Train path and marshalling plan optimization method and system considering whole-train priority

Abstract

The invention provides a train path and marshalling plan optimization method and system under the condition of considering whole-train priority, and belongs to the technical field of train operation management, and the method comprises the steps: constructing a marshalling station sub-network of commercial vehicle train transportation and a supernetwork based on the marshalling station sub-network through employing a traffic flow path of a historical transportation plan; according to actual operation requirements, marshalling constraints and optimization targets are determined, and a train path and marshalling plan comprehensive optimization model is established; and solving the train path and marshalling plan comprehensive optimization model by using a commercial solver to obtain an optimized train transportation route and a pairing relationship between goods and trains on each road section, and further obtaining a marshalling plan of the commercial vehicle. According to the method, train traffic flow path planning and train marshalling planning are combined, a train path and loading plan optimization model under the multi-starting-point and multi-terminal-point condition is established, the matching relation between the train traffic flow path and the train and the matching relation between the train and the train on each road section are decided, and overall optimization of railway commercial vehicle transportation is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of train traffic organization, and relates to the optimization of the formation plan of freight trains and the traffic path. Specifically, it relates to a method and system for optimizing train routes and formation plans considering the priority of the whole train. Background Art

[0002] In recent years, the railway has attracted the source of goods vehicles from the road to the railway with its characteristics of large transportation volume, energy conservation and environmental protection, and low price. The transportation of railway commodity vehicles has gradually emerged. Against the background of the continuous adjustment of the transportation structure of commodity vehicles, the railway will play an indispensable role in the commodity vehicle transportation market.

[0003] At the beginning of the development of railway transportation of commodity vehicles, the transportation mode and route were simple and fixed, and there were few disintegration and formation operations during the transportation process, which could give full play to the characteristics of long-distance, large-batch and large-scale transportation of the railway. However, with the continuous establishment of the main vehicle factories, the starting points of railway transportation of commodity vehicles have increased continuously, resulting in the transformation of the supply chain organization mode from the original "forward movement of the warehouse" to a multi-point radiation mode, and also causing the continuous increase in the scale and complexity of the railway commodity vehicle logistics transportation scale and network. A large number of departure and arrival stations have brought huge disintegration and formation workloads to freight trains, and have also seriously affected the transportation efficiency.

[0004] At present, although there is a certain accumulation in the research on train disintegration and formation technology and formation plans, there are still many imperfections. On the one hand, the disintegration facilities for commodity vehicle trains are not perfect, and the relevant disintegration technologies are limited. At present, some marshalling yards cannot realize hump shunting, and can only use plane shunting on the detour line. Affected by factors such as the utilization efficiency of the detour line in the marshalling yard, the number of vehicles stored on the detour line, and the utilization rate of the formation line, the operation efficiency of trains in the marshalling yard is low; in addition, although some marshalling yards can realize over-hump shunting, due to restrictions on shunting mode, the use of formation lines, speed, etc., compared with ordinary railway vehicles, the operation efficiency is low. On the other hand, the train formation plan is not systematic, and the loading of commodity vehicles is relatively disorderly. At present, the formulation of the departure plan of commodity vehicle trains mostly relies on experience combined with the formation plan manually, with low efficiency and low accuracy, resulting in a relatively rough loading plan, and an unbalanced situation where the full load rate of some trains is low while some trains are seriously overloaded during the transportation process. These lead to a large number of train disintegration and formation operations during the transportation of commodity vehicles, bringing huge pressure to the marshalling yard, and at the same time having an impact on the timeliness of commodity vehicle transportation, affecting the whole-process transportation and delivery of commodity vehicle logistics.

[0005] Therefore, for the transportation of commodity vehicle trains, there is an urgent need for a scientific and reasonable train formation plan to plan the train flow path and the matching relationship between commodity vehicles and trains on the path, organize large groups or full trains for loading as much as possible, maximize the utilization rate of train carriages, and reduce the number of reorganizations at intermediate marshalling stations along the way, so as to balance the freight pressure and improve the transportation efficiency. Summary of the Invention

[0006] To solve at least one of the technical problems in the above background art, the present invention provides an optimization method and system for train path and formation plan considering the priority of full trains, which solves the problems such as the unsystematic loading plan of commodity vehicle trains, uneven train loading, and low operation efficiency.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] In the first aspect, the present invention provides an optimization method for train path and formation plan considering the priority of full trains, including:

[0009] Using the train flow paths of historical transportation plans, a marshalling station sub-network for the transportation of commodity vehicle trains is designed, a virtual total starting point and a virtual total ending point are introduced, and a super-network based on the marshalling station sub-network is constructed;

[0010] According to the actual operation requirements, marshalling constraints and optimization objectives are determined, and a comprehensive optimization model for train path and formation plan is established;

[0011] Using a commercial solver to solve the comprehensive optimization model for train path and formation plan, the optimized train transportation route and the pairing relationship between goods and trains on each section are obtained, and then the formation plan for commodity vehicle trains is obtained.

[0012] As a further limitation of the first aspect of the present invention, establishing the sub-network for the transportation of commodity vehicle trains includes: by querying the train flow paths between the departure and arrival stations of commercial vehicles required by the daily transportation plan, extracting all the marshalling stations passed through as nodes, and connecting the marshalling stations that can be directly accessed or connected through stations with no operation capacity as edges.

[0013] As a further limitation of the first aspect of the present invention, establishing the super-network of train marshalling stations includes: on the basis of the constructed marshalling station sub-network, adding two virtual nodes, namely a virtual total starting station and a virtual total ending station, stipulating that all trains in the model start from the virtual total starting station and the virtual total ending station is the end point, and connecting the two virtual nodes to all marshalling station nodes respectively as virtual edges.

[0014] As a further limitation of the first aspect of the present invention, the objective function of the comprehensive optimization model for train path and formation plan considering the priority of full trains is:

[0015] min Q = q1 + μq2

[0016]

[0017] Among them, min represents the minimization operation, Q represents the objective function, q1 represents the total number of train breakup times in the road network, q2 is a penalty term, representing the sum of the differences between the number of carriages of all trains that do not meet the minimum formation condition and the minimum formation condition in the road network, μ represents the weight coefficient of the penalty term, T represents the set of all trains, N represents the set of all marshalling stations, j represents any train in the road network, m and n both represent any marshalling station in the road network, is a decision variable, representing the train rearrangement situation. If train j is rearranged at marshalling station m, it is 1, otherwise it is 0. L represents the set of all sections on the road network, The variable represents the difference between the minimum formation condition and the number of vehicles when train j runs from marshalling station m to n.

[0018] As a further limitation of the first aspect of the present invention, the constraint conditions of the comprehensive optimization model of train routing and marshalling plan considering the whole - train priority include: the constraint of the conservation of the number of loaded cars in the transportation plan, the transportation demand constraint, the train breakup constraint, the train capacity constraint, the train path connectivity constraint, the logical constraint between the transportation plan variables and the train path variables, the constraint that empty trains cannot run, the constraint of the difference between the minimum formation condition and the number of vehicles, the train interruption constraint, and the decision variable constraint.

[0019] In the second aspect, the present invention provides an optimization system for train routing and marshalling plan considering the whole - train priority, including:

[0020] The first construction module is used to design the marshalling station sub - network for the transportation of commodity car trains by using the vehicle flow path of the historical transportation plan, introduce a virtual total starting point and a virtual total ending point, and construct a super - network based on the marshalling station sub - network;

[0021] The second construction module is used to determine the marshalling constraints and optimization objectives according to the actual operation requirements, and establish a comprehensive optimization model of train routing and marshalling plan;

[0022] The solving module is used to solve the comprehensive optimization model of train routing and marshalling plan by using a commercial solver, obtain the optimized train transportation route and the pairing relationship between goods and trains on each section, and further obtain the commodity car train marshalling plan.

[0023] In the third aspect, the present invention provides a non - transient computer - readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the train routing and marshalling plan optimization method as described in the first aspect is realized.

[0024] In a fourth aspect, the present invention provides a computer device, including a memory and a processor, where the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the train route and formation plan optimization method considering the whole train priority as described in the first aspect.

[0025] In a fifth aspect, the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the train route and formation plan optimization method considering the whole train priority as described in the first aspect.

[0026] Advantages of the present invention: Based on the existing vehicle flow paths of commodity vehicle trains at departure and arrival stations, and on the premise of meeting the daily transportation plan, the present invention combines the train vehicle flow path planning with the train formation plan, establishes an optimization model for train routes and loading plans under the conditions of multiple origins and multiple destinations, determines the train vehicle flow paths and the matching relationships between vehicles and trains on each section, and realizes the overall optimization of railway commodity vehicle transportation. In terms of the train formation plan, from the perspective of improving the utilization rate of train carriages, the present invention considers that the priority of the train is higher than that of the transportation plan. The same transportation plan can be split into specific numbers of copies, and each copy is transported to the destination by different trains, and it is required that the number of carriages loaded on the departing trains reaches the given minimum formation condition as much as possible (for trains that have met the minimum formation condition, they can be formed and dispatched separately); in terms of the train vehicle flow paths, in order to reduce the overall number of train breakup and shunting operations and improve the concentration of shipment, it is required to increase the number of long-distance technical through trains as much as possible during the planning process. Based on this, a comprehensive optimization model for train routes and formation plans considering the whole train priority is constructed, aiming to organize whole train loading to the greatest extent, reduce the waste of train carrying space, improve the concentration of train shipment, and reduce the number of train breakup and shunting operations. In summary, the present invention has broad development prospects in the field of railway freight transportation and can provide a useful exploration approach for the transportation organization of railway commodity vehicles.

[0027] The advantages of the additional aspects of the present invention will be more clearly given in the following description part, or can be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0029] Figure 1 This is a flowchart of the train route and formation plan optimization method considering the priority of the whole train formation in the embodiments of the present invention.

[0030] Figure 2 This is a schematic diagram of the establishment of the marshalling station sub-network in the embodiments of the present invention.

[0031] Figure 3 This is a schematic diagram of the establishment of the super-network based on the marshalling station sub-network in the embodiments of the present invention.

[0032] Figure 4 This is the train transportation route map obtained by solving the model in the embodiments of the present invention. Detailed implementation manners

[0033] The following details the implementation manners of the present invention. The examples of the implementation manners are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The implementation manners described through the drawings are exemplary and are only used to explain the present invention, and cannot be construed as a limitation to the present invention.

[0034] Those skilled in the art of this technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used here have the same meaning as the general understanding of those of ordinary skill in the art in the field to which the present invention belongs.

[0035] It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined as here.

[0036] Those skilled in the art of this technology can understand that, unless specifically stated, the singular forms "a", "an", "the" and "said" used here may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or their groups.

[0037] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. Without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0038] For ease of understanding the present invention, the following further explains the present invention with specific embodiments in conjunction with the accompanying drawings, and the specific embodiments do not constitute a limitation to the embodiments of the present invention.

[0039] Those skilled in the art should understand that the drawings are only schematic diagrams of the embodiments, and the components in the drawings are not necessarily essential for implementing the present invention.

[0040] Embodiment 1

[0041] In this Embodiment 1, first, a train routing and formation plan optimization system considering the case of whole - train priority is provided, including: a first construction module, which uses the traffic flow paths of historical transportation plans to design a sub - network of marshalling stations for the transportation of commodity vehicle trains, introduces a virtual total starting point and a virtual total ending point, and constructs a super - network based on the sub - network of marshalling stations; a second construction module, which determines formation constraints and optimization objectives according to actual operation requirements, and establishes a comprehensive optimization model for train routing and formation plan; a solving module, which uses a commercial solver to solve the comprehensive optimization model for train routing and formation plan, obtains the optimized train transportation route and the pairing relationship between goods and trains on each section, and further obtains the formation plan of commodity vehicle trains.

[0042] In this embodiment, using the above - mentioned system, an optimization method for train routing and formation plan considering the case of whole - train priority is realized, which is mainly composed of a comprehensive optimization model for train routing and formation plan. The comprehensive optimization model for train routing and formation plan assumes that the same transportation plan can be split during the train transportation process, aims to minimize the number of train break - up times and the number of non - formed carriages, considers train break - up constraints, train capacity constraints, and constraints such as the connectivity of traffic flow paths on the network. At the same time, to ensure that the train stops running after transporting commodity vehicles, a train interruption constraint is added to the model to make the model more in line with the actual situation. Considering that the constructed model is a mixed - integer linear programming problem, the commercial solver Gurobi can be used to directly solve this problem.

[0043] Such as Figure 1As shown in the figure, in the method of this embodiment, first, the vehicle flow path of the historical transportation plan is used to design the marshalling yard sub-network for the transportation of commodity vehicle trains. On this basis, a virtual total starting point and a virtual total ending point are introduced to construct a super-network based on the marshalling yard sub-network. Then, according to the actual operation requirements, constraints and optimization objectives are constructed, and an integrated optimization model of train routing and marshalling plan is established. Finally, a commercial solver is used to solve the model to obtain the optimized train transportation route and the pairing relationship between goods and trains on each section, and then the marshalling plan of commodity vehicle trains is obtained, and the effect is evaluated.

[0044] Design the sub-network for the transportation of commodity vehicle trains according to the vehicle flow path of the historical transportation plan, and on this basis, establish a super-network of train marshalling yards, including:

[0045] By querying the vehicle flow path between the departure and arrival stations of commercial vehicles required by the daily transportation plan, all marshalling yards passed through are extracted as nodes, and the marshalling yards that can be directly connected or connected through stations without operation capacity are connected as edges to establish a marshalling yard sub-network. As Figure 2 shown in the figure, marshalling yards B and E are directly connected in the figure, so B and E are directly connected as edges of the road network during the construction of the network. However, marshalling yards A and B are not directly connected, but are connected through non-marshalling station C. Therefore, A and B are also directly connected as edges when generating the marshalling yard sub-network.

[0046] On the basis of the above-constructed marshalling yard sub-network, two virtual nodes are added, namely the virtual total starting point station and the virtual total ending point station. It is stipulated that all trains in the model start from the virtual total starting point station and end at the virtual total ending point station. The two virtual nodes are respectively connected to all marshalling yard nodes as virtual edges to establish a super-network based on the marshalling yard sub-network, as Figure 3 shown in the figure.

[0047] On the basis of the designed super-network of train marshalling yards, an integrated optimization model of train routing and marshalling plan considering the priority of full trains is constructed, including:

[0048] In this step, an integrated optimization model of train routing and marshalling plan considering the priority of full trains is constructed. The work in this stage first gives the symbols and definitions of specific decision variables, intermediate variables, sets and parameters, and then constructs an integrated optimization model of train routing and marshalling plan considering the priority of full trains based on constraints such as train capacity limitations, train operation condition limitations, and train interruption limitations, and with the goal of minimizing the number of train breakup times and the number of non-train carriages.

[0049] In this embodiment, the objective function of constructing the model includes:

[0050] During the transportation of commodity cars by trains, due to the limitations of the marshalling and unmarshalling technology at marshalling stations, the operation process is inefficient. At the same time, it is necessary to make full use of the carrying space of trains to improve the shipping concentration and organize carriages into groups to the greatest extent. Therefore, in the objective function, minimizing the total number of train unmarshalling operations on the railway network is considered, and at the same time, penalties are imposed on the number of carriages that do not meet the minimum formation conditions. The expression of the objective function is as follows:

[0051] min Q=q1+μq2

[0052]

[0053] Among them, min represents the minimization operation, Q represents the objective function, q1 represents the total number of train unmarshalling operations on the railway network, q2 is a penalty term, representing the sum of the differences between the number of carriages of all trains that do not meet the minimum formation conditions and the minimum formation conditions on the railway network, μ represents the weight coefficient of the penalty term, T represents the set of all trains, N represents the set of all marshalling stations, j represents any train on the railway network, m and n both represent any marshalling station on the railway network, is a decision variable, representing the adaptation situation of the train. If train j is adapted at marshalling station m, it is 1, otherwise it is 0. L represents the set of all sections on the railway network, The variable represents the difference between the minimum formation condition and the number of vehicles when train j runs from marshalling station m to n.

[0054] The constraint conditions of the model include:

[0055] The constraint conditions for the comprehensive optimization model of train routing and marshalling plan considering the priority of full trains are:

[0056] (1) The constraint of the conservation of the number of loaded cars in the transportation plan, that is, the sum of the number of loaded cars in each part of the transportation plan after splitting is equal to the number of loaded cars required by the plan:

[0057]

[0058] Among them, K represents the set of the number of parts after the transportation plan is split, K = {1, 2, 3,..., b max}, b max represents the maximum number of splits of the transportation plan, calibrated by the number of trains, that is, if there are n trains, then b max = n, k represents one of the transportation plans after splitting, i represents a certain transportation plan, is an integer variable, representing the number of loaded cars in the k-th part of transportation plan i, w i represents the number of loaded cars required by transportation plan i, and C represents the set of all transportation plans;

[0059] (2) Transportation demand satisfaction constraint, that is, there must be a train on each section of the transportation plan to ensure that the transportation plan is sent to the destination:

[0060]

[0061] Among them, j represents any train in the road network, i represents a certain transportation plan, k represents one of the split transportation plans, and m and n both represent any marshalling station in the road network. It is a decision variable. If the k-th part of transportation plan i is on train j on the edge from marshalling station m to n, it is 1; otherwise, it is 0. T represents the set of all trains, C represents the set of all transportation plans, K represents the set of the number of parts after the transportation plan is split, L represents the set of all sections on the road network, and L(i) represents the set of all sections that transportation plan i needs to pass through.

[0062] (3) Train breakup constraint, that is, if the train does not perform breakup operation, the transportation plans carried by the train before and after entering and leaving the marshalling station do not change:

[0063]

[0064] Among them, j represents any train in the road network, i represents a certain transportation plan, k represents one of the split transportation plans, and m, n, and l all represent any marshalling station in the road network. It is a decision variable. If the k-th part of transportation plan i is on train j on the edge from marshalling station m to n, it is 1; otherwise, it is 0. M represents an infinitely large number. It is a decision variable representing the train reorganization situation. If train j is reorganized at marshalling station m, it is 1; otherwise, it is 0. A - (m) represents the set of all upstream marshalling stations directly connected to marshalling station m, and A + (m) represents the set of all downstream marshalling stations directly connected to marshalling station m. T represents the set of all trains, C represents the set of all transportation plans, K represents the set of the number of parts after the transportation plan is split, and N r represents the set of all real marshalling stations (excluding virtual nodes O and F) on the road network.

[0065] (4) Train capacity constraint to ensure that each train is not overloaded:

[0066]

[0067] Among them, j represents any train in the road network, i represents a certain transportation plan, k represents one of the split transportation plans, and m and n both represent any marshalling station in the road network. is a decision variable, which is 1 if the k-th part of transportation plan i is on train j on the edge from marshalling station m to n, and 0 otherwise. is an integer variable representing the number of loaded cars in the k-th part of transportation plan i. represents the maximum number of cars that train j can carry (usually taken as 29), C represents the set of all transportation plans, K represents the set of the number of parts after the transportation plan is split, T represents the set of all trains, and L represents the set of all sections on the road network.

[0068] (5) Train path connectivity constraint, ensuring that each train starts from the virtual total origin, passes through a connected car flow path, and finally reaches the virtual total destination:

[0069]

[0070] Among them, j represents any train in the road network, and m, n, and l all represent any marshalling station in the road network. is a decision variable, which is 1 if train j runs from marshalling station m to marshalling station n, and 0 otherwise. O represents the virtual total origin of the road network, D represents the virtual total destination of the road network, and A - (m) represents the set of all upstream marshalling stations directly connected to marshalling station m, and A + (m) represents the set of all downstream marshalling stations directly connected to marshalling station m, T represents the set of all trains, and N r represents the set of all real marshalling stations on the road network (excluding the virtual nodes O and D).

[0071] (6) Logical constraint between transportation plan variables and train path variables, stipulating that the premise for a transportation plan to be transported is that the train passes through this section:

[0072]

[0073] Among them, i represents a certain transportation plan, j represents any train in the road network, k represents one of the parts of the split transportation plan, and m and n both represent any marshalling station in the road network. is a decision variable, which is 1 if the k-th part of transportation plan i is on train j on the edge from marshalling station m to n, and 0 otherwise. M is an infinitely large number. is a decision variable, which is 1 if train j runs from marshalling station m to marshalling station n, and 0 otherwise. T represents the set of all trains, and L represents the set of all sections on the road network.

[0074] (7) Constraint that empty trains cannot run, stipulating that all running trains need to carry transportation plans:

[0075]

[0076] Among them, \(i\) represents a certain transportation plan, \(j\) represents any train in the road network, \(k\) represents one of the split transportation plans, and \(m\) and \(n\) both represent any marshalling station in the road network. is a decision variable, which is 1 if train \(j\) runs from marshalling station \(m\) to marshalling station \(n\), otherwise 0. is a decision variable, which is 1 if the \(k\)-th part of transportation plan \(i\) is on train \(j\) on the edge from marshalling station \(m\) to \(n\), otherwise 0. \(T\) represents the set of all trains, and \(L\) represents the set of all sections in the road network.

[0077] (8) Intermediate variable The expression of represents the difference between the minimum formation condition and the number of train cars:

[0078]

[0079] Among them, \(i\) represents a certain transportation plan, \(j\) represents any train in the road network, \(k\) represents one of the split transportation plans, and \(m\) and \(n\) both represent any marshalling station in the road network. The variable represents the difference between the minimum formation condition and the number of cars when train \(j\) runs from marshalling station \(m\) to \(n\). represents the minimum number of cars for train \(j\) to reach the formation condition (usually taken as 25). is a decision variable, which is 1 if train \(j\) runs from marshalling station \(m\) to marshalling station \(n\), otherwise 0. is an integer variable representing the number of loaded cars in the \(k\)-th part of transportation plan \(i\). is a decision variable, which is 1 if the \(k\)-th part of transportation plan \(i\) is on train \(j\) on the edge from marshalling station \(m\) to \(n\), otherwise 0. \(C\) represents the set of all transportation plans, \(K\) represents the set of the number of parts after the transportation plan is split, \(T\) represents the set of all trains, and \(L\) r represents the set of all real sections (excluding the sections directly connected to the virtual nodes \(O\) and \(D\)).

[0080] (9) Train interruption constraint. When the train reaches the destination and finishes transporting the transportation plan, the train stops running (returns to the virtual total end point):

[0081]

[0082] Among them, \(i\) represents a certain transportation plan, \(j\) represents any train in the road network, \(k\) represents one of the split transportation plans, and \(m\) and \(n\) both represent any marshalling station in the road network. \(M\) is an infinitely large number. represents the minimum number of cars for train \(j\) to reach the formation condition (usually taken as 25). The variable represents the difference between the minimum formation condition and the number of cars when train \(j\) runs from marshalling station \(m\) to \(n\). is an integer variable representing the number of loaded cars in the k-th part of transportation plan i. is a decision variable, which is 1 if the k-th part of transportation plan i is on train j on the edge from marshalling station m to n, and 0 otherwise. is a decision variable, which is 1 if train j runs from marshalling station m to marshalling station n, and 0 otherwise, C D (n) represents the set of all transportation plans with n as the destination, K represents the set of the number of parts after the transportation plan is split, T represents the set of all trains, L r represents the set of all real sections (excluding the sections directly connected to the virtual nodes O and D).

[0083] (10) Decision variable constraints:

[0084]

[0085] Use a solver to solve the integrated optimization model of train routing and marshalling plan, visualize the train transportation route and marshalling plan, and through sensitivity analysis, explore the influence of penalty terms on factors such as the number of train uncoupling times and the average number of carriages.

[0086] Considering that the above integrated optimization model of train routing and marshalling plan is a mixed integer linear programming problem, a commercial solver Gurobi is used to solve the model, and the optimal transportation route of the train and the corresponding marshalling plan can be obtained.

[0087] In this embodiment, taking Baomou Station as the originating station as an example, the transportation plan as shown in Table 1 is known at this station. By solving the model, the train transportation route can be obtained. Among them, there are a total of three trains originating from Baomou Station, with a total of 3 uncoupling times. The first train is the orange transportation route in the figure, originating from Baomou Station and loading 11 carriages required by Pumou Station, and loading 6 carriages to Xinmou Station and 2 carriages to Fanmou Station through uncoupling at Shanmou Station, and then arriving at Pumou Station, Fanmou Station and Xinmou Station in sequence to complete the transportation task; the second train is the green transportation route in the figure, starting from Baomou Station and loading 5 carriages required by Lanan Dongchuan Station, loading 24 carriages from Sanmou Station through uncoupling at Yingmou Station, and then arriving at Lanan Dongchuan Station and Sanmou Station in sequence; the third train is the purple transportation route, originating from Baomou Station and loading 4 carriages required by Yinmou Station, and directly reaching Yinmou Station.

[0088] Table 1

[0089] Order Serial Number Number of Loaded Vehicles Destination 1 6 New Certain Station 2 2 Fan's Station 3 11 Pu's Station 4 5 Lan's Dongchuan Station 5 4 Yin's Station 6 24 San's Station

[0090] Meanwhile, in this embodiment, a sensitivity analysis is performed on the comprehensive optimization model of train routes and formation plans, which can prove that the train transportation efficiency can be improved by this method. Under two conditions with and without penalty terms, the influence of the existence of the penalty for not meeting the minimum formation condition on the train breakup plan and transportation efficiency is explored, and trains departing from a certain station in Baoding and a certain station in Fuzhou are selected for illustration respectively, as shown in Table 2.

[0091] Table 2

[0092]

[0093] It can be seen that without the penalty for trains not meeting the minimum formation condition, the breakup times of trains can be greatly reduced, and most trains complete the transportation tasks through direct routes. There is no breakup work for trains departing from Baoding and trains departing from North Fuzhou. However, this will lead to an increase in the number of trains required to complete the transportation tasks and more occupied transport capacity resources. For example, for trains departing from a certain station in Baoding, only 3 trains are required to complete the task considering the penalty for trains not meeting the minimum formation condition, while 6 trains are required to complete the transportation task without considering the penalty. Moreover, the average number of carriages loaded per train also increases from 8.7 to 17.3 under the penalty condition, almost doubling, and the loading rate of trains also increases from 29.9% to 59.7%. This shows that the space utilization rate of trains can be greatly improved under the penalty condition, reducing the waste of transport capacity resources. At the same time, by imposing penalties, the transportation efficiency of trains can also be greatly improved. For example, among the trains departing from North Fuzhou, for the loading demand of 10 carriages, without considering the penalty for trains not meeting the minimum formation condition, 6 trains are required to complete the transportation task, and each train only undertakes 16.7% of the transportation plan on average. While considering the penalty condition, only 1 train can complete the task through 5 breakups, and the transportation efficiency is increased by 83.3%.

[0094] Embodiment 2

[0095] This Embodiment 2 provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the train route and formation plan optimization method considering the whole train priority as described above is implemented. The method includes:

[0096] Using the vehicle flow paths of historical transportation plans, a sub-network of marshalling stations for the transportation of commodity cars is designed, a virtual total starting point and a virtual total ending point are introduced, and a super-network based on the sub-network of marshalling stations is constructed;

[0097] According to the actual operation requirements, determine the formation constraints and optimization objectives, and establish a comprehensive optimization model of train routes and formation plans;

[0098] Use a commercial solver to solve the integrated optimization model of train routing and formation plan, obtain the optimized train transportation route and the pairing relationship between goods and trains on each section, and then obtain the formation plan of commodity car trains.

[0099] Embodiment 3

[0100] This Embodiment 3 provides a computer device, including a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the train routing and formation plan optimization method considering the whole train priority as described above. The method includes:

[0101] Using the vehicle flow path of the historical transportation plan, design the marshalling station subnet for the transportation of commodity car trains, introduce a virtual total starting point and a virtual total ending point, and construct a super network based on the marshalling station subnet;

[0102] According to the actual operation requirements, determine the formation constraints and optimization objectives, and establish an integrated optimization model of train routing and formation plan;

[0103] Use a commercial solver to solve the integrated optimization model of train routing and formation plan, obtain the optimized train transportation route and the pairing relationship between goods and trains on each section, and then obtain the formation plan of commodity car trains.

[0104] Embodiment 4

[0105] This Embodiment 4 provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory to enable the electronic device to execute the instructions for implementing the train routing and formation plan optimization method considering the whole train priority as described above. The method includes:

[0106] Using the vehicle flow path of the historical transportation plan, design the marshalling station subnet for the transportation of commodity car trains, introduce a virtual total starting point and a virtual total ending point, and construct a super network based on the marshalling station subnet;

[0107] According to the actual operation requirements, determine the formation constraints and optimization objectives, and establish an integrated optimization model of train routing and formation plan;

[0108] Use a commercial solver to solve the integrated optimization model of train routing and formation plan, obtain the optimized train transportation route and the pairing relationship between goods and trains on each section, and then obtain the formation plan of commodity car trains.

[0109] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0110] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure One one flow or multiple flows and / or blocks Figure One one block or multiple blocks.

[0111] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realizes the functions specified in Figure One one flow or multiple flows and / or blocks Figure One one block or multiple blocks.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, and a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable devices provide steps for realizing the functions specified in Figure One one flow or multiple flows and / or blocks Figure One one block or multiple blocks.

[0113] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation to the protection scope of the present invention. Those skilled in the art should understand that based on the technical solutions disclosed in the present invention, various modifications or deformations that can be made by those skilled in the art without creative efforts should be covered within the protection scope of the present invention.

Claims

1. A train routing and marshaling plan optimization method considering whole train priority, characterized in that: include: Using the traffic paths of historical transportation plans, we construct a marshaling yard subnetwork for commercial vehicle train transportation, introduce virtual total starting points and virtual total end points, and build a supernetwork based on the marshaling yard subnetwork; According to actual operation needs, determine the train formation constraints and optimization goals, and establish a comprehensive optimization model for train routing and formation planning; A commercial solver is used to solve the comprehensive optimization model of train routes and marshaling plans, and the optimized train transportation routes and the pairing relationship between goods and trains on each section are obtained, and then the commercial vehicle train marshaling plan is obtained.

2. The train routing and marshaling plan optimization method considering whole train priority according to claim 1, characterized in that: Establishing the commercial vehicle train transportation subnetwork includes: querying the traffic routes between the commercial vehicle departure and arrival stations required by the daily transportation plan, extracting all the marshaling yards along the route as nodes, and connecting the marshaling yards that can be directly reached or connected through stations without operating capacity as edges.

3. The train route and formation plan optimization method considering whole train priority according to claim 2, characterized in that: Establishing the super network of the train marshalling yard includes: adding two virtual nodes, the virtual total starting station and the virtual total terminal station, on the basis of the constructed marshalling yard subnetwork, stipulating that all trains in the model depart from the virtual total starting station and the virtual total terminal station is the terminal, and connecting the two virtual nodes to all marshalling yard nodes as virtual edges.

4. The train routing and marshaling plan optimization method considering whole train priority according to claim 1, characterized in that: The objective function of the comprehensive optimization model of train routing and marshaling planning considering the whole train priority is: min Q=q1+μq2 Among them, min represents minimization operation, Q represents the objective function, q1 represents the total number of train unmarshaling times in the network, q2 is a penalty term, which represents the sum of the difference between the number of carriages of all trains in the network that do not meet the minimum marshaling condition and the minimum marshaling condition, μ represents the weight coefficient of the penalty term, T represents the set of all trains, N represents the set of all marshaling sites, j represents any train in the network, m and n both represent any marshaling site in the network, is a decision variable, indicating the reshuffling of the train. If train j is reshuffled at marshaling yard m, it is 1, otherwise it is 0. L represents the set of all sections on the road network. The variable represents the difference between the minimum formation condition and the number of vehicles when train j runs from marshaling yard m to n.

5. The train routing and marshaling plan optimization method considering whole train priority according to claim 4, characterized in that: The constraints of the comprehensive optimization model of train route and marshaling plan considering the whole train priority include: conservation constraint of transportation plan loading number, transportation demand constraint, train disassembly constraint, train capacity constraint, train path connectivity constraint, logical constraint between transportation plan variables and train path variables, constraint that empty trains cannot run, constraint on the difference between minimum train formation condition and the number of vehicles, train interruption constraint and decision variable constraint.

6. The train routing and marshaling plan optimization method considering whole train priority according to claim 5, characterized in that: The conservation constraint of the number of vehicles loaded in the transportation plan is: Where K represents the number of copies of the order after it is split, K = {1, 2, 3, ..., b max }, b max represents the maximum number of splits of an order, which is calibrated by the number of trains. That is, if there are n trains, then b max = n, k represents one of the split orders, i represents a transportation plan, is an integer variable, representing the number of vehicles loaded in the kth part of transportation plan i, w i represents the number of loads required for transportation plan i, and C represents the set of all transportation plans; The transportation demand satisfies the constraints: in, is a decision variable, which is 1 if the kth part of transport plan i is on train j on the edge from marshaling yard m to n, otherwise it is 0. L(i) represents the set of all sections that transport plan i needs to pass through; The train unpacking constraints are: The train capacity constraint is: The train path connectivity constraints are: in, is a decision variable. If train j runs from marshalling station m to marshalling station n, it is 1, otherwise it is 0. + (m) represents the set of all downstream marshalling yards directly connected to marshalling yard m; The logical constraints between the transportation plan variables and the train path variables are: Among them, if the kth part of the transport plan i is on the train j on the edge of the marshaling yard m to n, it is 1, otherwise it is 0, and M is an infinite number. is a decision variable, which is 1 if train j runs from marshaling station m to marshaling station n, otherwise it is 0; Empty trains cannot run. Constraints: The difference between the minimum platooning condition and the number of vehicles is: in, The variable represents the difference between the minimum formation condition and the number of vehicles when train j runs from marshaling yard m to n. Indicates the minimum number of vehicles required for train j to meet the formation condition. If train j runs from marshaling station m to marshaling station n, it is 1, otherwise it is 0. is an integer variable, indicating the number of vehicles loaded in the kth part of transportation plan i, L r represents the set of all real road segments, excluding the road segments directly connected to virtual nodes O and D; The train disruption constraints are: Among them, C D (n) represents the set of all transportation plans with n as the destination; The decision variable constraints are:

7. A train routing and marshaling plan optimization system considering whole train priority, characterized in that: include: The first construction module is used to use the vehicle flow path of the historical transportation plan to construct the marshaling yard sub-network of commercial vehicle train transportation, introduce the virtual total starting point and virtual total end point, and construct a super network based on the marshaling yard sub-network; The second building module is used to determine the train routing and train formation plan comprehensive optimization model based on the actual operation requirements and optimization objectives; The solution module is used to use a commercial solver to solve the comprehensive optimization model of train routes and train formation plans, obtain the optimized train transportation routes and the pairing relationship between goods and trains on each section, and then obtain the commercial vehicle train formation plan.

8. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the train route and formation plan optimization method considering the whole train priority as described in any one of claims 1-6 is implemented.

9. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the train route and formation plan optimization method considering the whole train priority as described in any one of claims 1-6.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the train route and formation plan optimization method considering the whole train priority as described in any one of claims 1-6.

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