only perform combination operations at a technical station if and only if the technical station is on the route of the loaded train, the constraint on the route of the loaded train is:

[0121]

[0122] In the formula, DX b DX represents the loading station for the empty car demand in item b. b′ b ′ Loading stations for empty cars; dX b dX represents the station that delivers the empty car demand in item b. b′ Indicates the bth ′ The station where empty cars are requested to be handed over; Indicates that the heavy train is from DX b The station is running to dX b Has the site been approved by technical site j? If so... otherwise Indicates that the heavy train is from DX b′ The station is running to dX b′ Has the site been processed by technical site j? If so... otherwise

[0123] Based on the above embodiments, the 0-1 variables:

[0124]

[0125] S24. Construct a capacity constraint sub-model based on the heavy-haul railway information. The capacity constraint sub-model is used to constrain the section throughput capacity and the ability of technical stations to decompose empty trains and combine loaded trains. The capacity constraint sub-model includes technical station decomposition capacity constraints, technical station combination capacity constraints, and section throughput capacity constraints.

[0126] In this embodiment, the technical station decomposition capability constraint is:

[0127]

[0128] In the formula, ηd is a 0-1 variable, representing whether the empty train in column a matches the empty train demand in item b and the decomposition operation is only performed at technical station j during the journey; A represents the total number of empty trains that need to be allocated; B represents the total number of empty train demands submitted by each loading station; ηd j This indicates that technical station j has the capacity to decompose 10,000 tons of empty train cars.

[0129] In this embodiment, the constraint on the technology station combination capability is:

[0130]

[0131] In the formula, The variable is 0-1, indicating whether the loaded train from the empty car demand item b reaches technical station j and is combined with the loaded train from the empty car demand item b'; B represents the total number of empty car demands submitted by each loading station; ηc j This indicates that the technical station j has the capacity to combine a 10,000-ton heavy train.

[0132] In this embodiment, the interval throughput constraint is:

[0133]

[0134] In the formula, This indicates whether column a (empty vehicle column) matches item b (empty vehicle demand). This indicates whether the empty train in column a matches the empty train demand in item b and whether the decomposition operation is only performed at technical station j during the journey. This indicates whether the loaded car train from the empty car demand in item b has been transported to technical station j to be combined with the loaded car train from the empty car demand in item b′; Ξ i,i+1 Ω represents the set of technical stations behind section (i, i+1) in the direction of travel of the empty train; i,i+1 OE represents the set of technical stations ahead of the train in the direction of travel (i, i+1); a DX represents the access station for the empty train in column a; b This indicates the loading station for the empty car demand in item b; dX b This indicates the station from which the empty car demand in item b is fulfilled; Indicates that the empty train is operated by OE a Station operation to DX b Does the station pass through the interval (i, i+1)? otherwise Indicates that the heavy train is from DX b The station is running to dX b Does the station pass through the interval (i, i+1)? otherwise

[0135] S25. Using the parameter calculation sub-model, empty train transport organization constraint sub-model, loaded train combination constraint sub-model and capacity constraint sub-model, establish the objective function calculation sub-model.

[0136] Specifically, step S25 includes:

[0137] S251. Based on the parameters, calculate the sub-model and the empty train transport organization constraint sub-model, and construct the first objective function with the goal of minimizing the penalty value of each loading station on the arriving empty trains;

[0138]

[0139] Where F1 represents the first objective function, A represents the total number of empty trains that need to be allocated, B represents the total number of empty train requests submitted by each loading station, and CO represents the total number of empty train requests submitted by each loading station. b This represents the penalty value for the failure to meet the empty vehicle demand in item b. The variable is 0-1, indicating whether the empty vehicle demand in item b matches the virtual empty vehicle list; UX b Indicates the type of empty train required for item b; This represents the penalty value for the arrival of the empty train in column a due to the empty train demand in item b; The variable is 0-1, indicating whether column a (empty vehicle) matches the empty vehicle demand in item b.

[0140] S252. Based on the parameter calculation sub-model, the heavy train combination constraint sub-model, and the capacity constraint sub-model, construct a second objective function with the goal of maximizing the number of loaded heavy train combinations, and construct a third objective function with the goal of minimizing the waiting time for loaded heavy train combinations.

[0141] Specifically, to improve the freight transport capacity of the line, after empty cars are loaded at the loading station, the dispatched loaded trains should be combined as many times as possible at the technical stations ahead of the line. Therefore, the second objective function is:

[0142]

[0143] Where F2 represents the second objective function, J represents the total number of technical stations, and B represents the total number of empty car requests submitted by each loading station; The variable is 0-1, indicating whether the loaded train of empty cars demanded in item b runs to technical station j to be combined with the loaded train of empty cars demanded in item b′.

[0144] Specifically, since only certain car models can be paired up in loaded trains, when allocating empty cars, consideration should be given to how to distribute empty trains of different car models to various loading stations. This ensures that after the empty trains are loaded, the waiting time for paired loaded trains of the same car model to be combined at the forward technical station is minimized. Therefore, the third objective function is:

[0145]

[0146] Where F3 represents the third objective function, J represents the total number of technical stations, and B represents the total number of empty car requests submitted by each loading station; This indicates the time it takes for the loaded train of empty wagons (item b) to arrive at technical station j after the empty wagon demand is completed and loaded. This indicates the time when the empty car demand of item b′ is completed, the loaded car train arrives at technical station j; The variable is 0-1, indicating whether the loaded train of empty cars demanded in item b runs to technical station j to be combined with the loaded train of empty cars demanded in item b′.

[0147] Based on the above embodiments, this method further includes:

[0148] S3. Under the condition of satisfying the train dispatching constraint model, randomly generate the initial solution set of the empty car dispatching model at the loading end of the heavy-haul railway, the initial solution set including at least one initial scheme for empty car dispatching at the loading end of the heavy-haul railway.

[0149] The solution is encoded using real numbers and consists of sets X and Y, as follows: Figure 2 As shown;

[0150] Where X = [X1,…,X] a ,…,X A [This represents the allocation result of A empty trains;]

[0151] Among them, X a =(a,b,b) ′ X(j) represents the allocation plan for the a-th empty train, where a is the number of the empty train, b and b' are the numbers of different empty train demands that match the empty train a, and j is the technical station number for the decomposition operation of the a-th empty train. If the empty train a is not decomposed, then X(j) = ... a =(a,b,0,0);

[0152] Where Y = [Y1, ..., Y b ,…,Y B [] indicates the combination result of B loaded trains;

[0153] Among them, Y b = (b, b”, j) represents the combination result of the loaded car train after the empty car demand of item b is loaded. b is the number of the empty car demand, b” and b” are the numbers of the different empty car demands that are loaded into the loaded car train for combination, and j is the technical station number of the loaded car train combination operation. If the loaded car train loaded from empty car demand b is not combined, then Y b = (b, 0, 0).

[0154] Specifically, step S3 includes:

[0155] S31. Under the condition of satisfying the empty train transport organization constraint sub-model, randomly generate several empty train allocation results;

[0156] S32. The technical station for decomposing empty car trains is determined by the decomposition capability constraint of the technical station. The technical station for decomposing empty car trains decomposes one empty car train into two empty car trains. After the empty car trains are loaded at the loading end, they become a loaded car train.

[0157] Specifically, under the constraints of formulas (5), (6), (7), and (8), the allocation results of A empty trains are randomly generated. Under the constraints of formulas (9) and (15), a technical station is randomly selected for the decomposition operation of each empty train that needs to be decomposed. The empty train that needs to be decomposed is a 10,000-ton empty train. Thus, the allocation results of A empty trains are obtained: X = [X1, ..., X...]. a ,…,X A ].

[0158] S33. The technical station for combining heavy trains is determined by the technical station's combination capability constraint, and the technical station for combining heavy trains combines the two heavy trains into one heavy train.

[0159] Specifically, based on the empty car allocation results, the empty car demand matching the empty car trains obtained from the decomposition of the same empty car train is selected, and the loaded car trains are combined in pairs. Under the constraints of formulas (13) and (16), the technical station for combining the loaded car trains is randomly selected, thereby obtaining the combination result of B loaded car trains Y = [Y1, ..., Y b ,…,Y B ].

[0160] S34. Determine whether the allocation result of empty trains and the combination result of loaded trains meet the section throughput capacity constraints:

[0161] If satisfied, the allocation results of several empty trains and the combination results of several loaded trains will be used as the initial solution (X, Y);

[0162] Otherwise, based on satisfying the technical station decomposition capacity constraints and technical station combination capacity constraints, adjust the allocation results of empty trains, the technical stations for decomposing empty trains, the combination results of loaded trains, and the technical stations for combining loaded trains to meet the section throughput capacity constraints.

[0163] S35. Randomly generate several initial solutions, and form an initial solution set from these initial solutions.

[0164] Based on the above embodiments, this method further includes:

[0165] S4. Based on the train dispatching constraint sub-model and the objective function calculation sub-model, the initial solution set is optimized using a non-dominated sorting genetic algorithm with an elite strategy to obtain an optimized set of empty car dispatching schemes for the loading end of heavy-haul railways.

[0166] Specifically, step S4 includes:

[0167] S41. Calculate the sub-model based on the objective function to determine the dominance relationship of the solution;

[0168] Specifically, since the primary goal of empty car dispatching is to meet the demand for empty trains at each loading station, F1 is significantly more important than F2 and F3 among the three objective functions. Therefore, the dominance relationship between solutions (X,Y) and (X′,Y′) is defined as follows: The relationship is valid if and only if any one of the following three conditions is met:

[0169] (1)F1(X,Y) <F1(X′,Y′);

[0170] (2)F1(X,Y)=F1(X′,Y′), F2(X,Y)≤F2(X′,Y′), F3(X,Y) <F3(X′,Y′);

[0171] (3)F1(X,Y)=F1(X′,Y′), F2(X,Y) <F2(X′,Y′),F3(X,Y)≤F3(X′,Y′);

[0172] The solution (X,Y) is said to dominate the solution (X′,Y′), indicating that the solution (X,Y) is better than the solution (X′,Y′).

[0173] S42. Using the initial solution set as the initial population, perform genetic operations on the initial population based on the train dispatch constraint sub-model to generate a offspring population. Specifically, the offspring population is the first generation offspring population.

[0174] Specifically, the genetic operations include:

[0175] Obtain the parameters of the non-dominated sorting genetic algorithm with elitist strategy. The parameters include population size POsize, number of iterations MAXinter, crossover probability control parameter α, and mutation probability control parameter β. The population is the initial solution set, and the population size POsize is the number of initial solutions in the initial solution set.

[0176] An initial solution (X,Y) is treated as a chromosome.

[0177] 1) Calculate the adaptive crossover probability and adaptive mutation probability using the objective function and the sub-model;

[0178] Specifically, the method for calculating the adaptive crossover probability is as follows:

[0179]

[0180] In the formula, P c For adaptive crossover probability, F is the calculated parameter of the chromosome. mean F is the average of F for all chromosomes in the population. min F is the minimum value of F among all chromosomes in the population, and F is calculated by the following formula:

[0181]

[0182] The method for calculating the adaptive mutation probability is as follows:

[0183]

[0184] In the formula, P m This represents the adaptive mutation probability.

[0185] 2) Perform crossover operation on any two solutions in the initial solution set with adaptive crossover probability, and ensure that both solutions after the crossover operation satisfy the capability constraint sub-model;

[0186] Specifically, using the adaptive crossover probability P c Randomly select any two solutions, and interleave portions of X and Y from the two solutions to improve the solution search capability. For example... Figure 3 As shown, the {X1,X2,X3,Y1,Y2,Y3} segments in solutions 1 and 2 are crossed. After crossing, the b terms in the X3 and X4 segments of solution 1 are repeated. Therefore, the repeated b terms are deleted and the missing b terms are added. Similarly, the repeated b terms are deleted and the missing b terms are added to the X3 and X4 segments of the crossed solution.

[0187] At the same time, it is determined whether the chromosome after crossover satisfies the constraints of formulas (15), (16), and (17): if it does not, the technical station of the crossover segment is changed, i.e., the j value.

[0188] 3) Perform mutation operations on the solutions in the initial solution set with adaptive mutation probability, and ensure that the solutions after mutation operations satisfy the capability constraint sub-model.

[0189] Specifically, a subset b of a solution X and Y is randomly selected, and the Hungarian algorithm is used to find the optimal selection result for the selected b in relation to empty and combined loaded vehicles, such as... Figure 4 As shown, the {X1,X2,Y1,Y2,Y3} segment is selected from the solution. A portion of b terms from the selected segment is extracted and processed. The Hungarian algorithm is then used to arrange the extracted b terms into the empty spaces of the selected segment. Under the constraints of formulas (15), (16), and (17), the technical stations of some trains are randomly modified. To improve the search capability of the solution, each solution is processed with an adaptive mutation probability P. m Choose whether to perform mutation.

[0190] S43. Merge the initial population as the parent population with the offspring population and perform a non-dominated sorting;

[0191] S44. Generate a new parent population through a binary tournament, i.e., generate the second generation parent population;

[0192] Specifically, by calculating the crowding degree of individuals (solutions), and comparing the size of the crowding degree, better individuals are retained to form a new parent population. In this embodiment, the non-dominated sorting is an existing technology and will not be described in detail here.

[0193] S45. Perform genetic operations on the new parent population to generate a new offspring population, i.e., generate the second generation offspring population;

[0194] S46. Determine if the number of generation iterations (iter) of the offspring population is greater than the preset number of iterations (MAXiter):

[0195] If so, the new parent population and the new offspring population are merged into a new population, and the new population is sorted non-dominated to obtain the Pareto front solution set. The Pareto front solution set is used as the set of empty car allocation schemes at the loading end of heavy-haul railways.

[0196] Specifically, the Pareto front solution set contains several mutually non-dominant solutions, which are different empty car allocation schemes at the loading end of heavy-haul railways. In this embodiment, the acquisition of the Pareto front solution set is an existing technology, which will not be described in detail here.

[0197] If not, proceed to step S43 and repeat the process of merging the new parent population with the new offspring population and performing non-dominated sorting, that is, merging the second-generation parent population with the second-generation offspring population.

[0198] This method establishes a theoretical framework and mathematical model for the optimization of empty and loaded train combinations at the loading end of heavy-haul railways, based on the conversion relationship between empty and loaded trains at the loading end. It also designs a solution method for the problem of empty train allocation at the loading end of heavy-haul railways based on a non-dominated sorting genetic algorithm with an elite strategy. The obtained empty train allocation scheme can meet the constraints of technical station operation capacity and section throughput capacity, ensuring the feasibility of the scheme. At the same time, when allocating empty trains, the combination scheme of subsequent loaded trains is considered, which can shorten the waiting time for loaded trains to combine operations at the technical station while meeting the loading needs of the loading station.

[0199] Example 2:

[0200] like Figure 5 , Figure 6 As shown in the figure, this embodiment provides a device for dispatching empty cars at the loading end of a heavy-haul railway. The device includes:

[0201] Acquisition Module 01: Acquires heavy-haul railway information from the railway dispatching information system;

[0202] Model building module 02: Based on the heavy-haul railway information, a heavy-haul railway loading end empty car allocation model is established, which includes a train allocation constraint sub-model and an objective function calculation sub-model.

[0203] Initial solution set generation module 03: under the condition of satisfying the train dispatch constraint sub-model, randomly generate the initial solution set of the heavy-haul railway loading end empty car dispatch model, the initial solution set including at least one initial scheme for heavy-haul railway loading end empty car dispatch;

[0204] Optimization Module 04: Based on the train dispatch constraint sub-model and the objective function calculation sub-model, the initial solution set is optimized using a non-dominated sorting genetic algorithm with an elite strategy to obtain an optimized set of empty car dispatch schemes for the loading end of heavy-haul railways.

[0205] Based on the above embodiments, the acquisition module 01 specifically includes:

[0206] First acquisition unit 011: Acquires loading end line parameters, the loading end line parameters include loading station set, technical station set, running time of empty train in the section, running time of loaded train in the section, section throughput capacity, path information of empty train in the running direction and path information of loaded train in the running direction;

[0207] Second acquisition unit 012: Acquire empty train parameters, the empty train parameters including empty train access time, empty train access station, empty train type and empty train class;

[0208] Third acquisition unit 013: Acquire loading station loading demand parameters, the loading station loading demand parameters include loading demand station, set of satisfactory empty train models, empty train demand type, empty train arrival time window, demand not met penalty value, empty train early arrival penalty value and empty train late arrival penalty value.

[0209] Fourth acquisition unit 014: Acquires loading station operation parameters, including empty train loading operation time;

[0210] Fifth acquisition unit 015: Acquires technical station operation parameters, including empty train decomposition time, empty train decomposition capacity, and loaded train combination capacity;

[0211] The sixth acquisition unit 016: acquires different vehicle type combination determination parameters, which are used to determine whether trains of different vehicle types can be combined.

[0212] Based on the above embodiments, the model building module 02 specifically includes:

[0213] First construction unit 021: Constructs a parameter calculation sub-model, an empty train transport organization constraint sub-model, a loaded train combination constraint sub-model, and a capacity constraint sub-model based on the heavy-haul railway information. The parameter calculation sub-model is used to calculate the arrival time of empty or loaded trains. The empty train transport organization constraint sub-model is used to constrain the transport organization of empty trains. The loaded train combination constraint sub-model is used to constrain the combination mode of loaded trains. The capacity constraint sub-model is used to constrain the section throughput capacity and the ability of technical stations to decompose empty trains and combined loaded trains.

[0214] The capability constraint sub-model includes technical station decomposition capability constraints, technical station combination capability constraints, and interval throughput capability constraints.

[0215] Second construction unit 022: Based on the parameter calculation sub-model, empty train transport organization constraint sub-model, loaded train combination constraint sub-model and capacity constraint sub-model, establish objective function calculation sub-model.

[0216] Based on the above embodiments, the second construction module 022 specifically includes:

[0217] The third construction unit 0221: Based on the parameters, calculate the sub-model and the empty train transport organization constraint sub-model, and construct the first objective function with the goal of minimizing the penalty value of each loading station on the arriving empty train;

[0218] Fourth construction unit 0222: Based on the parameter calculation sub-model, the heavy train combination constraint sub-model and the capacity constraint sub-model, construct a second objective function with the goal of maximizing the number of loaded heavy train combinations and construct a third objective function with the goal of minimizing the waiting time for loaded heavy train combinations.

[0219] Based on the above embodiments, the initial solution set generation module 03 specifically includes:

[0220] First generation unit 031: Under the condition of satisfying the empty train transport organization constraint model, randomly generate several empty train allocation results;

[0221] First determining unit 032: The technical station for decomposing empty car trains is determined by the decomposition capability constraint of the technical station. The technical station for decomposing empty car trains decomposes one empty car train into two empty car trains. After the empty car trains are loaded at the loading end, they become a loaded car train.

[0222] Second determining unit 033: The technical station for combining heavy trains is determined by the technical station's combination capability constraint, and the technical station for combining heavy trains combines the two heavy trains into one heavy train.

[0223] First Judgment Unit 034: Determine whether the allocation result of empty trains and the combination result of loaded trains meet the section throughput capacity constraints:

[0224] If satisfied, the allocation results of several empty trains and the combination results of several loaded trains will be used as the initial solution;

[0225] Otherwise, based on satisfying the technical station decomposition capacity constraints and technical station combination capacity constraints, adjust the allocation results of empty trains, the technical stations for decomposing empty trains, the combination results of loaded trains, and the technical stations for combining loaded trains to meet the section throughput capacity constraints.

[0226] Second generation unit 035: Randomly generates several initial solutions, which together form an initial solution set.

[0227] Based on the above embodiments, the optimization module 04 specifically includes:

[0228] Third determining unit 041: Calculates the dominance relationship of the solution based on the objective function using the sub-model;

[0229] Third generation unit 042: Using the initial solution set as the initial population, perform genetic operations on the initial population based on the train dispatch constraint sub-model to generate offspring population;

[0230] Merging Unit 043: Merge the initial population as the parent population with the offspring population, and perform non-dominated sorting based on the dominance relationship of the solution;

[0231] Fourth generation unit 044: Generates a new parent population through a binary tournament;

[0232] Fifth generation unit 045: Perform genetic operations on the new parent population to generate a new offspring population;

[0233] Second judgment unit 046: Determine whether the number of times the offspring population has been generated is greater than the preset number of iterations:

[0234] If so, merge the new parent population and the new offspring population into a new population, and perform a non-dominated sort on the new population to obtain the Pareto front solution set. The Pareto front solution set is taken as the final solution set, which is the set of empty car allocation schemes at the loading end of heavy-haul railways.

[0235] If not, the new parent population is repeatedly merged with the new offspring population for non-dominated sorting.

[0236] Based on the above embodiments, the third generation unit 042 includes:

[0237] Calculation Unit 0421: Calculates the adaptive crossover probability and adaptive mutation probability using the objective function in the sub-model;

[0238] Crossover operation unit 0422: Performs crossover operation on any two solutions in the initial solution set with adaptive crossover probability, and ensures that both solutions after the crossover operation satisfy the capability constraint sub-model;

[0239] Mutation operation unit 0423: Performs mutation operation on the solutions in the initial solution set with adaptive mutation probability, and makes the solutions after mutation operation satisfy the capability constraint sub-model.

[0240] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0241] Example 3:

[0242] Corresponding to the above method embodiments, this embodiment also provides a heavy-haul railway loading end empty car dispatching device. The heavy-haul railway loading end empty car dispatching device described below and the heavy-haul railway loading end empty car dispatching method described above can be referred to each other.

[0243] Figure 7 This is a block diagram illustrating an empty car dispatching device 800 at the loading end of a heavy-haul railway, according to an exemplary embodiment. Figure 7 As shown, the heavy-haul railway loading end empty car dispatching equipment 800 may include: a processor 801 and a memory 802. The heavy-haul railway loading end empty car dispatching equipment 800 may also include one or more of the following: a multimedia component 803, an I / O interface 804, and a communication component 805.

[0244] The processor 801 controls the overall operation of the heavy-haul railway loading end empty car dispatching equipment 800 to complete all or part of the steps in the aforementioned heavy-haul railway loading end empty car dispatching method. The memory 802 stores various types of data to support the operation of the heavy-haul railway loading end empty car dispatching equipment 800. This data may include, for example, instructions for any application or method operating on the heavy-haul railway loading end empty car dispatching equipment 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the heavy-haul railway loading end empty car dispatching equipment 800 and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0245] In an exemplary embodiment, the empty car dispatching equipment 800 at the loading end of a heavy-haul railway can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described empty car dispatching method at the loading end of a heavy-haul railway.

[0246] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the above-described method for dispatching empty cars at the loading end of a heavy-haul railway. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by the processor 801 of the heavy-haul railway loading end empty car dispatching device 800 to complete the above-described method for dispatching empty cars at the loading end of a heavy-haul railway.

[0247] Example 4:

[0248] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the above-described method for dispatching empty cars at the loading end of heavy-haul railways.

[0249] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the heavy-haul railway empty car dispatching method described in the above method embodiments.

[0250] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0251] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0252] 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 variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dispatching empty cars at the loading end of a heavy-haul railway, characterized in that, include: Obtain information on heavy-haul railways from the railway dispatching information system; Based on the heavy-haul railway information, an empty car allocation model is established at the loading end of the heavy-haul railway. This model includes a train allocation constraint sub-model and an objective function calculation sub-model, comprising: Based on the heavy-haul railway information, a parameter calculation sub-model, an empty train transport organization constraint sub-model, a loaded train combination constraint sub-model, and a capacity constraint sub-model are constructed. The parameter calculation sub-model is used to calculate the arrival time of empty or loaded trains; the empty train transport organization constraint sub-model is used to constrain the transport organization of empty trains; the loaded train combination constraint sub-model is used to constrain the combination mode of loaded trains; and the capacity constraint sub-model is used to constrain the section throughput capacity and the ability of technical stations to decompose empty trains and combined loaded trains. The capability constraint sub-model includes technical station decomposition capability constraints, technical station combination capability constraints, and interval throughput capability constraints. Based on the parameter calculation sub-model, empty train transport organization constraint sub-model, loaded train combination constraint model and capacity constraint model, establish an objective function calculation sub-model; Under the condition of satisfying the train dispatching constraint sub-model, an initial solution set of the heavy-haul railway loading end empty car dispatching model is randomly generated, and the initial solution set includes at least one initial scheme for heavy-haul railway loading end empty car dispatching. Based on the train dispatching constraint sub-model and the objective function calculation sub-model, a non-dominated sorting genetic algorithm with an elitist strategy is used to optimize the initial solution set, resulting in an optimized set of empty car dispatching schemes for heavy-haul railway loading ends, including: The dominance relationship of the solution is determined by calculating the sub-model based on the objective function; The initial solution set is used as the initial population. Genetic operations are performed on the initial population based on the train dispatch constraint sub-model to generate the offspring population. The initial population is merged with the offspring population as the parent population, and a non-dominated sort is performed based on the dominance relationship of the solution. New parent populations are generated through a binary tournament format. Genetic manipulation is performed on the new parent population to generate a new offspring population; Determine whether the number of times the offspring population is generated is greater than the preset number of iterations: if so, merge the new parent population and the new offspring population into a new population, and perform non-dominated sorting on the new population to obtain the Pareto front solution set, and use the Pareto front solution set as the set of empty car dispatching schemes at the loading end of heavy-haul railways. If not, the new parent population is merged with the new offspring population and non-dominated sorting is performed repeatedly.

2. The method for dispatching empty cars at the loading end of heavy-haul railways according to claim 1, characterized in that, The initial solution set for randomly generating the empty car dispatching model at the loading end of the heavy-haul railway, under the condition of satisfying the train dispatching constraint sub-model, includes: Under the condition of satisfying the empty train transport organization constraint sub-model, several empty train allocation results are randomly generated; The technical station for decomposing empty car trains is determined by the decomposition capacity constraint of the technical station. The technical station for decomposing empty car trains decomposes one empty car train into two empty car trains. After the empty car trains are loaded at the loading end, they become a loaded car train. The technical station for combining heavy-duty trains is determined by the technical station's combination capability constraint, and the technical station for combining heavy-duty trains combines the two heavy-duty trains into one heavy-duty train. Determine whether the allocation results of empty trains and the combination results of loaded trains satisfy the section throughput capacity constraints: If satisfied, the allocation results of several empty trains and the combination results of several loaded trains will be used as the initial solution; Otherwise, based on satisfying the technical station decomposition capacity constraints and technical station combination capacity constraints, adjust the allocation results of empty trains, the technical stations for decomposing empty trains, the combination results of loaded trains, and the technical stations for combining loaded trains to meet the section throughput capacity constraints. Several initial solutions are randomly generated, and the initial solution set is composed of these initial solutions.

3. A device for dispatching empty cars at the loading end of a heavy-haul railway, used in the method for dispatching empty cars at the loading end of a heavy-haul railway as described in any one of claims 1 to 2, characterized in that, include: Acquisition module: Acquires heavy-haul railway information from the railway dispatching information system; Model building module: Based on the heavy-haul railway information, a heavy-haul railway loading end empty car allocation model is established. The heavy-haul railway loading end empty car allocation model includes a train allocation constraint sub-model and an objective function calculation sub-model. Initial solution set generation module: under the condition of satisfying the train dispatch constraint sub-model, randomly generate the initial solution set of the heavy-haul railway loading end empty car dispatch model, the initial solution set including at least one initial scheme for heavy-haul railway loading end empty car dispatch; Optimization module: Based on the train dispatch constraint sub-model and the objective function calculation sub-model, the initial solution set is optimized using a non-dominated sorting genetic algorithm with an elite strategy to obtain an optimized set of empty car dispatch schemes for the loading end of heavy-haul railways.

4. The heavy-haul railway loading end empty car dispatching device according to claim 3, characterized in that, The initial solution set generation module specifically includes: First generation unit: Under the condition of satisfying the empty train transport organization constraint sub-model, randomly generate several empty train allocation results; First determining unit: The technical station for decomposing empty car trains is determined by the decomposition capability constraint of the technical station. The technical station for decomposing empty car trains decomposes one empty car train into two empty car trains. After the empty car trains are loaded at the loading end, they become a loaded car train. Second determining unit: The technical station for combining heavy trains is determined by the technical station's combination capability constraint, and the technical station for combining heavy trains combines the two heavy trains into one heavy train. First judgment unit: Determine whether the allocation result of empty trains and the combination result of loaded trains meet the section throughput capacity constraints. If satisfied, the allocation results of several empty trains and the combination results of several loaded trains will be used as the initial solution; Otherwise, based on satisfying the technical station decomposition capacity constraints and technical station combination capacity constraints, adjust the allocation results of empty trains, the technical stations for decomposing empty trains, the combination results of loaded trains, and the technical stations for combining loaded trains to meet the section throughput capacity constraints. The second generation unit randomly generates several initial solutions, which together form an initial solution set.

5. A heavy-haul railway loading end empty car dispatching equipment, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for dispatching empty wagons at the loading end of a heavy-haul railway as described in any one of claims 1 to 2.

6. A readable storage medium, characterized in that: The readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for dispatching empty wagons at the loading end of a heavy-haul railway as described in any one of claims 1 to 2.

Images