Goods train marshalling plan optimization method based on section traffic flow incidence relation

Abstract

The invention discloses a freight train marshalling plan optimization method based on a section traffic flow incidence relation, and the method comprises the steps: firstly building a traffic flow path and marshalling scheme comprehensive optimization model, and then building a single-group train marshalling scheme optimization model given by a traffic flow path; and comparing the optimization result of the two-stage optimization method with the optimization result of the comprehensive optimization, verifying the replacement of the two-stage optimization method for the comprehensive optimization method in analyzing the traffic flow organization problem, and after verification, replacing the comprehensive optimization method with the two-stage optimization method and applying the two-stage optimization method to a large-scale instance to obtain a calculation result meeting requirements. The two-stage optimization method provided by the invention can obtain a high-quality approximate optimal scheme when being applied to a large-scale case under the condition of limited resources, thereby effectively accelerating the turnover of the rolling stock and reducing the in-transit transportation time of the traffic flow; the problems that when a comprehensive optimization model is applied, the operation duration is too long, and large-scale cases cannot be solved are solved.

Description

Technical Field

[0001] The invention relates to the technical field of railway transportation, and in particular to an optimization method for a freight train marshaling plan based on a section vehicle flow association relationship. Background Art

[0002] Freight train marshaling plan is a basic document with a long planning period in railway traffic organization work, guiding the transformation of the traffic flow of the entire railway into train flows with different destinations. Specifically, the traffic flow organization plan stipulates the marshaling destination, the attraction range of the destination, the train arrival and departure stations, the train types, etc.; while determining the traffic flow organization plan, it also determines the number of reshuffled cars at each station and the use of shunting lines. Therefore, a reasonable traffic flow organization plan can coordinate the reshuffling tasks of various technical stations on the railway network and alleviate the situation of tight transportation capacity.

[0003] Since the optimization of railway marshaling plans belongs to a large-scale combinatorial optimization problem, it is extremely important to establish a suitable model that is easy to solve. However, there is no good way to optimize railway marshaling plans in the prior art. Conventional methods include a method of optimizing railway marshaling plans by using a model that simultaneously optimizes marshaling plans and vehicle flow paths. When applied to smaller-scale cases, it has a faster solution speed and higher accuracy, but for larger-scale cases, it requires too much computing power and cannot produce results. In practice, there are many larger-scale cases, and the above method cannot effectively solve the problem. There is also a lack of other optimization models to solve the problem. The present invention provides an optimization method for freight train marshaling plans based on the relationship between segment vehicle flows to solve the above problems. Summary of the invention

[0004] The present invention provides an optimization method for freight train marshaling plan based on section traffic association, which is used to solve the problems of railway freight transportation network involving network complexity and freight exchange between important nodes.

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] A method for optimizing freight train marshaling plan based on section traffic association relationship comprises the following steps:

[0007] S1, establish a comprehensive optimization model for vehicle flow routing and marshaling scheme;

[0008] S2, establish a single train marshaling scheme optimization model under a given traffic flow path;

[0009] S3, apply the two models to the same case to optimize the railway network marshaling plan problem, obtain the results respectively and compare them, and verify the accuracy of the results of the single train marshaling plan optimization model under the given traffic flow path;

[0010] S4, optimize the railway network marshaling plan problem using the single train marshaling plan optimization model under given traffic flow paths.

[0011] Furthermore, in step S1, the specific steps of establishing a comprehensive optimization model of vehicle flow routing and formation scheme include:

[0012] S11, clarify the problem boundaries and assumptions;

[0013] S12, clarify the objective function;

[0014] S13, clarify constraints;

[0015] S14, establish a comprehensive optimization model for vehicle flow routing and vehicle formation schemes.

[0016] Furthermore, in step S11, the problem boundaries and assumptions specifically include:

[0017] A01. Assume that section trains must be operated between adjacent marshalling yards. If there are no section trains between adjacent marshalling yards, that is, there is no direct destination between two adjacent marshalling yards, then all traffic from the station needs to bypass the line section. This situation will only occur when the section has no through capacity.

[0018] A02. The operating capacity of a marshaling yard is described by the reorganization capacity and the number of shunting lines. The reorganization capacity determines the number of vehicles that the marshaling yard can handle every day, and the number of shunting lines determines the number of marshaling destinations that the marshaling yard can form. In addition, the disassembly capacity of the marshaling yard matches the marshaling capacity, so the disassembly capacity of the marshaling yard is not considered.

[0019] And when establishing the model, the model needs to reflect the basic path tree constraints in railway traffic organization and the principle that single traffic flow cannot be split.

[0020] The sets, parameters and decision variables in the comprehensive optimization model of railway train flow routing and marshaling scheme under the condition of train flow determination are defined as follows: (I) Sets,

[0021] V: the set of all stations in the railway network;

[0022] V Adj (i): the set of all stations adjacent to station i in the railway network;

[0023] V Non (i): the set of all stations in the railway network that are not adjacent to station i;

[0024] E: the set of all line segments in the railway network;

[0025] E Remain (i,j): The set of remaining segments in the railway network excluding line segment [i,j].

[0026] (ii) Parameters,

[0027] N ij : The original traffic volume from station i to station j, unit: car;

[0028] m ij : Direct destination<i,j> The average number of trains on the train, unit: car;

[0029] c i : The freight train assembly coefficient of marshaling yard i, including all factors that affect the hourly consumption of freight train assembly except the average number of trains m, unit: hour;

[0030] l st : The physical mileage of the line section [s, t]. Since the railway line type considered in the present invention is a single line, the sections [s, t] and [t, s] have the same physical mileage, unit: kilometers;

[0031] L ij : The shortest route distance from station i to station j without considering the capacity limit of the line section, unit: kilometers;

[0032] Λ ij : The total mileage of the route section shared by the detour route and the shortest route from station i to station j, in kilometers;

[0033] λ: unit conversion coefficient of vehicle flow running cost, unit: vehicle hour / vehicle kilometer;

[0034] γ Detour : The relative detour rate or upper limit of the detour rate of the traffic path;

[0035] C st : The maximum throughput capacity of the line section [s, t]. Similarly, the sections [s, t] and [t, s] have symmetrical throughput capacities. Unit: column;

[0036] α st : Line capacity reserve coefficient of single-track railway section [s, t];

[0037] τ k : The reorganization parameter of the marshaling yard k station, that is, the relative delay. When the vehicle is transferred at the technical station, it needs to go through the operations of arrival, technical inspection, dismantling, marshaling, and departure. Before the waiting operation starts, there is also time consumption for waiting for inspection, waiting for dismantling, waiting for marshaling, and waiting for departure. Waiting time is not necessary. When the operation diagram and the station operation process cannot be fully coordinated, waiting time will be generated at this time; in addition, this coefficient also includes the wages of shunting operators and the fuel consumption of shunting locomotives, etc., unit: hour;

[0038] T k : The maximum number of shunting lines at marshalling yard k, i.e. the number of classification lines; a technical station usually has one shunting line for one marshalling destination. When the average daily traffic volume of two marshalling destinations is less than 100 cars, the traffic volume is small, so one shunting line is shared; when the average daily traffic volume of the marshalling destination exceeds 250 cars, the traffic volume is large, and an additional shunting line can be added. Unit: line;

[0039] The number of shunting lines reserved at marshalling yard k, unit: line;

[0040] R k : The maximum reshuffling capacity of marshaling yard k refers to the number of freight cars that can be marshaled in one day and night according to the existing equipment of the marshaling yard. The reshuffling capacity of the marshaling yard and the number of shunting lines are collectively referred to as the operating capacity, unit: car;

[0041] β k : marshaling capacity reserve coefficient of marshaling yard k;

[0042] M: A sufficiently large positive number.

[0043] (iii) Decision variables,

[0044] If the group is going to<i,j> If the physical path of includes the segment [s, t], the value is 1, otherwise the value is 0, which is a Boolean variable;

[0045] Section-traffic association variable, the direction of the train group through the line section [s, t]<i,j> The volume of traffic attracted is a continuous variable;

[0046] If the traffic flow from station i to station j chooses station k as the first forward station for reconfiguration, the value is 1, otherwise the value is 0, which is a Boolean variable; the final destination station j of the traffic flow cannot be used as the reconfiguration station of the traffic flow, that is, k≠j;

[0047] The volume of traffic from station i to station j, which is selected as the first forward station for adaptation, is a continuous variable;

[0048] y ij : If the marshalling destination from station i to station j is provided, the value is 1, otherwise the value is 0. It is a Boolean variable.

[0049] (iv) Auxiliary variables,

[0050] f ij : The size of the combined traffic flow after the traffic flows with the same terminal station j merge at station i, which is a continuous variable;

[0051] S ij:Destination of a single train<i,j> The traffic volume attracted is a continuous variable;

[0052] P ij : The physical mileage of the route of traffic from station i to station j, which is a continuous variable;

[0053] The average daily marshalling workload at marshalling yard k is a continuous variable.

[0054] Furthermore, in step S12, the objective function is composed of three parts: vehicle flow running cost, train assembly cost and vehicle flow reorganization cost.

[0055] The traffic flow cost is shown in formula (1):

[0056]

[0057] Since the traffic flow path is formed by linking the direct train paths that the branch traffic takes, the auxiliary variable P of the traffic flow path is used. ij To calculate the route mileage of each vehicle flow, the expression is as follows:

[0058]

[0059] It is a group destination path variable used to describe the direct destination.<i,j> The physical path of

[0060]

[0061] According to whether the traffic flow is transported to the final destination in a direct or indirect way, there are two methods for calculating the mileage of the traffic flow path. For direct traffic flow whose origin and final destination are the starting and ending points of a certain marshaling destination respectively, its path is the same as the physical path of the marshaling destination, and is composed of the arcs that constitute the physical path of the destination, which is expressed by formula (2);

[0062] For direct traffic without corresponding marshaling destination, traffic flow N ij First, the reconfiguration operation is performed at station k, and the traffic flow path from station i to station j is P ik and P kj The composition is expressed by formula (3).

[0063] Train assembly cost: The assembly vehicle hours consumed by all marshaling yards on the railway network in one day and night, as shown in formula (4):

[0064]

[0065] Traffic flow adaptation cost: The traffic flow adaptation cost of the marshaling yard is obtained by multiplying the adaptation parameter and the adapted traffic volume. The total cost of traffic flow adaptation in all marshaling yards on the road network is shown in formula (5):

[0066]

[0067] In formula (5) is the average daily marshaling workload undertaken by marshaling yard k, As shown in formula (6),

[0068]

[0069] In formula (6), the actual vehicle flow f starting from station i and ending at station j is ij The original transportation demand N from station i to station j ij and the traffic flow from station h, the rear station of station i, and the final destination is station j and is reorganized at station i Composition; if the traffic flow f ij If the rescheduling operation is performed at station k, the traffic flow will be rescheduling. The value of is equal to the actual traffic flow f ij If not, then The value of is equal to 0;

[0070] The traffic flow is only disassembled at the terminal station, and no reorganization is performed. Therefore, the reorganization station of the traffic flow cannot be its terminal station. That is, only the traffic flow between two non-adjacent stations can be reorganized on the way. Therefore, when reorganization occurs, the terminal station j of the traffic flow belongs to the set V Non (i).

[0071] Reconfigure traffic flow and the actual traffic flow f ij The conversion relationship between the two is given by the following formula:

[0072]

[0073] Combining equations (7) and (8), we can get the modified traffic flow: The calculation formula is as follows:

[0074]

[0075] When formula (9) is expressed in iterative form, there is a traffic flow reconfiguration chain Then the modified traffic flow from station i to station j is As shown in formula (10),

[0076]

[0077] If the traffic flow ij Can be programmed into direct destinations<i,j> In the above equation, the traffic flow fij That is the destination<i,j> Part of the traffic flow intensity; for the traffic flow with the starting station as station i and the final station as station k, and the reorganization operation is performed at station j According to the traffic merging principle, this traffic also needs to be included in the direct destination.<i,j> Therefore, the traffic flow S attracted by the marshaling direction ij , that is, the traffic intensity is as shown in formula (11),

[0078]

[0079] Furthermore, in step S13, the constraints include path continuity and integrity constraints, station operation capacity constraints, vehicle flow reorganization constraints, line section capacity constraints, vehicle flow path mileage constraints and flow balance constraints.

[0080] In the path continuity and integrity constraints, since the comprehensive optimization model is designed for the component arcs of the direct column flow, that is, the comprehensive optimization model is designed for the component arcs of the physical path of the marshaling destination, it is necessary to set constraints to ensure the path continuity and integrity.

[0081]

[0082] Formula (12) ensures the direction of the group<i,j> The physical path starts from station i, and equation (13) guarantees the direction of the marshaling<i,j> The physical path ends at station j;

[0083] The two constraints of equations (12) and (13) only determine the starting arc and the ending arc of a certain physical path. The relevant constraints that constitute the middle part of the path are expressed by equation (14):

[0084]

[0085] For any marshaling station t on the network, if the station is in the marshaling direction<i,j> If the station t is not on the physical path of the marshaling, the in-degree and out-degree of the station t are both equal to 1; if the station t is not in the marshaling direction<i,j> If the physical path of station t is on the same physical path, the in-degree and out-degree of station t are both equal to 0; since it is impossible to determine whether station t is on the destination

[0086] <i,j> Therefore, the out-degree of station t is set equal to the in-degree through equation (14), covering the above two possible situations;

[0087] Formula (2) and Formula (3) determine that the traffic flow path is composed of the direct column flow path. On this basis, the constraints Formula (12), Formula (12) and Formula (14) restrict the traffic flow relocation station to be on its traffic flow path.

[0088] Due to the direct path variable There are impossible values, so set a mandatory constraint and set the value of the impossible variable to 0;

[0089]

[0090] Formula (15) indicates that the non-direct traffic generated between adjacent marshaling yards i and j must choose the arc segment [i, j] linking the two stations for transportation;

[0091] Formula (16) indicates that for the traffic flow from station i to station j, other arcs on the road network will not appear in its travel path;

[0092] Formula (17) indicates that if a marshaling has station i as its starting station and station j as its final destination, the arcs that make up its physical path do not end at station i or start at station j;

[0093] Formula (18) indicates that a physical path is arranged only when the marshaling destination exists; if the traffic flow N ij The reorganization at station k indicates that the traffic flow did not take the direct train from station i to station j, that is, there is no direct destination from station i to station j.<i,j> .

[0094] Among the station operating capacity constraints, the marshalling yard's operating capacity is specifically manifested in the effective shunting capacity and the number of available shunting lines.

[0095] The station adaptation capacity constraint is expressed by equation (19):

[0096]

[0097] Only the traffic flow that is reorganized at station k is included in the summation. If the final destination of the traffic flow is the same as the final destination of the marshaling, the traffic flow will only be disassembled at this station and will not occupy the reorganization capacity.

[0098] The constraint on the number of shunting lines at a station is expressed by equation (20):

[0099]

[0100] In formula (20), ψ(S ij ) is the group destination<i,j> The number of tracks that the attracted traffic needs to occupy has the following piecewise continuous linear form:

[0101]

[0102] The parameters a1, a2, …, a in formula (21) are n The value of depends on the infrastructure and labor level of the station, usually a1=200, a2=400,…,a n =200n, that is, each shunting line can handle an average of 200 cars per day and night.

[0103] Formula (21) is valid only when any two marshaling destinations do not share a shunting line. When the traffic volume attracted by the two marshaling destinations is small, they share a shunting line, which is expressed by Formula (22):

[0104]

[0105] In formula (22), H[200-S ik -S ij ] is the Heaviside step function.<i,j> and<i,k> When the sum of the daily average traffic intensities on is greater than 200 vehicles, the step function takes the value of 0; in other cases, the value is 1.

[0106] Traffic flow reorganization constraints: The traffic flow grouping and destination linking scheme on the road network needs to be coordinated with the grouping and destination scheme; if a certain destination does not exist, the traffic flow cannot be reorganized into that destination;

[0107]

[0108] Formula (23) is used to determine the traffic flow N ij Can the forward reorganization station be station k: If there is a marshaling destination<i,k> , then the traffic may be reorganized at station k; if there is no marshaling direction<i,k> , then the traffic flow cannot be reorganized at station k.

[0109]

[0110] Formula (24) is the uniqueness constraint of the vehicle flow grouping scheme, that is, if the vehicle flow from station i to station j is transported directly to the final destination station j, then the corresponding grouping destination variable y ij The value is 1; if you choose to reorganize at station k on the way, the corresponding group destination link variable The value is 1.

[0111] Line section capacity constraint: It includes the line section capacity and station capacity. The line section capacity is a necessary factor to be considered when optimizing the traffic flow path. The station capacity is also one of the decisive factors affecting the traffic flow path selection. The station capacity can be decomposed into access capacity and departure capacity. In the process of model construction, the station capacity constraint is absorbed into the arc capacity connected to it. Therefore, only the line section capacity constraint is considered, which is expressed by formula (25):

[0112]

[0113] The line section capacity of the present invention is measured in terms of the number of trains. Therefore, it is necessary to use the average train formation number parameter to convert the traffic intensity in units of vehicles into the number of trains, and then compare it with the section capacity.

[0114] Traffic flow route mileage constraint: Due to the capacity constraints of the line sections on the road network, some traffic flows need to choose detour routes to avoid bottleneck sections. In order to control the mileage of the detour routes, the detour rate threshold of the traffic flow route is introduced to control the mileage of the detour route. Only the traffic flow routes within the detour rate threshold are considered reasonable routes, including relative detour rate constraints and detour rate constraints;

[0115] The relative detour rate constraint is expressed by equation (26):

[0116]

[0117] Formula (26) is used to limit the mileage and eliminate unreasonable paths. For example, unreasonable paths include repeated operation of vehicles in certain sections, or the direction of travel is contrary to the transportation direction. However, only when the detour path of the vehicle flow can be determined, it can be compared with the shortest path and the common section mileage of the two can be obtained. That is, this constraint is only applicable to preprocessing the model solution space or post-adjustment of the optimization results.

[0118] Since the traffic flow route mileage constraint has been embedded in the planning model and is not used as a pre-processing condition or post-adjustment condition, only the detour rate constraint is considered in the traffic flow route mileage constraint, which is expressed by formula (27):

[0119]

[0120] Flow balance constraint: One of the key constraints in the comprehensive optimization model of vehicle flow routing and formation scheme is the flow balance constraint. According to the design of decision variables in the model, there are two kinds of unknown flows in the road network: one is the arc flow The other is the marshalling yard to adapt the flow The actual traffic flow involved in the auxiliary variable f ij and the outbound traffic intensity S ij It can be expressed by decision variables, so no auxiliary traffic flow variables are used to describe the flow balance constraints, and the arc flow on the road network The equilibrium constraint is expressed by equation (28):

[0121]

[0122] Formula (28) shows that the direction of the group on the arc segment [s, t] is<i,j> The traffic flow of station i to station j consists of three parts: the original traffic flow N ij ; The traffic flow starts from the station behind station i, ends at station j and is reorganized at station i The traffic flow starts at station i and ends at the station before station j and is reorganized at station j If the above three types of traffic flow are included in the direct destination with the final destination being station j at station i, and the transportation path of this destination passes through the arc segment [s, t], then is the sum of these three types of traffic flow; if the direct destination does not pass through the arc [s, t], or these traffic flows are incorporated into a direct train whose final destination is not station j, then is equal to 0. Fig.15 The direct destination of the arc segment [s, t] shown<i,j> Attracting traffic composition.

[0123] In addition to the flow balance constraint on the arc segment, the flow in and out of a certain node is constrained by equation (29):

[0124]

[0125] Formula (29) shows that if the group direction<i,j> The physical path passes through station t, then the traffic flow from station i to station j received by station t is equal to the traffic flow from station i to station j sent out in that direction; if the marshaling direction<i,j> If the physical path does not pass through station t, the vehicle flow from station i to station j received and sent by station t is equal to 0.

[0126] Furthermore, a comprehensive optimization model Model-1 of vehicle flow routing and formation scheme is established, and its specific features are as follows:

[0127]

[0128] Constraints:

[0129]

[0130]

[0131] Furthermore, the constraints of the comprehensive optimization model Model-1 are modified as follows:

[0132] E Potential (i,j): the destination of the marshaling in the road network<i,j> The set of arcs that can be selected for the physical path of the vehicle flow can be formed by the path of the marshaling it takes. Therefore, when the vehicle flow path is determined, the physical path of the marshaling destination is also determined. If the vehicle flow path from station i to station j is given as i→k→l→j, then the marshaling destination<i,j> Only arcs [i,k], [k,l], and [l,j] can be selected for transportation;

[0133] E Eliminate (i,j): the destination of the marshaling in the road network<i,j> The set of arcs that cannot be included in the physical path is equal to the set E of all arcs on the road network minus the available directions for grouping<i,j> The arc set E selected by the path Potential (i,j), that is, E Eliminate(i,j)=EE Potential (i,j);

[0134] V Potential (i,j): the set of first forward reconfiguration stations that the traffic flow from station i to station j may choose, excluding stations i and j. Taking the traffic flow from station i to station j as an example, if its traffic flow path is the same as above, the first forward reconfiguration stations that the traffic flow can choose are stations k and l. In addition, the traffic flow only undergoes disassembly operations at its terminal station, so station j cannot be used as a reconfiguration station for this traffic flow.

[0135] V Eliminate (i,j): The set of first forward reconfiguration stations that the traffic from station i to station j cannot choose, which is equal to the set V of all stations on the road network minus the traffic flow N ij Possible first forward reconfiguration station set V Potential (i,j), that is, V Eliminate (i,j)=VV Potential (i,j);

[0136] According to the above constraint set, some constraints of the comprehensive optimization model Model-1 are reconstructed.

[0137]

[0138] Formula (30) is an improvement of formula (12), formula (31) is an improvement of formula (13), formula (32) is an improvement of formula (18), formula (33) is an improvement of formula (23), and formula (34) is an improvement of formula (24);

[0139] And add mandatory constraints,

[0140]

[0141] Formula (35) eliminates the grouping direction<i,j> The arc segment that cannot be selected by the physical path;

[0142] Formula (36) eliminates the traffic flow N ij The first forward relocation station that cannot be selected, combined with equations (33) and (34), makes the traffic flow N ij Only one station can be selected as the first forward rescheduling station on its given route, or the branch traffic flow can be incorporated into a direct destination at the departure station without rescheduling to the final destination station;

[0143] Formula (37) indicates that if there is a group destination<i,j> , then the number of arcs contained in the physical path of the destination is equal to the given traffic flow N ij The number of arcs contained in the walking path; if there is no grouping destination<i,j> , then the corresponding direct column flow path variable is equal to 0;

[0144] Formula (38) shows that the symbol The meaning is that in a given traffic flow routing scheme, traffic flow N ij The mileage of the running route; let the vehicle flow route mileage P formed by the route of the marshaling destination be ij Equal to the given route mileage of the branch traffic flow It can further ensure that the traffic flow path can maintain consistency before and after optimization after being used as input data for the optimization problem of the formation plan;

[0145] Formula (39) is the traffic flow intensity consistency constraint condition, symbol Represents the traffic intensity on the arc segment [s, t] under a given traffic routing scheme.

[0146] Furthermore, the comprehensive optimization model Model-1 is modified to adapt to the optimization problem of the train formation plan under the given train flow path, and a single train formation plan optimization model Model-2 based on the train flow path is constructed. The specific features are as follows:

[0147]

[0148] Constraints:

[0149]

[0150]

[0151] The beneficial effects of the present invention are as follows:

[0152] The railway network marshaling scheme problem is optimized based on the consideration of the network capacity constraints and the tree-like characteristics of the routes. The two-stage optimization method adopted is able to obtain an approximate optimal solution that meets the requirements when the solution time is too long or the resources are limited, compared with the marshaling scheme under comprehensive optimization. It can effectively accelerate the turnover of locomotives and vehicles and reduce the in-transit transportation time of vehicles, and effectively solve the problems of complex network and cargo exchange between important nodes involved in the railway freight transportation network. BRIEF DESCRIPTION OF THE DRAWINGS

[0153] Figure 1 A schematic diagram of a railway network according to an application embodiment of the present invention;

[0154] Figure 2 The parameter table of each support station of the railway network of the present invention;

[0155] Figure 3 The mileage and throughput capacity table of each line section of the road network of the present invention;

[0156] Figure 4 The shortest path mileage table between the road network support stations of the present invention;

[0157] Figure 5 A schematic diagram of a service network under comprehensive optimization of the present invention;

[0158] Figure 6 The traffic intensity table of each marshaling in the direction of the present invention;

[0159] Figure 7 The operating load table of each station of the present invention;

[0160] Figure 8 The table of capacity utilization of each line section of the present invention;

[0161] Fig. 9 A table of adapted strategies for each vehicle flow under the comprehensive optimization of the present invention;

[0162] Fig.10 A schematic diagram of a service network for two-stage optimization of the present invention;

[0163] Fig.11 The traffic intensity table of each marshaling direction under the two-stage optimization of the present invention;

[0164] Fig.12 It is a table of operating load conditions of each station under the two-stage optimization of the present invention;

[0165] Fig.13 It is a table of the utilization of the throughput capacity of each line section under the two-stage optimization of the present invention;

[0166] Fig.14 A table of adapted strategies for each vehicle flow under the two-stage optimization of the present invention;

[0167] Fig.15 The direct destination of the arc segment [s, t] of the present invention is<i,j> Schematic diagram of the attracted traffic composition. DETAILED DESCRIPTION

[0168] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0169] In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0170] For the comprehensive optimization of marshaling plan and traffic flow path, the required results can be simply summarized as traffic flow reorganization strategy and traffic flow path selection. Then the more intuitive way to design decision variables is to describe whether a specific traffic flow needs to be reorganized and whether the traffic flow chooses a certain arc segment for transportation. Taking into account the reorganization capacity of the marshaling yard, the number of shunting lines, and the line capacity constraints, the comprehensive optimization model of marshaling plan and traffic flow path is obtained. The comprehensive optimization model jointly optimizes the railway freight train marshaling plan and traffic flow path under the condition of determined parameters, and can obtain more accurate calculation results. The present invention establishes a comprehensive optimization model of vehicle flow paths and marshaling schemes in the first stage, and then obtains a single train marshaling scheme optimization model under a given vehicle flow path by considering the marshaling yard's reconfiguration capacity, the number of shunting lines, and the line's through capacity constraints in the second stage. Then, the marshaling plan optimization result under a given vehicle flow path is compared with the optimal marshaling plan obtained under comprehensive optimization to verify whether the two-stage optimization method proposed in the present invention can replace the comprehensive optimization method in analyzing vehicle flow organization problems. After verification, the two-stage optimization method is applied to large-scale examples instead of the comprehensive optimization method, and the large-scale cases are optimized to obtain calculation results that meet the requirements. The two-stage optimization method proposed in the present invention can obtain a high-quality approximate optimal solution when applied to large-scale cases under limited resources, effectively accelerate the turnover of locomotives and vehicles and reduce the in-transit transportation time of vehicles, and solve the problem of too long calculation time when the comprehensive optimization model is applied and cannot be used to solve large-scale cases.

[0171] A method for optimizing freight train marshaling plan based on section traffic association relationship comprises the following steps:

[0172] S1, establish a comprehensive optimization model for vehicle flow routing and marshaling scheme;

[0173] First, clarify the problem boundary and assumptions, clarify the objective function and clarify the constraints, and then establish a comprehensive optimization model for traffic flow routing and formation schemes;

[0174] S2, changing the constraint conditions of the comprehensive optimization model of the vehicle flow path and the marshaling scheme, and establishing a single train marshaling scheme optimization model under a given vehicle flow path based on the comprehensive optimization model of the vehicle flow path and the marshaling scheme;

[0175] S3, optimize the railway network marshaling scheme problem of the same case using the single train marshaling scheme optimization model under given traffic flow path and the comprehensive optimization model of traffic flow path and marshaling scheme, obtain the results respectively, and compare the optimization results of the two to verify whether the optimization result of the single train marshaling scheme optimization model under given traffic flow path meets the accuracy requirements. If it meets the requirements, the single train marshaling scheme optimization model under given traffic flow path can be used to replace the comprehensive optimization model of traffic flow path and marshaling scheme to optimize large-scale examples;

[0176] S4, after verification, uses the single train marshaling scheme optimization model under given traffic flow path to optimize the railway network marshaling scheme problem.

[0177] The comprehensive optimization model of marshaling plan and traffic flow path obtained under the constraints of marshaling yard reorganization capacity, number of shunting lines and line passing capacity has the advantage of high optimization result accuracy. However, due to the large order of magnitude of its constraints, it requires extremely high computing power, which will greatly prolong the calculation process. Even when optimizing large-scale cases, it will not be able to obtain results due to excessive computing power requirements. Therefore, on its basis, a single train marshaling scheme optimization model with given traffic flow path is established. After obtaining the two optimization models, it is verified whether the optimization results of the single train marshaling scheme optimization model with given traffic flow path meet the accuracy requirements compared with the optimization results of the comprehensive optimization model of marshaling plan and traffic flow path. If the accuracy requirements are met, the single train marshaling scheme optimization model with given traffic flow path with higher computational efficiency can be used instead of the comprehensive optimization model of marshaling plan and traffic flow path to optimize large-scale cases. At this time, the optimization results can be obtained, and since the optimization results of the two-stage optimization model have been compared and verified, its optimization results also meet the requirements.

[0178] The comprehensive optimization model of vehicle flow routing and formation scheme Model-1 established through the above steps has the following specific features:

[0179]

[0180] Constraints:

[0181]

[0182] The comprehensive optimization model of railway vehicle flow routing and marshaling scheme Model-1 is a mixed integer nonlinear programming. The model contains many types of constraints, and the number of constraints actually generated is also considerable. Assuming that the railway network consists of n nodes and m arcs, the number of decision variables generated will reach (2n 3 -2n 2 +2mn 2+3n-2mn). According to the number of edges and points on the road network, the average degree of the node is 2m / n. For any point in the road network, the average number of nodes adjacent to it is 2m / n, and the average number of nodes not adjacent to the point is (n-2m / n). For the path continuity and integrity constraint group, that is, equations (12) to (18), (n 3 +n 2 +2m 2 -2mn-2m) equations and (n 2 -n) inequalities; for the station operation capacity constraint condition group, that is, equations (19) to (20), 2n inequalities will be generated; for the vehicle flow reorganization constraint condition group, that is, equations (23) to (24), (n 2 -n) equations and (n 2 -n) inequalities; for the section capacity constraint group, that is, formula (25), m inequalities will be generated; for the traffic route mileage constraint, that is, formula (27), (n 2 -n) inequalities; for the flow balance constraint condition group, that is, equations (28) to (29), (n 3 -3n 2 +mn 2 +2n-mn) equations. It can be seen that the number of equation constraints generated by Model-I under this road network scale will reach O(n 3 ) level, the number of inequality constraints will reach O(n 2 ) level. As the scale of the road network gradually expands, the number of decision variables and constraints of the model increases exponentially. Therefore, in the actual application of the calculation process, the calculation process will be greatly prolonged, and the accuracy of the data will be greatly affected, and even the computing power requirement will be too large to obtain the result.

[0183] Therefore, based on the comprehensive optimization model of railway train flow path and marshaling scheme Model-1, it is modified to adapt to the optimization problem of marshaling scheme under given train flow path, and a single train marshaling plan optimization model Model-2 based on train flow path is constructed. The specific features are as follows:

[0184]

[0185] Constraints:

[0186]

[0187]

[0188] Since the travel cost Z in the objective function of Model-2 TravelThis item is a constant. In order to distinguish the optimal objective function value obtained by the comprehensive optimization of the traffic flow route and the marshaling scheme and the optimization of the marshaling plan under a given traffic flow route, the running cost is retained in the objective function of Model-2. In addition, since the traffic flow route plan of the single train marshaling plan optimized under a given traffic flow route is known information, the optimization result of the marshaling plan must ensure that the traffic flow intensity on the arc segment is consistent with the traffic flow intensity on the arc segment in the traffic flow route plan. Therefore, the arc capacity constraint is omitted in Model-2, and the constraint condition formula (39) is added.

[0189] The comprehensive optimization method used by Model-1 has acceptable computational efficiency and accuracy when applied to small-scale cases, but for large-scale cases, the computation time is too long or no results can be obtained, so it cannot meet the needs. Model-2 has comparable computational efficiency and accuracy with the comprehensive optimization method in small-scale cases, but for large-scale cases, it can greatly reduce the computational time and has similar high-precision solution quality.

[0190] A specific application example of this embodiment. Taking the east-west branch network of my country's railway as the background, a total of 11 marshalling yards were selected as branch stations, forming a small-scale branch network containing 13 line sections. The east-west network consists of part of the Longhai Line and part of the Lanxin Line. Considering that the easternmost marshalling yard of the channel is the Xuzhou North marshalling yard and the westernmost is the Wuxi marshalling yard. Without loss of generality, the network of this case selects the main sections of the channel and some related parallel channels. The network structure is as follows Figure 1 shown.

[0191] Will Figure 1 The 11 fulcrum stations are numbered, and the parameters in brackets in the figure are the corresponding numbers of the stations. Figure 2 The parameter values ​​related to each pivot station in the east-west pivot road network are given, including assembly coefficient, adaptation parameters and operating capacity. It is assumed that the adaptation capacity reserve coefficient of each pivot station is 15%, and the number of shunting lines reserved for local operating vehicles is set to 2. The operating capacity of each pivot station on this road network varies greatly, and the scale of the pivot stations varies greatly. The station with the largest available adaptation capacity is Lanzhou North Station-Y5 Station, which is 2134 cars, and the station with the smallest available adaptation capacity is Wuxi Station-Y 11 The average available adaptation capacity of all stations is 524 cars.

[0192] Figure 3The capacity parameters and mileage information of each line section on the network are listed. Assuming that the network is a single-track railway, the line mileage and capacity in the up and down directions of the same section are symmetrical and equal, and the capacity reserve coefficient is set to 20%. The degree of separation of stations on the network varies. The farthest distance between adjacent branch stations is from Wuwei South Station to Hami East Station-Y9 Station to Y 10 Station, which is 1,037 kilometers. The shortest distance is from Baoji East Station to Pingliang South Station - Y4 Station to Y7 Station, which is 186 kilometers. The average mileage of the arc is 385.54 kilometers.

[0193] Figure 4 is the transportation demand data of the east-west road network, where the vertical columns are the departure stations and the horizontal columns are the final destination stations. A total of 121 original transportation demands are formed in the east-west road network, of which there are 110 non-zero traffic flows, and 11 traffic flows are generated inside the fulcrum station. Since this embodiment does not consider the path problem of traffic flow inside the marshaling yard, the flow of these 11 traffic flows is set to 0.

[0194] In addition, the parameter values ​​involved in the objective functions of Model-1 and Model-2 are as follows: the conversion coefficient λ between vehicle hours and vehicle kilometers is set to 0.1 vehicle hours / vehicle kilometers;<i,j> The average number of trains in a direction is m ij The number of cars is set to 50. In addition, it is necessary to assign a value to the parameter M in the constraint condition (18). The value of the parameter M in (18) is set to 10. 6 ; In formula (21), the number of vehicles that each shunting line can handle in one day and night is set to 200 vehicles; in formula (25), the threshold of the detour rate of vehicle flow is set to 1.55, such as Figure 4 shown.

[0195] The optimization result of the comprehensive optimization model of traffic flow routing and marshalling scheme, that is, the optimization result of comprehensive optimization.

[0196] Based on the above parameters, Gurobi built-in simplex algorithm was used to solve Model-1. The solution process took 11 seconds, and the optimal objective function value was 1177798.6 vehicle hours, where the vehicle flow cost Z Travel The vehicle hours are 1132275.0, and the assembly cost is Z Accum For 29995.0 vehicle hours, the adaptation cost is Z Transfer It is 16156.0 vehicle hours.

[0197] In the traffic flow routing scheme under comprehensive optimization, a total of 9 traffic flows adopt detour routes, namely N 1,9 、N 1,10 、N 1,11 、N 4,9 、N 4,10 、N4,11 、N 5,7 、N 11,3 Among them, the traffic flow with the largest detour rate is N 5,7 , reaching 1.18. The road network marshaling distribution under comprehensive optimization is as follows Figure 5 As shown in the figure, the marshaling direction is represented by a blue dotted line with an arrow. Since section trains must run between adjacent branch stations, the marshaling direction between adjacent branch stations is called the must-run direction. This case consists of 13 line sections, forming a total of 26 must-run directions. In practice, some of the directions corresponding to the must-run directions are recorded as prohibited directions. The traffic volume in these directions is small or the number of weight changes is too many, so they are manually selected as prohibited directions. Before comprehensive optimization, the must-run directions and prohibited directions are used as preprocessing conditions of the solution space, which reduces the model scale while integrating manual experience with computer optimization, thereby improving the reliability and practicality of the optimization scheme. In addition to the must-run directions, there are 26 optimized directions on the service network. Due to the limitations of the line section's through-capacity and the station's operating capacity, the physical paths of these optimized directions cannot all select the shortest path. Marshalling direction<Y7,Y5> The physical path detour rate is the highest, at 1.18; <Y2,Y 11 >The route detour rate is 1.17; <Y 11 ,Y4> has a detour rate of 1.09;<Y9,Y4> The detour rate of the route is 1.09. Except for these four optimized destinations that use detour routes, the rest of the destinations are transported via the shortest route.

[0198] The traffic intensity of each marshaling group in the direction under comprehensive optimization is listed in Figure 6 Among them, the destination<Y2,Y3> The largest number of vehicles was attracted, reaching 596 vehicles;<Y5,Y8> The traffic volume attracted is the smallest, only 39 cars. The average traffic intensity of all destinations is 265.69 cars. The pivot station Y3 has the largest number of departures, which is 9; the Y5 and Y8 stations have the largest number of arrivals, which is 7. Generally speaking, a large number of departures means a large number of shunting lines are occupied. Therefore, it is necessary to pay attention to whether the number of tracks occupied by Y3 station due to shunting operations exceeds the number of available shunting lines.

[0199] The adaption capacity load and shunting line usage of the fulcrum station are listed in Figure 7 . It can be seen that the Y1 station, Y6 station and Y 11The reshuffling load of the stations is 0. This is because these three pivot stations are located at the edge of the road network and can only serve as departure or arrival stations for traffic, and cannot be used as reshuffling stations on the way. The average reshuffling load of each pivot station is 405.45 vehicles, and the average capacity utilization rate is 57.91%. Among them, the reshuffling capacity utilization rate of the pivot station Y3 is the highest, reaching 99.92%. And according to the above analysis, Y3 station needs to organize the most departures and destinations. Therefore, the number of shunting lines used at Y3 station is the highest, reaching 12, but it does not exceed the limit on the number of available shunting lines.

[0200] The capacity utilization of each line section on the road network is listed in Figure 8 The two line sections with the highest capacity utilization are [Y7, Y8] and [Y9, Y8], reaching 99.80%; the line section with the lowest capacity utilization is [Y4, Y3], only 54.80%; the line section with the highest load is [Y9, Y 10 ], reaching an average of 37.6 trains per day, with a corresponding utilization rate of 94.00%; the line section with the lowest load is [Y8,Y5], with only 2.9 trains, with a corresponding utilization rate of 58.00%.

[0201] Combination Figure 7 and Figure 8 It can be seen that the optimal solution under the comprehensive optimization of the traffic flow path and the single train formation plan can meet the operation capacity limit of the support station and the passing capacity limit of the line section. Under the comprehensive optimization plan, the reorganization strategy of each traffic flow is listed in Fig. 9 .

[0202] Fig. 9 Given the comprehensive optimization scheme, the group destination link variable with a value of 1 in Model-1 is and the group destination variable y ij .pass Fig. 9 For example, when the ordinate is Y1 station and the abscissa is Y2 station, the corresponding pivot station is Y2 station, that is, y 12 =1, indicating that the traffic flow N 1,2 Transport directly to the final destination; when the ordinate is Y1 station and the abscissa is Y4 station, the corresponding fulcrum station is Y3 station, that is Description of traffic flow N 1,4 First, it was adapted at the Y4 station. Figure 6-9 11-13 can present the marshaling and destination connection plan for each traffic flow. 5,11 The adaptation strategy is Y 11 station, which means that the traffic flow N 5,11 The vehicle flow N is obtained by directly transporting the vehicle to the final destination without any reshuffling. 1,11 The marshaling destination connection plan is The traffic flow modification station Y5 is on its traffic flow route, which shows that the traffic flow modification plan can be well coordinated with the traffic flow routing plan.

[0203] The optimization results of the single train formation optimization model under a given traffic flow path, that is, the optimization results of the two-stage optimization.

[0204] The built-in simplex algorithm of Gurobi is also used to solve Model-2. Under the same configuration environment, the solution process takes 9 seconds in total, and the traffic assembly cost is 28895.0 vehicle hours and the adaptation cost is 16978.4 vehicle hours.

[0205] The single train service network under the two-stage optimization consists of 50 marshaling destinations, and its overall distribution is as follows: Fig.10 Compared with the service network under the comprehensive optimization solution, the number of destinations is reduced. <Y2,Y 11 >、<Y3,Y6> ,<Y3,Y9> ,<Y6,Y8> ,<Y8,Y1> ,<Y8,Y5> ,<Y9,Y2> , <Y 11 ,Y4>, added destination<Y2,Y5> ,<Y5,Y7> ,<Y8,Y2> ,<Y8,Y6> ,<Y9,Y1> , <Y 10 ,Y3>. Due to the limited operating capacity of the fulcrum station and the limited throughput capacity of the line section, some direct destinations need to be transported by detour routes. In the two-stage optimization plan, a total of 17 marshaling destinations choose detour transportation, including<Y3,Y8> and whereabouts<Y8,Y3> The physical path has the highest detour rate, which is 1.54, and its path plans are Y3→Y7→Y8 and Y8→Y7→Y3 respectively.

[0206] The train flow intensity of the marshaling direction under the two-stage optimization is listed in Fig.11 The average traffic intensity for all directions is 281.08 vehicles, of which<Y6,Y5> The traffic intensity attracted was the highest, with 669 vehicles;<Y8,Y5> The traffic intensity attracted was the lowest, only 45 cars; the number of departures and destinations formed by the pivot station Y5 was the largest, 8; the number of departures and destinations formed by the Y6 station was the smallest, only 2; the number of arrivals received by the Y3 and Y5 stations was the largest, 7; 11 The station receives the least number of arrival destinations, only 2.

[0207] The utilization of the reorganization capacity of each support station and the occupancy of the shunting line under the two-stage optimization are listed in Fig.12 The utilization rates of the adapted capacity of the Y2 and Y9 fulcrum stations reached 98.22% and 98.60% respectively, close to full capacity. Similarly, the Y1, Y6 and Y 11The station is located at the end of the branch road network and can only be used as a traffic disassembly station, not a traffic reorganization station. Therefore, the corresponding reorganization capacity utilization rate is 0.00%. Fig.11 From the analysis, it can be seen that the fulcrum station Y5 has the largest number of departure and destinations, so the number of shunting lines required to marshal direct trains departing from this station is also relatively large. Fig.12 It can be found that the number of shunting lines in use at Y5 station is 13, which is at a higher level compared with other stations; the number of remaining shunting lines at the terminal station Y1 is only 1, indicating that the traffic intensity from this station is relatively high relative to the number of available shunting lines at this station.

[0208] The utilization rate of each line section's throughput capacity under the two-stage optimization is listed in Fig.13 Among them, the line sections with higher capacity utilization are [Y7, Y8], [Y3, Y4], and [Y4, Y5], reaching 98.80%, 98.90%, and 95.90% respectively; the line sections with lower capacity utilization are [Y4, Y3], [Y5, Y8], and [Y9, Y5], reaching 65.00%, 65.60%, and 65.70% respectively. The line section with the highest load is [Y9, Y 10 ], reaching an average of 37.6 trains per day, or 1,880 cars per day, with a corresponding capacity utilization rate of 94.00%; the line section with the lowest load is [Y5, Y8], with an average of only 3.3 trains per day, or 165 cars per day, with a corresponding capacity utilization rate of 65.60%.

[0209] according to Fig.14 The adaptation strategy for each traffic flow under two-stage optimization can be derived. 11,1 The marshaling destination connection plan is first Fig.14 Find the horizontal coordinate Y 11 The vertical coordinate is the station corresponding to Y1, so we know that the traffic flow N 11,1 The first forward station is Y9. 11,1 In Y 11 Station included in the destination <Y 11 ,Y9>, the transportation plan for this traffic flow after arriving at Y9 station needs to be further explored. Fig.14 The station with Y9 as the horizontal coordinate and Y1 as the vertical coordinate corresponds to is known as the traffic flow N 9,1 Enter a direct route at Y9 station<Y9,Y1> , and the vehicle flow reaches its final destination without any reorganization. Thus, we can get the vehicle flow N 11,1 The marshaling destination connection plan is

[0210] contrast Fig. 9 , it can be found that the traffic flow N under the comprehensive optimization scheme 11,1Only one adaptation was carried out at Y8 station, and its adaptation strategy was Although the marshaling destination plan under the two-stage optimization also includes the destination <Y 11 ,Y8>, but traffic N 11,1 The optimal path is Y 11 →Y 10 →Y9→Y5→Y4→Y3→Y2→Y1. Combined Figure 6-9 , 11-14 shows that Y 11 The station has three direct destinations, namely <Y 11 ,Y8>、 <Y 11 ,Y9>、 <Y 11 ,Y 10 >, so this traffic flow can only choose Y9 station or Y 10 This also shows that the optimization of the marshaling scheme under a given traffic flow path has certain limitations and it is difficult to obtain the overall optimal solution.

[0211] Comparative analysis of optimization results.

[0212] In the comprehensive optimization results, since the traffic can more flexibly choose the appropriate route for transportation, the design of the marshaling destination scheme on the road network is also more diverse. And in theory, the total cost corresponding to the optimal solution under comprehensive optimization will not be higher than the total cost corresponding to the optimal solution under two-stage optimization. When applied to the above-mentioned small-scale case, the optimal objective function value under comprehensive optimization is 1177798.6 vehicle hours, and the optimal objective function value under two-stage optimization is 1180815.8 vehicle hours. The gap between the two is only 0.26%. From the results, the optimal objective function values ​​obtained by the two optimization methods are not much different, indicating that when the problem scale is large and the comprehensive optimization solution speed is slow, the two-stage optimization method can be used instead of the comprehensive optimization method to obtain a high-quality approximate optimal solution to the problem. When applied to large-scale cases, the number of decision variables and constraints in the model of comprehensive optimization increases exponentially during operation, which greatly increases the operation time. When the road network scale increases to 17 pivot stations, the simplex algorithm terminates after 1953 seconds. At this time, the comprehensive optimization cannot produce results and requires a significant increase in computing power. At this time, the solution time of the two-stage optimization is 21 seconds. The optimal function value obtained by this algorithm can be used as an approximate optimal solution for this case. From the above results, it can be seen that the computing power requirement of the two-stage optimization is lower, and its operation results are close to those of the comprehensive optimization. Therefore, the two-stage optimization can be applied to the analysis of large-scale cases to obtain optimization results that meet the needs.

[0213] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting from any point of view, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any reference numerals in the claims should not be regarded as limiting the claims involved.

Claims

1. A method for optimizing freight train marshaling plan based on section traffic association, characterized by: Includes the following step, S1, establish a comprehensive optimization model for vehicle flow routing and marshaling scheme; S2, establish a single train marshaling scheme optimization model under a given traffic flow path; S3, apply the two models to the same case to optimize the railway network marshaling plan problem, obtain the optimization results respectively and compare them, and verify the accuracy of the result of the single train marshaling plan optimization model under the given traffic flow path; S4, optimize the railway network marshaling plan problem using the single train marshaling plan optimization model under given traffic flow paths.

2. The method for optimizing freight train marshaling plan based on the section traffic association relationship according to claim 1 is characterized by: In S1, the specific steps of establishing a comprehensive optimization model for vehicle flow routing and formation schemes include: S11, clarify the problem boundaries and assumptions; S12, clarify the objective function; S13, clarify constraints; S14, establish a comprehensive optimization model for vehicle flow routing and vehicle formation schemes.

3. The method for optimizing freight train marshaling plan based on section traffic association according to claim 2 is characterized in that: In step S11, the problem boundaries and assumptions specifically include: A01. Assume that section trains must be operated between adjacent marshaling yards; A02. The operating capacity of a marshaling yard is described by its adaptability and the number of shunting lines. The adaptability determines the number of vehicles that the marshaling yard can handle daily, while the number of shunting lines determines the number of marshaling destinations that the marshaling yard can form.

4. The method for optimizing freight train marshaling plan based on section traffic association according to claim 2 is characterized by: In step S12, the objective function consists of three parts: vehicle flow running cost, train assembly cost and vehicle flow reorganization cost; Traffic flow running cost: The traffic flow running cost is shown in formula (1). Since the traffic flow path is formed by linking the direct train paths that the branch traffic takes, the auxiliary variable P of the traffic flow path is used. ij To calculate the route mileage of each vehicle flow, the expression is as follows: According to whether the traffic flow is transported to the final destination in a direct or indirect way, there are two methods for calculating the mileage of the traffic flow path. For direct traffic flow whose origin and final destination are the starting and ending points of a certain marshaling destination respectively, its path is the same as the physical path of the marshaling destination, and is composed of the arcs that constitute the physical path of the destination, which is expressed by formula (2); For direct traffic without corresponding marshaling destination, traffic flow N ij First, the reconfiguration operation is performed at station k, and the traffic flow path from station i to station j is P ik and P kj Composition, expressed by formula (3); Train assembly cost: The assembly vehicle hours consumed by all marshaling yards on the railway network in one day and night, as shown in formula (4): Traffic flow adaptation cost: The traffic flow adaptation cost of the marshaling yard is obtained by multiplying the adaptation parameter and the adapted traffic volume. The total cost of traffic flow adaptation in all marshaling yards on the road network is shown in formula (5): In formula (5) is the average daily marshaling workload undertaken by marshaling yard k, As shown in formula (6), In formula (6), the actual vehicle flow f starting from station i and ending at station j is ij The original transportation demand N from station i to station j ij and the traffic flow from station h, the rear station of station i, and the final destination is station j and is reorganized at station i Composition; if the traffic flow f ij If the rescheduling operation is performed at station k, the traffic flow will be rescheduling. The value of is equal to the actual traffic flow f ij If not, then The value of is equal to 0; modify the traffic flow and the actual traffic flow f ij The conversion relationship between the two is given by the following formula: Combining equations (7) and (8), we can get the modified traffic flow: The calculation formula is as follows: When formula (9) is expressed in iterative form, there is a traffic flow reconfiguration chain Then the modified traffic flow from station i to station j is As shown in formula (10), If the traffic flow ij Can be programmed into direct destinations<i,j> In the above equation, the traffic flow f ij That is the destination<i,j> Part of the traffic flow intensity; for the traffic flow with the starting station as station i and the final station as station k, and the reorganization operation is performed at station j According to the traffic merging principle, this traffic also needs to be included in the direct destination.<i,j> Therefore, the traffic flow S attracted by the marshaling direction ij As shown in formula (11), 5. The method for optimizing freight train marshaling plan based on section traffic association according to claim 2 is characterized by: In step S13, the constraints include path continuity and integrity constraints, station operation capacity constraints, vehicle flow reconfiguration constraints, line section capacity constraints, vehicle flow path mileage constraints, and flow balance constraints; In the path continuity and integrity constraints, since the comprehensive optimization model is designed for the arc segments of the direct column flow path, it is necessary to set constraints to ensure the path continuity and integrity; Formula (12) ensures the direction of the group<i,j> The physical path starts from station i, and equation (13) guarantees the direction of the marshaling<i,j> The physical path ends at station j; The two constraints of equations (12) and (13) only determine the starting arc and the ending arc of a certain physical path. The relevant constraints that constitute the middle part of the path are expressed by equation (14): Formula (2) and Formula (3) determine that the traffic flow path is composed of the direct column flow path. On this basis, the constraints Formula (12), Formula (12) and Formula (14) restrict the traffic flow relocation station to be on its traffic flow path; In the station operation capacity constraint, the operation capacity of the marshaling yard is specifically manifested in the effective marshaling capacity and the number of available shunting lines; The station adaptation capacity constraint is expressed by equation (19): The number constraint of shunting lines at a station is expressed by equation (20): In formula (20), ψ(S ij ) is the group destination<i,j> The number of tracks that the attracted traffic needs to occupy has the following piecewise continuous linear form: Formula (21) is valid only when any two marshaling destinations do not share a shunting line. When the traffic volume attracted by the two marshaling destinations is small, they share a shunting line, which is expressed by formula (22): Traffic flow reorganization constraints: The traffic flow grouping and destination linking scheme on the road network needs to be coordinated with the grouping and destination scheme; if a certain destination does not exist, the traffic flow cannot be reorganized into that destination; Formula (23) is used to determine the traffic flow N ij Can the forward reorganization station be station k: If there is a marshaling destination<i,k> , then the traffic may be reorganized at station k; if there is no marshaling direction<i,k> , then the traffic flow cannot be reorganized at station k; Formula (24) is the uniqueness constraint of the vehicle flow grouping scheme, that is, if the vehicle flow from station i to station j is transported directly to the final destination station j, then the corresponding grouping destination variable y ij The value is 1; if you choose to reorganize at station k on the way, the corresponding group destination link variable The value is 1; Line section capacity constraint: The line section capacity constraint is expressed by equation (25): Traffic flow route mileage constraints: including relative detour rate constraints and detour rate constraints; The relative detour rate constraint is expressed by equation (26): Formula (26) is used to limit mileage and eliminate unreasonable paths; The detour rate constraint is expressed by equation (27): Flow balance constraints: arc flow on the road network The equilibrium constraint is expressed by equation (28): Formula (28) shows that the direction of the group on the arc segment [s, t] is<i,j> The traffic flow of station i to station j consists of three parts: the original traffic flow N ij ; The traffic flow starts from the station behind station i, ends at station j and is reorganized at station i The traffic flow starts at station i and ends at the station before station j and is reorganized at station j If the above three types of traffic flow are included in the direct destination with the final destination being station j at station i, and the transportation path of this destination passes through the arc segment [s, t], then is the sum of these three types of traffic flow; if the direct destination does not pass through the arc [s, t], or these traffic flows are incorporated into direct trains whose final destination is not station j, but is equal to 0; The flow in and out of a node is constrained by equation (29): Formula (29) shows that if the group direction<i,j> If the physical path passes through station t, the traffic flow from station i to station j received by station t is equal to the traffic flow from station i to station j sent out in that direction; if the marshaling direction<i,j> If the physical path does not pass through station t, the vehicle flow from station i to station j received and sent by station t is equal to 0.

6. The method for optimizing freight train marshaling plan based on section traffic association according to claim 5 is characterized by: Path variables for direct access to destinations in path continuity and integrity constraints If there are impossible values, set mandatory constraints and set the value of the impossible variable to 0; Formula (15) indicates that the non-direct traffic generated between adjacent marshaling yards i and j must choose the arc segment [i, j] linking the two stations for transportation; Formula (16) indicates that for the traffic flow from station i to station j, other arcs on the road network will not appear in its travel path; Formula (17) indicates that if a marshaling has station i as its starting station and station j as its final destination, the arcs that make up its physical path do not end at station i or start at station j; Formula (18) indicates that a physical path is arranged only when the marshaling destination exists.

7. The method for optimizing freight train marshaling plan based on section traffic association according to claim 1 is characterized by: A comprehensive optimization model Model-1 for traffic flow routing and marshaling scheme is established, with the following specific features: Constraints:

8. The method for optimizing freight train marshaling plan based on section traffic association according to claim 7 is characterized by: The constraints of the comprehensive optimization model Model-1 are modified as follows: E Potential (i,j): the destination of the marshaling in the road network<i,j> The set of arcs that can be selected for the physical path; if the traffic path from station i to station j is given as i→k→l→j, then the direction of the group<i,j> Only arcs [i,k], [k,l], and [l,j] can be selected for transportation; E Eliminate (i,j): the destination of the marshaling in the road network<i,j> The set of arcs that cannot be included in the physical path is equal to the set E of all arcs on the road network minus the available directions for grouping<i,j> The arc set E selected by the path Potential (i,j), that is, E Eliminate (i,j)=EE Potential (i,j); V Potential (i,j): the set of first forward relocation stations that the traffic from station i to station j may choose, excluding stations i and j; V Eliminate (i,j): The set of first forward reconfiguration stations that the traffic from station i to station j cannot choose, which is equal to the set V of all stations on the road network minus the traffic flow N ij Possible first forward reconfiguration station set V Potential (i,j), that is, V Eliminate (i,j)=VV Potential (i,j); According to the above constraint set, some constraints of the comprehensive optimization model Model-1 are reconstructed. Formula (30) is an improvement of formula (12), formula (31) is an improvement of formula (13), formula (32) is an improvement of formula (18), formula (33) is an improvement of formula (23), and formula (34) is an improvement of formula (24); And add mandatory constraints, Formula (35) eliminates the grouping direction<i,j> The arc segment that cannot be selected by the physical path; Formula (36) eliminates the traffic flow N ij The first forward relocation station that cannot be selected, combined with equation (33) and equation (34), makes the traffic flow N ij Only one station can be selected as the first forward rescheduling station on its given route, or the branch traffic flow can be incorporated into a direct destination at the departure station without rescheduling to the final destination station; Formula (37) indicates that if there is a group destination<i,j> , then the number of arcs contained in the physical path of the destination is equal to the given traffic flow N ij The number of arcs contained in the walking path; if there is no grouping destination<i,j> , then the corresponding direct column flow path variable is equal to 0; Formula (38) shows that the symbol The meaning is that in a given traffic flow routing scheme, traffic flow N ij Mileage of walking routes Formula (39) is the traffic flow intensity consistency constraint condition, symbol Represents the traffic intensity on the arc segment [s, t] under a given traffic routing scheme.

9. The method for optimizing freight train marshaling plan based on section traffic association according to claim 8 is characterized by: The comprehensive optimization model Model-1 is modified to adapt to the optimization problem of the train formation plan under the given traffic flow path, and a single train formation plan optimization model Model-2 based on the traffic flow path is constructed. The specific features are as follows: Constraints:

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