A method and system for coordinated optimization of headway and train diagram considering fluctuation demand

Abstract

The application discloses a method and system for optimizing skylight and train diagram considering fluctuation demand, and relates to the technical field of skylight and train diagram optimization. The method comprises the following steps: obtaining the working state of track equipment, passenger and freight transportation demand, maintenance operation demand, passenger and freight train operation parameters, and maintenance operation cancellation cost parameters; constructing a skylight and train diagram optimization model considering fluctuation demand according to the above information; decomposing the initial model into a series of minimum cost sub-models by using a high-order term dimension reduction method and an alternating direction multiplier decomposition method; solving the minimum cost sub-models by using an improved heuristic algorithm based on an alternating direction multiplier framework, and outputting an optimal skylight scheme and train diagram under specific passenger and freight transportation demand. The application simultaneously considers passenger and freight train operation cost and maintenance operation cancellation cost, adjusts skylight length by changing the number of daily skylight maintenance operations, and flexibly matches passenger and freight transportation demand, so that the service level of trains is improved while the safety of railway transportation organization is ensured.

Description

Technical Field

[0001] This application relates to the field of skylight and operation chart optimization technology, and more specifically, to a method and system for collaborative optimization of skylight and operation chart considering fluctuation requirements. Background Technology

[0002] The timetable is a comprehensive plan for railway transportation, the foundation of railway traffic organization, and a tool for coordinating the production activities of various railway departments and units according to certain procedures. Track windows, as an indispensable component of the timetable, refer to the time reserved for construction and maintenance work by not marking train lines or adjusting / reducing train lines. It is a fundamental strategy for properly handling the contradictions between transportation organization and construction / maintenance. Because there are contradictions between train operation and maintenance work in terms of railway capacity utilization, it is necessary to properly coordinate their plans to balance transportation efficiency and safety. Currently, railway departments usually determine track windows based on manual experience after the timetable is compiled, and the form, time period, and duration of these windows are relatively fixed. However, the demand for bulk freight and passenger transport exhibits significant seasonal fluctuations, resulting in differentiated railway capacity demands at different times. Fixed track window schemes may lead to a mismatch between fluctuating passenger and freight demand and track window duration and maintenance resources, resulting in low track window fulfillment rates or work outside of track windows, threatening railway traffic safety, affecting transportation efficiency, and wasting maintenance resources. Therefore, it is necessary to consider the matching mechanism between the duration of maintenance windows and fluctuating demand, and on this basis, to reasonably coordinate maintenance windows and timetables in order to reduce train operating costs, maintenance window opening time offset costs, and maintenance work cancellation costs, so as to fully release railway transport capacity and ensure safe and efficient railway transport organization. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for co-optimizing skylights and running charts that take into account fluctuation requirements, so as to improve the above-mentioned problems.

[0004] To achieve the above objectives, in one aspect, this application provides a method for co-optimizing skylights and operation charts that considers fluctuating demand, the method comprising:

[0005] Obtain the working status of track equipment, passenger and freight transport demand, maintenance operation demand, passenger and freight train operation parameters, and maintenance operation cancellation cost parameters.

[0006] The operating parameters for passenger and freight trains include the operating sections of passenger and freight trains, the interval between departure and arrival times at stations, the travel time in different railway sections, the range of stop durations, and the unit travel time cost.

[0007] The maintenance cancellation cost parameter includes the cost of canceling maintenance work on track equipment in good condition.

[0008] Based on the acquired information, a collaborative optimization model for the skylight and running chart that takes into account fluctuation requirements is constructed.

[0009] The collaborative optimization model of the sunroof and timetable includes: objective function and constraints. The objective function is the minimum total cost consisting of train operating cost, sunroof opening time offset cost, and maintenance operation cancellation cost.

[0010] The train operating cost is the time cost of transporting passengers and goods, reflecting the timeliness of transportation. It is directly proportional to the transportation time, and the operating costs may differ between different classes of passenger trains and freight trains.

[0011] The sunroof opening time offset cost refers to the offset value between the sunroof start time and the expected start time window.

[0012] The maintenance cancellation cost refers to the safety hazards arising from canceling maintenance work on equipment that is in good working order.

[0013] The constraints include skylight feasibility constraints, train and skylight network flow balance constraints, track capacity resource constraints, and cross-scale consistency constraints.

[0014] The model for co-optimizing the skylight and orbital chart, taking into account fluctuating demand, is decomposed. The objective is linearized using a higher-order dimensionality reduction method, and the cross-scale consistency constraint and orbital capacity resource constraint are relaxed using an alternating direction multiplier decomposition method, thus decomposing the original model into a series of minimum-cost sub-models.

[0015] Output the transportation and maintenance plan under specific passenger and freight transportation needs. Based on the constraints and objective function, solve the sub-model using an improved heuristic algorithm based on the alternating direction multiplier framework to obtain the optimal sunroof scheme and operation diagram with the minimum total cost.

[0016] To address the aforementioned needs, this application also provides a system for the collaborative optimization of window and running chart considering fluctuation requirements, utilizing the aforementioned method for the collaborative optimization of window and running chart considering fluctuation requirements. The system includes a parameter acquisition module, a model construction module, a model decomposition module, and a model solving module.

[0017] The parameter acquisition module is used to acquire the working status of track equipment, passenger and freight transport demand, maintenance operation demand, passenger and freight train operation parameters, and maintenance operation cancellation cost parameters.

[0018] The model building module is used to establish a collaborative optimization model of the skylight and the running chart that takes into account fluctuation requirements.

[0019] The model decomposition module is used to decompose the window and running chart co-optimization model that takes into account fluctuation requirements into a series of minimum cost sub-models.

[0020] The model solving module is used to solve the collaborative optimization model of skylight and operation diagram considering fluctuating demand, and output the optimal skylight scheme and operation diagram under specific passenger and freight transportation demand.

[0021] The beneficial effects of this invention are:

[0022] (1) The collaborative optimization model of track windows and timetable considering fluctuating demand simultaneously considers train operating costs, track window opening time offset costs, and maintenance operation cancellation costs, thereby balancing railway transportation efficiency, service quality and operational safety.

[0023] (2) The model controls the amount of maintenance work on equipment in good working condition to dynamically match the length of the track window with fluctuating passenger and freight demand, thereby fully releasing the transportation capacity of the passenger and freight railway and increasing the level of refined transportation organization;

[0024] (3) Decision-makers can adjust the weight parameters of train operation and track closure implementation in the model objectives and flexibly output operation diagrams and track closure schemes that meet actual needs. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the collaborative optimization method for skylights and running charts that takes into account fluctuation requirements, as described in this embodiment of the invention.

[0027] Figure 2 This is a schematic diagram of the system structure for the collaborative optimization of the skylight and the running chart, which takes into account fluctuation requirements, as described in this embodiment of the invention. Detailed Implementation

[0028] Example 1

[0029] like Figure 1 As shown, this embodiment provides a method for co-optimizing the window and the operation chart that takes into account fluctuation requirements. The method includes steps S1, S2, S3 and S4.

[0030] Step S1: Obtain the working status of track equipment, passenger and freight transport demand, maintenance operation demand, passenger and freight train operation parameters, and maintenance operation cancellation cost parameters.

[0031] Railway operators can easily obtain the parameters mentioned above by analyzing historical statistical data and monitoring equipment data for specific application scenarios, and use these parameters as inputs to this method.

[0032] Step S2: Based on the information obtained above, construct a collaborative optimization model for the window and operation schedule of fluctuating passenger and freight transport demand.

[0033] The collaborative optimization model for track maintenance windows and timetables includes: an objective function and constraints. The objective function refers to the minimum total cost comprising train operating costs, track maintenance window opening time offset costs, and maintenance work cancellation costs. Specifically, the objective function is:

[0034] (1)

[0035] (2)

[0036] (3)

[0037] (4)

[0038] Among them, train operating costs middle, Indicates train Select Arc Cost, in yuan , Indices representing railway station indices An index representing a time point; For 0-1 decision variables, if the train Select Arc but ,otherwise ; Gather for the train; For train The selectable arc set is determined by the input passenger and freight train operating parameters; the cost of offsetting the start time of the track maintenance window. middle, Indicates skylight Select Arc The offset cost, i.e., the cost at the start time. Cost of offset between the expected start time window of the sunroof, in yuan; Let the decision variable be 0-1. If the sunroof option is the first... Planting time skylight Select Arc but ,otherwise ; For the collection of skylights; For skylight The selectable set of arcs is determined by the input customer maintenance operation requirements parameters; maintenance operation cancellation cost. middle, Indicates the first Costs incurred due to the cancellation of maintenance work for the sunroof solution, in yuan. , For parameters of 0-1, when the first... This type of maintenance work will be done on the first The implementation of the skylight scheme ,otherwise , This is a collection of maintenance work inside the sunroof; For 0-1 decision variables, if at the station The choice of sunroof The skylight solution ,otherwise ; For the station index set; A set of sunroof solutions; minimum total cost middle, These represent the weighting parameters for train operation and sunroofing implementation, respectively. And satisfy .

[0039] The constraints include track window feasibility constraints, train and track window network flow balance constraints, track capacity resource constraints, and cross-scale consistency constraints; the specific constraints are as follows:

[0040] (5)

[0041] (6)

[0042] (7)

[0043] (8)

[0044] (9)

[0045] (10)

[0046] Formulas (5)-(8) represent the feasibility constraints for the skylight. Formula (5) indicates the station... Only one skylight scheme can be used. Formula (6) indicates that equipment warned by the track condition monitoring equipment must be repaired, where the set This refers to the set of maintenance tasks for equipment that is being monitored and warned by track monitoring devices. This represents the set of track maintenance schedules. Formula (7) indicates that the number of maintenance operations completed on the railway must be greater than a minimum value. This minimum value is determined by the operations department based on the overall working status of the track. If the working status is poor, the minimum value needs to be increased; otherwise, the minimum value needs to be decreased. Formula (8) is the flow balance constraint between the train and track maintenance schedule network. Since the flow balance constraint forms for the train and track maintenance schedule are the same, it is used here. Unified representation and , Indicates train or sunroof The originating or starting station, Indicates train or sunroof The start or commencement time, Indicates train or sunroof The arrival or end time, Indicates the maximum planning time. Let represent the set of trains and skylights. Formula (9) represents the track capacity resource constraint, which guarantees that at most in the set There is an arc in the middle that is blocked by the train or sunroof Occupation, in which set Indicates station At any moment Incompatible arc set, for External station index, for External time index, For the set of times, set The requirements are determined based on the input passenger and freight transport demand and maintenance operation demand. Formula (10) is a cross-scale consistency constraint, which links the skylight scheme with the decision variables of the skylight arc in the network flow framework, ensuring the consistency between the skylight scheme and the skylight arc selection.

[0047] Step S3: Decompose the window and operation chart co-optimization model considering fluctuation demand.

[0048] Because the objectives and constraints of the train and the sunroof in the joint optimization model of the sunroof and the timetable are coupled, the difficulty of solving the model is increased. Therefore, the complex constraints (9)(10) are relaxed into the objective function by using the alternating direction multiplier decomposition method, and the objective function is linearized by using the high-order term dimensionality reduction method, thereby decomposing the objective function. The decomposed costs of the train and the sunroof, and the selection costs of the train and the sunroof arc in the network flow framework are as follows:

[0049] (11)

[0050] (12)

[0051] (13)

[0052] (14)

[0053] Formula (11) represents the train after decomposition. The cost, of which Indicates train Choice costs in network flow frameworks; This represents the first penalty parameter in the alternating direction multiplier decomposition method, with an initial value of 1. Indicates except for trains Beyond and Time The number of incompatible arcs; The Lagrange multiplier associated with the orbital capacity resource constraint (9) is initially set to 1. Formula (12) represents the decomposed sky window. The cost, of which Indicates skylight Choice costs in network flow frameworks; Including skylight Beyond and Time The number of incompatible arcs; This represents the second penalty parameter in the alternating direction multiplier decomposition method; This represents the Lagrange multiplier associated with the cross-scale consistency constraint (10), initially set to 1. The variable representing the sunroof scheme obtained after fixing the train sub-cost (11) is called. The value of . Formula (13) represents the train Choice cost in the network flow framework. Equation (14) represents the window. Choice cost in the network flow framework.

[0054] Step S4: Solve the sub-model using an improved heuristic algorithm based on an alternating direction multiplier framework to obtain the optimal skylight scheme and operation diagram with the minimum total cost. The solution process of the improved heuristic algorithm based on an alternating direction multiplier framework is as follows:

[0055] S41. Initialize penalty parameters Lagrange multipliers Number of iterations Increasing value and increasing percentage Optimal lower bound value Optimal upper bound value By utilizing the acquired passenger and freight transport demand, maintenance operation requirements, and passenger and freight train operating parameters, the set of incompatible arcs for trains or sunroof windows is determined. .

[0056] S42. Solving the selection cost of trains and sunroof arcs in a network flow framework based on the Lagrange relaxation method. , The minimum variable cost path for all trains and sunroofs is determined by formulas (15) and (16) respectively. The Lagrange relaxation algorithm is then used to generate a pure lower bound solution. ,based on Update the optimal lower bound solution .

[0057] (15)

[0058] (16)

[0059] S43. Update the arc cost according to formulas (13) and (14), and search for the minimum variable cost path for all trains and sunroofs, and calculate the upper bound solution. ,based on renew .

[0060] S44, Let the number of iterations be... Update the Lagrange multipliers according to formulas (17) and (18). Determine if the upper bound solution is feasible. If not, proceed to step S42. If yes, the algorithm continues.

[0061] (17)

[0062] (18)

[0063] S45, based on Calculate the optimal algorithm gap value, and based on Update the optimal algorithm gap value;

[0064] S46. Determine the number of iterations Has the maximum setting been reached? If the condition is met, the algorithm terminates and outputs the optimal skylight scheme and operation diagram under specific passenger and freight flow requirements; otherwise, the algorithm continues and returns to step S42.

[0065] Example 2

[0066] like Figure 2 As shown, this embodiment provides a system for co-optimizing window and running chart considering fluctuation requirements. It uses the above-mentioned method for co-optimizing window and running chart considering fluctuation requirements. The system includes a parameter acquisition module 201, a model construction module 202, a model decomposition module 203, and a model solving module 204.

[0067] The parameter acquisition module 201 is used to acquire the working status of the track equipment, passenger and freight transport demand, maintenance operation demand, passenger and freight train operation parameters, and maintenance operation cancellation cost parameters.

[0068] The model building module 202 is used to establish a collaborative optimization model of the skylight and the running chart that takes into account fluctuation requirements.

[0069] The model decomposition module 203 is used to decompose the window and running chart collaborative optimization model that takes into account fluctuation requirements into a series of minimum cost sub-models.

[0070] The model solving module 204 is used to solve the collaborative optimization model of skylight and operation diagram considering fluctuating demand, and output the optimal skylight scheme and operation diagram under specific passenger and freight transportation demand.

[0071] Furthermore, the model building module 202 includes an objective function building module 2021 and a constraint condition building module 2022.

[0072] The objective function construction module 2021 is used to construct formulas for calculating train operating costs, track opening time offset costs, maintenance work cancellation costs, and minimum total cost.

[0073] The constraint construction module 2022 is used to construct constraints on the feasibility of track closures, the balance constraints of train and track closure network flow, track capacity resources, and cross-scale consistency constraints.

[0074] The model decomposition module 203 includes an objective function decomposition module and a constraint condition decomposition module.

[0075] The objective function decomposition module 2031 is used to linearize the higher-order terms in the objective function using the higher-order term dimensionality reduction method, and decompose the original objective into sub-objectives.

[0076] The constraint decomposition module 2032 is used to relax cross-scale consistency constraints and orbital capacity resource constraints by using the alternating direction multiplier decomposition method, decomposing the original constraints into sub-constraints.

[0077] The model solving module 204 includes an algorithm parameter initialization module 2041, a spatiotemporal arc cost update module 2042, a minimum time-varying cost path search module 2043, a multiplier update module 2044, an algorithm termination condition judgment module 2045, and a window scheme and running graph output module 2046.

[0078] Algorithm parameter initialization module 2041 is used to initialize penalty parameters. Lagrange multipliers Number of iterations Increasing value and increasing percentage Optimal lower bound value Optimal upper bound value By utilizing the acquired passenger and freight transport demand, maintenance operation requirements, and passenger and freight train operating parameters, the set of incompatible arcs for trains or sunroof windows is determined. .

[0079] Spatiotemporal arc cost update module 2042 is used to solve the choice cost between trains and sunroof arcs in the network flow framework. .

[0080] The minimum time-varying cost path search module 2043 is used to search for the minimum time-varying cost path for all trains and sunroofs, and uses the Lagrange relaxation algorithm to generate a pure lower bound solution. Update the optimal lower bound solution Calculate the upper bound solution Update the optimal upper bound solution .

[0081] Multiplier update module 2044 is used to update Lagrange multipliers. Then determine whether the upper bound solution is feasible. If not, go to step S42. If yes, the program continues.

[0082] The algorithm termination condition judgment module 2045 is used to calculate and update the optimal algorithm gap value. Determine the number of iterations Has the maximum setting been reached? If yes, the algorithm terminates; otherwise, the algorithm continues and returns to step S42.

[0083] The skylight scheme and operation diagram output module 2046 is used to utilize the determined optimal upper bound solution. Output the optimal sunroof scheme and operation diagram under specific passenger and freight flow requirements.

[0084] It should be noted that the specific methods by which the various modules of the above system perform their operations have been described in detail in the embodiments of the relevant method, and will not be elaborated here.

Claims

1. A method for co-optimizing window and running chart considering fluctuating demand, characterized in that, include: Obtain the working status of track equipment, passenger and freight transport demand, maintenance operation demand, passenger and freight train operation parameters, and maintenance operation cancellation cost parameters; Based on the information obtained above, a collaborative optimization model for skylights and running charts that takes into account fluctuation requirements is constructed. The initial model is decomposed into a series of minimum-cost sub-models using the high-order term dimensionality reduction method and the alternating direction multiplier decomposition method. The minimum cost sub-model is solved by an improved heuristic algorithm based on the alternating direction multiplier framework, and the optimal sunroof scheme and operation diagram under specific passenger and freight transportation needs are output.

2. The method for coordinated optimization of skylights and operational charts considering fluctuation requirements according to claim 1, characterized in that, Based on the information obtained above, a collaborative optimization model for the skylight and running chart, considering fluctuation requirements, is constructed, including: The collaborative optimization model of track maintenance windows and timetables includes an objective function and constraints. The objective function is the minimum total cost consisting of train operating costs, track maintenance window opening time offset costs, and maintenance operation cancellation costs. The constraints include track maintenance window feasibility constraints, train and track maintenance window network flow balance constraints, track capacity resource constraints, and cross-scale consistency constraints.

3. The method for coordinated optimization of skylights and operational charts considering fluctuation requirements according to claim 1, characterized in that, The initial model is decomposed into a series of minimum cost sub-models using the high-order term dimensionality reduction method and the alternating direction multiplier decomposition method.

4. The method for coordinated optimization of skylights and operational charts considering fluctuation requirements according to claim 1, characterized in that, The minimum cost sub-model is solved by an improved heuristic algorithm based on the alternating direction multiplier framework, and the optimal sunroof scheme and operation diagram under specific passenger and freight transportation needs are output.

5. A system for co-optimizing skylights and operational charts considering fluctuating demand, comprising using the co-optimization method for skylights and operational charts considering fluctuating demand as described in any one of claims 1-4, characterized in that, It includes a parameter acquisition module, a model building module, a model decomposition module, and a model solving module; The parameter acquisition module is used to acquire the working status of the track equipment, passenger and freight transport demand, maintenance operation demand, passenger and freight train operation parameters, and maintenance operation cancellation cost parameters. The model building module is used to establish a collaborative optimization model of the skylight and the running chart that takes into account fluctuation requirements; The model decomposition module is used to reduce the dimensionality of the objective function, relax cross-scale consistency constraints and orbital capacity resource constraints, and decompose the window and operation diagram co-optimization model that considers fluctuation requirements into a series of minimum cost sub-models. The model solving module is used to solve the collaborative optimization model of skylight and operation diagram considering fluctuating demand, and output the optimal skylight scheme and operation diagram under specific passenger and freight transportation demand.

6. The system for coordinated optimization of skylights and operational charts considering fluctuation requirements according to claim 5, characterized in that, The model building module includes an objective function building module and a constraint condition building module; The objective function construction module is used to construct formulas for calculating train operating costs, track opening time offset costs, maintenance work cancellation costs, and minimum total costs. The constraint construction module is used to construct constraints on the feasibility of track closures, the balance constraints between train and track closure network flows, track capacity resources, and cross-scale consistency constraints.

7. A system for coordinated optimization of skylights and operational charts considering fluctuation requirements, as described in claim 5, is characterized in that, The model decomposition module includes an objective function decomposition module and a constraint condition decomposition module; The objective function decomposition module is used to linearize the higher-order terms in the objective function using the higher-order term dimensionality reduction method, and decompose the original objective into sub-objectives; The constraint decomposition module is used to relax cross-scale consistency constraints and orbital capacity resource constraints by using the alternating direction multiplier decomposition method, decomposing the original constraints into sub-constraints.

8. The system for coordinated optimization of skylights and operational charts considering fluctuation requirements according to claim 5, characterized in that, The model solving module includes an algorithm parameter initialization module, a spatiotemporal arc cost update module, a minimum time-varying cost path search module, a multiplier update module, an algorithm termination condition judgment module, and an optimal window scheme and running graph output module.

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