CN109902866A - The cooperative optimization method of railway fast freight class column starting scheme and rolling stock - Google Patents
The cooperative optimization method of railway fast freight class column starting scheme and rolling stock Download PDFInfo
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Abstract
The present invention provides a kind of cooperative optimization methods of railway fast freight class column starting scheme and rolling stock, comprising: establishes the collaboration Optimized model and parameter constraints connected based on freight traffic volume distribution and class's train bottom；The given data of fast freight class column is obtained, and according to the given data, the collaboration Optimized model and the parameter constraints settling time-space-Ban Lie three-dimensional service network；The three-dimensional service network is carried out to decompose the minimum cost path for generating multiple three-dimensional services sub-network networks and determining each three-dimensional services sub-network network, class's column starting scheme and rolling stock plan are generated according to minimum cost path information.The present invention can reduce empty wagons traveling, improve the utilization rate of vehicle bottom resource, reduce the operation cost of railway department, ensure the smooth development of the normal Transportation Organization work in fast freight class Liege；And consider that collaboration optimum results obtained from the organic connections of the two can provide more structurally sound input information for the establishment of fast freight class column running schedule.
Description
Technical Field
The embodiment of the invention relates to the technical field of railway transportation, in particular to a cooperative optimization method for a railway express-transport class-train operation scheme and train bottom application.
Background
With the rapid development of the logistics industry, the goods sources oriented to the railway express class show the characteristics of small batch, multiple batches and high timeliness, and higher requirements are provided for the operation scheme. In order to adapt to the characteristics of the goods source and face increasingly complex transportation organization work, how to formulate a reasonable transportation organization mode becomes a problem worthy of quantification. In addition, due to the timeliness, the cargo carrying specificity and the like of the railway express train, available train bottom resources present a shortage state. The vehicle bottom resource is the guarantee of the exchange quality of the shift train operation scheme, and the quality of the operation scheme reflects the matching degree of freight transportation requirements and directly determines the operation benefit of the shift train, so that how to realize the benefit maximization of the operation scheme under the constraint of limited vehicle bottom resources becomes another problem needing to be discussed.
The existing optimization mode usually considers two problems of the operation scheme and the vehicle bottom application, breaks the association between the top plan and the bottom resource, and leads the optimization result to be difficult to be implemented to the reality. Therefore, it is necessary to provide a cooperative optimization method for the operation scheme of the express train and the vehicle bottom application, so as to improve the universality of the optimization method and the practicability of the optimization result.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a cooperative optimization method for a railway express train operation scheme and train bottom application, and the universality of the optimization method and the practicability of an optimization result are improved.
In order to achieve the purpose, the invention provides the following technical scheme:
on one hand, the invention provides a cooperative optimization method for a railway express train operation scheme and train bottom application, which comprises the following steps:
establishing a collaborative optimization model and parameter constraint conditions based on cargo flow distribution and train bottom connection;
acquiring known data of the fast-moving banquet train, and establishing a time-space-banquet train three-dimensional service network according to the known data, the collaborative optimization model and the parameter constraint condition;
and decomposing the three-dimensional service network to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and generating a class running scheme and a vehicle bottom operation plan according to the minimum cost path information.
In another aspect, the present invention further provides an electronic device, including: a processor, a memory, a communication interface, and a communication bus; wherein,
the processor, the communication interface and the memory complete mutual communication through a communication bus;
the processor is used for calling the logic instructions in the memory so as to execute the cooperative optimization method of the railway express train running scheme and the train bottom application.
In another aspect, the present invention further provides a non-transitory computer readable storage medium storing computer instructions, which cause the computer to execute the method for collaborative optimization of the train operation scheme and train bottom operation.
The cooperative optimization method of the railway express train operation scheme and the train bottom application can reduce empty train running, improve the utilization rate of train bottom resources and reduce the operation cost of railway departments; on the other hand, the smooth development of daily transportation organization work of the railway express train is ensured, and the cooperative optimization result obtained by considering the organic connection of the railway express train and the organization work can provide more reliable input information for the compilation of the operation schedule of the railway express train.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a cooperative optimization method for a railway express train operation scheme and underbody application according to an embodiment of the present invention;
fig. 2 is a specific flowchart of a cooperative optimization method for a railway express train operation scheme and underbody application according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a cooperative optimization method for a railway express train operation scheme and train bottom application, which is shown in figure 1 and comprises the following steps:
s101: establishing a collaborative optimization model and parameter constraint conditions based on cargo flow distribution and train bottom connection;
in the step, cargo flow data and shift train bottom data are collected through a data acquisition device, and a collaborative optimization model and a parameter constraint condition which should be met by parameters in the collaborative optimization model are established based on cargo flow distribution and shift train bottom connection;
the data acquisition device records the cargo flow data and the shift train bottom data in advance, and the pre-recorded data is collected or input in advance.
Further, the optimization goal of the collaborative optimization model is that the operation profit is the maximum, wherein the collaborative optimization model is as follows:
wherein F is a goods flow, F is a goods flow set, a is a service arc section, A is a service arc section set, AvFor virtual service arc set, oaTo serve the starting node of arc a, ofIs the originating node of the flow f, rfFor the purpose of the transport income of the cargo flow f,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then, the cost of transportation through the service arc a for the cargo flow f, V the vehicle flow, V the set of vehicle flows,the cost of running vehicle stream v through service arc a,a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,
further, the parameter constraint condition includes:
the method comprises the following steps of cargo flow restriction, cargo flow balance restriction, vehicle bottom application quantity restriction, vehicle bottom and cargo flow coupling restriction and decision variable value restriction.
Wherein the cargo flow constraint indicates that the sum of the cargo flows serviced by the class must be less than or equal to the total cargo flow. Due to uncertainty of the cargo transport requirements of the class, the requirements which cannot be met can be completed by a common cargo train, and the cargo flow constraint is as follows:
wherein F is the goods flow, F is the goods flow set, a is the service arc section, AvFor virtual service arc sets, QfIs the flow rate of the cargo flow f, oaTo serve the starting node of arc a, ofBeing the starting node of the flow f of goods,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then,
the cargo flow balance constraint represents the cargo inflow of each node andthe outflow rate is required to satisfy the flow conservation relationAnd ensuring the inseparability of the same cargo flow on the unique space path for the variable of 0-1, wherein the cargo flow balance constraint is as follows:
wherein, a is a service arc segment,representing a set of service arcs starting from a node N, N being a three-dimensional service network node, N being a set of three-dimensional service network nodes, F being a set of cargo flows, oaTo serve the starting node of arc segment a,represents a set of service arcs to reach node n; daTo serve the arriving node for arc segment a,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then,ofis the starting node of the flow f, dfAn arrival node for the cargo flow f;
the vehicle flow balance constraint means that the vehicle inflow and outflow of each node satisfy the flow conservation relation, and simultaneouslyFor a variable of 0-1, the vehicle flow is ensured to be continued on a unique spatial path, namely a vehicle bottom fixed marshalling, and the vehicle flow balance is constrained to be：
Wherein, a is a service arc segment,representing a set of service arcs starting from a node n, n being a three-dimensional service network node, oaTo serve the starting node of arc segment a,represents a set of service arcs to reach node n; daTo serve the arriving node for arc segment a,a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,dvis an arrival node of the vehicle flow v; n is a three-dimensional service network node set, and V is a vehicle flow set;
the number constraint of the vehicle bottom application means that the number of the vehicle bottoms put into operation must be less than or equal to the number of the vehicle bottoms available for application, and the number constraint of the vehicle bottom application is as follows:
wherein V is a vehicle flow, V is a vehicle flow set, a is a service arc, and AvFor virtual service arc set, oaTo serve the starting node of arc a, ovIs the starting node of the vehicle flow v,a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,Vmaxthe number of the usable vehicle bottoms is shown;
the coupling constraint of the vehicle bottom and the cargo flow means that the sum of the cargo flow on the service arc section is smaller than the capacity of the arc section (determined by the programmed vehicle number of the vehicle bottom passing through the arc section and the load capacity of a unit vehicle), and the coupling constraint of the vehicle bottom and the cargo flow is as follows:
wherein V is a vehicle flow, V is a set of vehicle flows,a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,f is the flow of goods, F is the collection of the flow of goods,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then,Qfis the flow rate of the cargo flow f, EvFor the flow of the vehicle stream v,. epsilon.for the load of a vehicle；AsTo run a service arc set, AdAn arc set is waited to be served;
the decision variable value constraint represents the value range of each decision variable, and the decision variable value constraint is as follows:
wherein,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then,f is a goods flow, F is a goods flow set, a is a service arc section, and A is a service arc section set;a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,v is the vehicle flow and V is the set of vehicle flows.
It should be noted that the nodes in the service network represent work stations, the arcs represent transportation services, the transportation process of the goods can be regarded as distributing the flow of the goods on the service network, and the operation arrangement of the vehicle bottom can be regarded as distributing the flow of the vehicles on the service network. The matching of vehicle bottom resources and freight transportation requirements is realized by using the service network as a medium for the vehicle flow and the freight flow.
S102: acquiring known data of the fast-moving banquet train, and establishing a time-space-banquet train three-dimensional service network according to the known data, the collaborative optimization model and the parameter constraint condition;
in this step, the known data of the express shift train includes: cargo transportation requirements, vehicle bottom information and transportation cost;
wherein the cargo transportation needs comprise: the system comprises a goods starting station, a goods final station, goods flow, the departure time of goods and the latest arrival time of goods;
the vehicle bottom information comprises: the number of matching stations and grouped vehicles at the bottom of the vehicle;
the transportation cost includes: freight transportation revenue, freight transportation cost, shift line driving fixed cost, and vehicle route usage fees.
The time network is determined according to the departure time and the latest arrival time of the goods, the space network is determined according to the goods origin station and the goods destination station, the networks on the shift are determined according to the goods flow, the vehicle bottom information and the transportation cost, and the time network, the space network and the networks on the shift are combined to form the time-space-shift three-dimensional service network.
In specific implementation, known data of the express flight is obtained, and a time-space-flight three-dimensional service network is constructed based on model constraint conditions. The known data including freight transportation requirement, vehicle bottom information and railway freight transportation cost parameters are stored by means of the csv file, and the known data in the csv file is read into a computer program by using C # language under the Net platform. When a three-dimensional service network is constructed, all service network nodes are generated according to three dimensions of time, space and class, and then an operation service arc, a waiting service arc, a virtual outgoing arc of a cargo flow, a virtual final arc of the cargo flow, a virtual super arc of the cargo flow, a virtual outgoing arc of a vehicle flow, a virtual final arc of the vehicle flow, a virtual super arc of the vehicle flow, a transit service arc and a continuous service arc are constructed according to constraint conditions and known data.
S103: and decomposing the three-dimensional service network to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and generating a class running scheme and a vehicle bottom operation plan according to the minimum cost path information.
In the step, a Lagrange relaxation method is adopted to carry out dual decomposition on the three-dimensional service network established in the step S102, and a plurality of three-dimensional service sub-networks are generated; taking the shortest path of the goods flow and the shortest path of the vehicle flow in the three-dimensional service sub-network as the minimum cost path of the three-dimensional service sub-network; and traversing the shortest paths of a plurality of cargo flows and the shortest paths of a plurality of vehicle flows in a plurality of three-dimensional service sub-networks, wherein the path meeting the arc transportation capacity is a transportation path which is optimized in a coordinated mode.
Referring to fig. 2, the following describes the present step in detail:
(1) when the dual decomposition is performed on the three-dimensional service network established in step S102 by using the lagrangian relaxation method, the iteration number k is set to 0, all lagrangian multiplier values are initialized to 0, the iteration step length is set to a positive number, and the optimal lower bound solution LB is set*Infinity, optimal upper bound solution UB*＝+∞。
(2) And solving a Lagrangian dual, wherein the Lagrangian dual is as follows:
Max H(D)'；
where ρ isa、σaAre lagrange multipliers on arc segment a.
Further, the solving step includes:
to pairSolving the sub-problem of shortest path of cargo flow, and storing the shortest path and path length of cargo flow
To pairSolving the sub-problem of the shortest path of the vehicle flow, and storing the shortest path and the path length of the vehicle flow
Updating lagrange multipliers ρa、σaThe update formula is:
where k is the number of iterations αkDenotes the iteration step size at the k-th iteration, αkThe value of (A) is set according to actual needs, and only the following rules are required to be met:
and k → ∞, αk→0。
(3) Calculate the lower bound solution LBkAnd generates the optimal lower bound solution LB*＝max{LB*,LBk}; calculate the upper bound solution UBkAnd generating an optimal upper bound solution UB*＝min{UB*,UBk}。
Using the calculation of the lower bound solution LBkThe formula of (1) is:
wherein,for the shortest path length of the flow f at the kth iteration,is the shortest path length of the vehicle flow v at the k-th iteration.
Calculate the upper bound solution UBkThe method comprises the following steps:
a. and loading the ith vehicle flow to a service network according to the shortest vehicle flow path in the lower bound solution, if a plurality of vehicle bottoms act on the same shift queue in the shortest vehicle flow path, turning to b, otherwise, i is i +1, setting the transport capacity value of the arc section as the flow of the vehicle flow, and repeating the step.
b. Unloading the vehicle flow i, updating the transport capacity of the arc section, and setting the vehicle flow price of the arc section through which the existing vehicle flow passes as a maximum value; and solving the sub-problem of the shortest path of the vehicle flow i, recovering the vehicle flow price of the arc section after the sub-problem is finished, if all the vehicle flows are completely loaded, turning to the step c, and otherwise, turning to the step a.
c. Planning priority coefficient w according to cargo flowfAnd sorting the goods flow in a descending order. Cargo flow planning priority coefficient wfThe calculation formula of (2) is as follows:
in the formula,for the path length of the flow f at the k-th iteration, QfLarge flow for cargo flowIs small. m is 0.2 and n is 0.8.
d. And loading the ith cargo flow to a service network according to the shortest cargo flow path in the lower bound solution. If the cargo flow in the shortest cargo flow path exceeds the arc section transportation capacity, the process is switched to e, otherwise, i is i +1, and the step is repeated.
e. And unloading the cargo flow i from the service network, traversing the arc section of the service network, and setting the cargo flow price of the arc section, which is beyond the arc section transportation capacity, of the loaded cargo flow i as a maximum value. And (4) solving the sub-problem of the shortest path of the goods flow i, recovering the goods flow price of the arc section after the sub-problem is solved, and turning to f if all the goods flows are completely loaded, or turning to d if all the goods flows are not completely loaded.
f. Saving feasible solution, calculating upper bound solution UBkIf the iteration number k is larger than the preset maximum iteration number or the dual gap of the upper and lower boundaries meets the requirement, the Lagrangian relaxation algorithm is ended; otherwise k is k +1, and the Lagrangian dual is continuously solved.
In this step, a lagrange relaxation algorithm is adopted, an original problem is decomposed into a plurality of independent three-dimensional service network minimum cost path subproblems through dual decomposition, the minimum cost path subproblems of all the cargo flows and the vehicle flows are solved, a minimum cost path subproblem solving result (lower bound) is taken as heuristic information and is brought into the lagrange heuristic algorithm, the lower bound result is feasible according to the heuristic information to obtain an upper bound solution, and meanwhile, the solving result is output, and the method comprises the following steps: the shift train running scheme, the goods flow distribution result and the vehicle bottom operation plan.
Wherein, the bangliang operation scheme comprises: the shift train running time interval, the shift train grouping content, the shift train running path and the shift train acting as the train bottom;
a flow allocation result comprising: the system comprises a cargo flow starting time interval, a cargo flow arrival time interval, a loading class train and an operation path;
the vehicle bottom operation plan comprises: the section-out time period of the vehicle bottom, the section-back time of the vehicle bottom, the running path of the vehicle bottom and the vehicle bottom connection relationship.
From the above description, it can be seen that the cooperative optimization method for the train operation scheme and the train bottom application of the express train provided by the embodiment of the invention can reduce the empty train running, improve the utilization rate of train bottom resources and reduce the operation cost of railway departments; on the other hand, the problem that the operation scheme cannot be honored due to insufficient vehicle bottom resources can be avoided, and the smooth development of the daily transportation organization work of the railway express train is ensured; and the cooperative optimization result obtained by the organic connection of the two can provide more reliable input information for the compilation of the operation schedule of the railway express shift train.
In conclusion, the invention considers the factors of the train running time interval, the marshalling content, the goods flow transfer, the vehicle bottom attachment and connection and the like, effectively realizes the matching of the transportation demand and the supply and demand of vehicle bottom resources, can reduce the train operation cost, improve the operation benefit and ensure the smooth operation of the train transportation organization. And has the following beneficial effects:
(1) the invention realizes the cooperative optimization of the shift operation scheme and the train bottom operation plan, not only can reduce the shift operation cost, improve the operation benefit and ensure the smooth operation of transportation organization, but also can provide more reliable input information for the compilation of the operation schedule of the railway express shift by considering the cooperative optimization result obtained by the organic connection of the shift operation scheme and the train bottom operation plan.
(2) The invention provides a method for constructing a time-space-banglian three-dimensional service network, which can depict complex transportation organization processes such as cargo transfer, vehicle bottom connection and the like and creates favorable conditions for constructing a network flow-based collaborative optimization model.
(3) The invention adopts Lagrange relaxation algorithm, decomposes the original problem into a plurality of independent three-dimensional service network minimum cost path subproblems by relaxing the complex constraint into the objective function and utilizing dual decomposition, thereby realizing the divide-and-conquer of large-scale problems and having higher solving efficiency and solving quality.
An embodiment of the present invention provides an electronic device, and referring to fig. 3, the electronic device may include: a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call logic instructions in the memory 830 to perform the following method: establishing a collaborative optimization model and parameter constraint conditions based on cargo flow distribution and train bottom connection; acquiring known data of the fast-moving banquet train, and establishing a time-space-banquet train three-dimensional service network according to the known data, the collaborative optimization model and the parameter constraint condition; and decomposing the three-dimensional service network to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and generating a class running scheme and a vehicle bottom operation plan according to the minimum cost path information.
In addition, the logic instructions in the memory 830 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the method provided by the above method embodiments, for example, the method includes: establishing a collaborative optimization model and parameter constraint conditions based on cargo flow distribution and train bottom connection; acquiring known data of the fast-moving banquet train, and establishing a time-space-banquet train three-dimensional service network according to the known data, the collaborative optimization model and the parameter constraint condition; and decomposing the three-dimensional service network to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and generating a class running scheme and a vehicle bottom operation plan according to the minimum cost path information.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means/systems for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. The terms "upper", "lower", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the referred devices or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description of the present invention, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description. Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention is not limited to any single aspect, nor is it limited to any single embodiment, nor is it limited to any combination and/or permutation of these aspects and/or embodiments. Moreover, each aspect and/or embodiment of the present invention may be utilized alone or in combination with one or more other aspects and/or embodiments thereof.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. A cooperative optimization method for a railway express train operation scheme and train bottom application is characterized by comprising the following steps:
establishing a collaborative optimization model and parameter constraint conditions based on cargo flow distribution and train bottom connection;
acquiring known data of the fast-moving banquet train, and establishing a time-space-banquet train three-dimensional service network according to the known data, the collaborative optimization model and the parameter constraint condition;
and decomposing the three-dimensional service network to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and generating a class running scheme and a vehicle bottom operation plan according to the minimum cost path information.
2. The method as claimed in claim 1, wherein the optimization goal of the cooperative optimization model is to maximize operational profit.
3. The cooperative optimization method for the railway express-class train operation scheme and train bottom application according to claim 2, wherein the cooperative optimization model is as follows:
wherein F is a goods flow, F is a goods flow set, a is a service arc section, A is a service arc section set, AvFor virtual service arc set, oaTo serve the starting node of arc a, ofIs the originating node of the flow f, rfFor the purpose of the transport income of the cargo flow f,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then, the cost of transportation through the service arc a for the cargo flow f, V the vehicle flow, V the set of vehicle flows,serving a vehicle flow v throughThe cost of the operation of the arc segment a,a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,
4. the cooperative optimization method for the railway express-class train operation scheme and train bottom application according to claim 1, wherein the parameter constraint conditions comprise:
the method comprises the following steps of cargo flow restriction, cargo flow balance restriction, vehicle bottom application quantity restriction, vehicle bottom and cargo flow coupling restriction and decision variable value restriction.
5. The cooperative optimization method for the railway express train operation scheme and underbody application as claimed in claim 4,
the cargo flow constraint is:
wherein F is the goods flow, F is the goods flow set, a is the service arc section, AvFor virtual service arc sets, QfIs the flow rate of the cargo flow f, oaTo serve the starting node of arc a, ofBeing the starting node of the flow f of goods,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then,
the cargo flow balance constraint is:
wherein, a is a service arc segment,representing a set of service arcs starting from a node N, N being a three-dimensional service network node, N being a set of three-dimensional service network nodes, F being a set of cargo flows, oaTo serve the starting node of arc segment a,represents a set of service arcs to reach node n; daTo serve the arriving node for arc segment a,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then,ofis the starting node of the flow f, dfAn arrival node for the cargo flow f;
the vehicle flow balance constraint is:
wherein, a is a service arc segment,representing a set of service arcs starting from a node n, n being a three-dimensional service network node, oaTo serve the starting node of arc segment a,represents a set of service arcs to reach node n; daTo serve the arriving node for arc segment a,a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,dvis an arrival node of the vehicle flow v; n is a three-dimensional service network node set, and V is a vehicle flow set;
the number of applications of the vehicle bottom is restricted as follows:
wherein V is a vehicle flow, V is a vehicle flow set, a is a service arc, and AvFor virtual service arc set, oaTo serve the starting node of arc a, ovIs the starting node of the vehicle flow v,a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,Vmaxnumber of cars bottom available；
The coupling constraint of vehicle bottom and cargo flow is:
wherein V is a vehicle flow, V is a set of vehicle flows,a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,f is the flow of goods, F is the collection of the flow of goods,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then,Qfis the flow rate of the cargo flow f, EvIs the flow of the vehicle stream v, epsilon is the load of one vehicle; a. thesTo run a service arc set, AdAn arc set is waited to be served;
the decision variable value constraint is as follows:
wherein,a variable of 0 or 1, the flow f is via the service arc a, thenIf not, then,f is a goods flow, F is a goods flow set, a is a service arc section, and A is a service arc section set;a variable of 0 or 1, if the vehicle flow v is via the service arc aIf not, then,v is the vehicle flow and V is the set of vehicle flows.
6. The method of claim 1, wherein the known data of the express train comprises: cargo transportation requirements, vehicle bottom information and transportation cost;
wherein the cargo transportation needs comprise: the system comprises a goods starting station, a goods final station, goods flow, the departure time of goods and the latest arrival time of goods;
the vehicle bottom information comprises: the number of matching stations and grouped vehicles at the bottom of the vehicle;
the transportation cost includes: freight transportation revenue, freight transportation cost, shift line driving fixed cost, and vehicle route usage fees.
7. The method of claim 1, wherein the decomposing of the three-dimensional service network to generate a plurality of three-dimensional service sub-networks, determining the minimum cost path of each three-dimensional service sub-network, and generating the train operation scheme and the train bottom operation plan according to the minimum cost path information comprises:
carrying out dual decomposition on the three-dimensional service network by adopting a Lagrange relaxation method to generate a plurality of three-dimensional service sub-networks;
taking the shortest path of the goods flow and the shortest path of the vehicle flow in the three-dimensional service sub-network as the minimum cost path of the three-dimensional service sub-network;
and traversing the shortest paths of a plurality of cargo flows and the shortest paths of a plurality of vehicle flows in a plurality of three-dimensional service sub-networks, wherein the path meeting the arc transportation capacity is a transportation path which is optimized in a coordinated mode.
8. An electronic device, comprising: a processor, a memory, a communication interface, and a communication bus; wherein,
the processor, the communication interface and the memory complete mutual communication through a communication bus;
the processor is used for calling the logic instructions in the memory to execute the cooperative optimization method of the railway express train running scheme and the train bottom operation, which is disclosed by any one of claims 1 to 7.
9. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a method of co-optimizing a railroad express train operation scheme with underbody use as claimed in any one of claims 1 to 7.
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