High-speed railway train operation adjusting method and device based on double targets
阅读说明：本技术 一种基于双目标的高速铁路列车运行调整方法及装置 (High-speed railway train operation adjusting method and device based on double targets ) 是由 丁舒忻 张琦 张秀广 张涛 王涛 王荣笙 周晓昭 刘黎 李智 于 2021-09-09 设计创作，主要内容包括：本发明公开了一种基于双目标的高速铁路列车运行调整方法及装置。其中,该方法包括：获取高速铁路列车运行信息,其中,所述列车运行信息包括：运行计划、晚点信息；根据所述运行信息计算理想点和最差点；根据所述理想点和最差点,生成方案调整数据；根据所述方案调整数据,得到帕累托最优前沿。本发明解决了现有技术中的决策者需要知道对不同优化目标的偏好,从而确定加权和法中的权重。然而,在实际过程中问题的全局偏好信息并不能准确获得。此外,采用加权法无法得到对应的非凸前沿,在Pareto前沿之外的一些不必要的计算,同时无法保证得到解是不是在所有Pareto前沿上的点,优化目标超过2个时会大大增加求解时间的技术问题。(The invention discloses a method and a device for adjusting the operation of a high-speed railway train based on double targets. Wherein, the method comprises the following steps: acquiring the running information of the high-speed railway train, wherein the running information of the train comprises: operation plan, late point information; calculating an ideal point and a worst point according to the operation information; generating scheme adjustment data according to the ideal points and the worst points; and adjusting data according to the scheme to obtain the pareto optimal leading edge. The invention solves the problem that the decision maker in the prior art needs to know the preference of different optimization targets so as to determine the weight in the weighting sum method. However, in practice global preference information for the problem is not accurately available. In addition, the corresponding non-convex front edge cannot be obtained by adopting a weighting method, unnecessary calculation is carried out except for the Pareto front edge, and meanwhile, the fact that whether the obtained solution is a point on all the Pareto front edges or not cannot be guaranteed, so that the technical problem that the solution time is greatly increased when the number of the optimization targets exceeds 2 is solved.)
1. A high-speed railway train operation adjusting method based on double targets is characterized by comprising the following steps:
acquiring the running information of the high-speed railway train, wherein the running information of the train comprises: operation plan, late point information;
calculating an ideal point and a worst point according to the operation information;
generating scheme adjustment data according to the ideal points and the worst points;
and adjusting data according to the scheme to obtain the pareto optimal leading edge.
2. The method of claim 1, wherein generating recipe adjustment data based on the ideal points and the worst points comprises:
determining the value range of a suboptimal target according to the ideal point and the worst point;
setting a constraint numerical value as the maximum value of the value range of the suboptimal target;
generating the recipe adjustment data based on the constraint value.
3. The method of claim 2, wherein after said calculating the solution adjustment data according to the constraint values, the method further comprises:
updating the constraint value according to the scheme adjustment data;
judging whether the constraint numerical value is the minimum value of the value range of the suboptimal target, and generating a judgment result, wherein the judgment result comprises: yes, no.
4. The method of claim 3, wherein adjusting the data according to the scheme to obtain a pareto optimal leading edge comprises:
when the judgment result is yes, generating a pareto optimal solution according to the scheme adjustment data;
and calculating to obtain the pareto optimal leading edge according to the pareto optimal solution.
5. A dual-target based high speed railway train operation adjustment device, comprising:
the acquisition module is used for acquiring the train operation information of the high-speed railway, wherein the train operation information comprises: operation plan, late point information;
the calculation module is used for calculating an ideal point and a worst point according to the operation information;
the generating module is used for generating scheme adjusting data according to the ideal point and the worst point;
and the optimal module is used for adjusting data according to the scheme to obtain the pareto optimal leading edge.
6. The apparatus of claim 5, wherein the generating module comprises:
the determining unit is used for determining the value range of the suboptimal target according to the ideal point and the worst point;
the setting unit is used for setting a constraint numerical value as the maximum value of the value range of the suboptimal target;
and the generating unit is used for generating the scheme adjusting data according to the constraint numerical value.
7. The apparatus of claim 6, further comprising:
the updating module is used for updating the constraint numerical value according to the scheme adjusting data;
a judging module, configured to judge whether the constraint value is a minimum value of the value range of the suboptimal target, and generate a judgment result, where the judgment result includes: yes, no.
8. The apparatus of claim 7, wherein the optimization module comprises:
the generating unit is used for generating a pareto optimal solution according to the scheme adjusting data when the judging result is yes;
and the calculation unit is used for calculating to obtain the pareto optimal leading edge according to the pareto optimal solution.
9. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 4.
10. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of any one of claims 1 to 4.
Technical Field
The invention relates to the field of high-speed train scheduling control, in particular to a method and a device for adjusting the running of a high-speed railway train based on double targets.
Background
With the development of high-speed railways, the mileage of high-speed railways reaches 3.79 kilometers in China by the end of 2020. High speed train operation has a "high density" feature. However, when an emergency occurs during the operation of the train, the train is late, and the normal operation of the train and the normal travel of passengers are seriously affected. In this case, train operation plan adjustment is indispensable. The train operation adjustment problem is an NP-hard problem. Along with the increase of the scale of the problem, the model solution becomes difficult, the traditional manual experience method is not suitable any more, and the obtained solution is not efficient for rapidly recovering the normal operation of the train. Therefore, how to ensure the safe, orderly and punctual running of the train is the key of the running adjustment problem of the high-speed railway train.
In the existing multi-target train operation adjustment decision-making method before consideration, aiming at the conditions of delay and speed limit of trains, the technology considers the minimum delay cost and the number of the trains which are seriously influenced, establishes a single-target optimization problem through linear weighting and adopts a solver to calculate. (journal articles quoted: Wang L, Qin Y, Xu J, et al. A Fuzzy Optimization Model for High-Speed Railway timing recovery [ J ]. Discrete Dynamics in Nature and Society,2012:827073.) however, this technique has the drawback that the decision maker needs to know the preferences for different Optimization objectives in order to determine the weights in the weight sum method. However, in practice global preference information for the problem is not accurately available. In addition, a corresponding non-convex front edge cannot be obtained by using a weighting method.
In the existing multi-target train operation adjustment and decision-making method after consideration, aiming at the situation that a plurality of station tracks are blocked, the minimum passenger dissatisfaction (transfer time), operation cost and adjustment cost are considered, an integer programming model is established, and a Pareto front edge is obtained by solving through CPLEX in combination with an epsilon-constraint method. (journal articles quoted: Binder S, Maknoon Y, Bierlaire M. the Multi-Objective Railway Timetable reserving purifying Part [ J ]. Transmission Research Part C: emery Technologies,2017,78:78-94.) however, the epsilon-constraint method in this technique has some drawbacks: (1) some unnecessary computations beyond the Pareto frontier; (2) the obtained solution is not guaranteed by all points on the Pareto front edge; (3) the solution time is greatly increased when the optimization objective exceeds 2.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method and a device for adjusting the running of a high-speed railway train based on double targets, which at least solve the problem that a decision maker in the prior art needs to know the preference of different optimization targets so as to determine the weight in a weighting sum method. However, in practice global preference information for the problem is not accurately available. In addition, the corresponding non-convex front edge cannot be obtained by adopting a weighting method, unnecessary calculation is carried out except for the Pareto front edge, and meanwhile, the fact that whether the obtained solution is a point on all the Pareto front edges or not cannot be guaranteed, so that the technical problem that the solution time is greatly increased when the number of the optimization targets exceeds 2 is solved.
According to an aspect of the embodiment of the invention, a method for adjusting the operation of a high-speed railway train based on double targets is provided, which comprises the following steps: acquiring the running information of the high-speed railway train, wherein the running information of the train comprises: operation plan, late point information; calculating an ideal point and a worst point according to the operation information; generating scheme adjustment data according to the ideal points and the worst points; and adjusting data according to the scheme to obtain the pareto optimal leading edge.
Optionally, the generating of the scheme adjustment data according to the ideal point and the worst point includes: determining the value range of a suboptimal target according to the ideal point and the worst point; setting a constraint numerical value as the maximum value of the value range of the suboptimal target; generating the recipe adjustment data based on the constraint value.
Optionally, after the calculating the recipe adjustment data according to the constraint value, the method further includes: updating the constraint value according to the scheme adjustment data; judging whether the constraint numerical value is the minimum value of the value range of the suboptimal target, and generating a judgment result, wherein the judgment result comprises: yes, no.
Optionally, the adjusting data according to the scheme to obtain the pareto optimal leading edge includes: when the judgment result is yes, generating a pareto optimal solution according to the scheme adjustment data; and calculating to obtain the pareto optimal leading edge according to the pareto optimal solution.
According to another aspect of the embodiments of the present invention, there is also provided a dual-target-based high-speed railway train operation adjusting apparatus, including: the acquisition module is used for acquiring the train operation information of the high-speed railway, wherein the train operation information comprises: operation plan, late point information; the calculation module is used for calculating an ideal point and a worst point according to the operation information; the generating module is used for generating scheme adjusting data according to the ideal point and the worst point; and the optimal module is used for adjusting data according to the scheme to obtain the pareto optimal leading edge.
Optionally, the generating module includes: the determining unit is used for determining the value range of the suboptimal target according to the ideal point and the worst point; the setting unit is used for setting a constraint numerical value as the maximum value of the value range of the suboptimal target; and the generating unit is used for generating the scheme adjusting data according to the constraint numerical value.
Optionally, the apparatus further comprises: the updating module is used for updating the constraint numerical value according to the scheme adjusting data; a judging module, configured to judge whether the constraint value is a minimum value of the value range of the suboptimal target, and generate a judgment result, where the judgment result includes: yes, no.
Optionally, the optimizing module includes: the generating unit is used for generating a pareto optimal solution according to the scheme adjusting data when the judging result is yes; and the calculation unit is used for calculating to obtain the pareto optimal leading edge according to the pareto optimal solution.
According to another aspect of the embodiment of the invention, a nonvolatile storage medium is further provided, and the nonvolatile storage medium comprises a stored program, wherein the program controls equipment where the nonvolatile storage medium is located to execute a dual-target-based high-speed railway train operation adjustment method during operation.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions are executable to perform a dual-target based adjustment method for high speed railway train operation.
In the embodiment of the invention, the method for acquiring the train operation information of the high-speed railway is adopted, wherein the train operation information comprises the following steps: operation plan, late point information; calculating an ideal point and a worst point according to the operation information; generating scheme adjustment data according to the ideal points and the worst points; the method for obtaining the pareto optimal leading edge by adjusting data according to the scheme solves the problem that a decision maker in the prior art needs to know the preference of different optimization targets so as to determine the weight in the weighting sum method. However, in practice global preference information for the problem is not accurately available. In addition, the corresponding non-convex front edge cannot be obtained by adopting a weighting method, unnecessary calculation is carried out except for the Pareto front edge, and meanwhile, the fact that whether the obtained solution is a point on all the Pareto front edges or not cannot be guaranteed, so that the technical problem that the solution time is greatly increased when the number of the optimization targets exceeds 2 is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flowchart of an application scenario implementation of a method for adjusting the operation of a high-speed railway train based on dual targets according to an embodiment of the present invention;
FIG. 2 is a schematic diagram showing a comparison of differences of non-dominant fronts obtained by modifying the ε -constraint method and the weighting method in different late scenes, according to an embodiment of the present invention;
FIG. 3 is a flow chart of a dual-target based adjustment method for high speed railway train operation according to an embodiment of the present invention;
fig. 4 is a block diagram illustrating a structure of a dual-target-based high-speed railway train operation adjusting apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a method embodiment of a dual-target based high speed railway train operation adjustment method, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than that described herein.
Example one
Fig. 3 is a flowchart of a method for regulating the operation of a high-speed railway train based on dual targets according to an embodiment of the present invention, as shown in fig. 3, the method includes the following steps:
step S302, obtaining the running information of the high-speed railway train, wherein the running information of the train comprises: operation plan, night information.
And step S304, calculating ideal points and worst points according to the operation information.
And S306, generating scheme adjustment data according to the ideal points and the worst points.
And S308, adjusting data according to the scheme to obtain the pareto optimal leading edge.
Optionally, the generating of the scheme adjustment data according to the ideal point and the worst point includes: determining the value range of a suboptimal target according to the ideal point and the worst point; setting a constraint numerical value as the maximum value of the value range of the suboptimal target; generating the recipe adjustment data based on the constraint value.
Optionally, after the calculating the recipe adjustment data according to the constraint value, the method further includes: updating the constraint value according to the scheme adjustment data; judging whether the constraint numerical value is the minimum value of the value range of the suboptimal target, and generating a judgment result, wherein the judgment result comprises: yes, no.
Optionally, the adjusting data according to the scheme to obtain the pareto optimal leading edge includes: when the judgment result is yes, generating a pareto optimal solution according to the scheme adjustment data; and calculating to obtain the pareto optimal leading edge according to the pareto optimal solution.
Specifically, as shown in fig. 1, an embodiment of the present invention provides a method for adjusting the operation of a high-speed railway train based on two targets, which mainly includes: firstly, establishing a double-target train operation plan adjustment model
(1) The train operation plan adjustment model takes into account the following assumptions:
1. the adjustment mode of the operation plan only comprises the adjustment of the arrival and departure time and the sequence of the train, and the train number cancellation is not considered;
2. the emergency event comprises a train interval running late point and a station running late point;
3. the high-speed train is considered to run on the complex line, and the uplink line and the downlink line are not interfered with each other. Only downlink adjustments are considered in the model;
4. stations and sections in the train operation diagram are numbered from top to bottom in sequence, and the total number of the stations is 1 more than the total number of the sections according to the sequence of the stations, the sections, the stations, the sections and the stations;
5. and the limitation of the butt joint departure capability of station tracks is not considered.
(2) The train operation plan adjustment model takes into account the following model parameters and decision variables, see table 1:
TABLE 1 model parameters and decision variable description
(3) The train operation plan adjustment model considers the following objective functions:
1. deviation of train running time
The train running time deviation in the model is mainly obtained by calculating the difference between the adjusted time and the time of the original plan, and is calculated as follows:
2. train operation adjustment cost
The train operation adjustment cost is mainly obtained by calculating the number of times of the train at a later point, and is calculated as follows:
in the formula, sign () is a sign function, and the sign of the corresponding parameter is returned. Since the train running time after adjustment is later than the original planning time, the symbolic function can calculate the adjustment times of the train due to the late point.
(4) The train operation plan adjustment model takes into account the following constraints:
in order to ensure the running safety of the train, the train receiving and dispatching capacity, the station passing capacity and the section passing capacity are reasonably utilized, and the constraint conditions of the model are discussed in detail.
1. Station minimum operating time constraint
The stop time of the train is enough to meet the requirement of passengers for getting on or off the train. Therefore, in the train operation adjustment plan, the stop time of the train must not be less than the minimum stop time of the train at the station.
2. Time division constraint for interval operation
The minimum running time of the interval is mainly related to the states of trains and lines, the speed limit of the interval and other factors. The method comprises start-stop time division and interval minimum operation time division. In addition, for a train i with a section k arriving late, itThe update is the sum of the actual interval running time and the arrival late time, namely
3. Minimum tracking interval constraint
In the formula, V-V and A represent respectively "or" and can be calculated by taking the maximum value and the minimum value of two numbers. Formulas (5) and (6) are respectively used for tracking interval constraint when the train starts and arrives, and formula (7) ensures that the sequence of two trains in the interval is unique.
For two or more trains running in the same interval, in order to ensure safety, a certain interval time exists between every two continuous trains. Here, it is assumed that the train operates at a constant speed in the interval, and therefore, it is only necessary to ensure that the train meets the constraint when entering and leaving the station.
4. Late train restraint
Equations (8) and (9) are station operation late point constraints and interval operation late point constraints, respectively.
5. Decision variable constraints
(5) Model processing
Since sign (·) sign function exists in equation (2), it needs to be converted into a linear model for processing. Defining an intermediate variable t1And t2The method comprises the following steps:
by replacing the parameters in the formula (2) with the parameters in the formula (12), a mixed integer linear programming model can be obtained. The model (P0) was established as follows:
solving by adopting an improved epsilon-constraint method and a GUROBI solver:
aiming at the dual-target mixed integer linear programming model, solving is carried out by improving an epsilon-constraint method and combining a GUROBI solver, and train operation plans under different adjustment costs are obtained. In the above two-target train operation adjustment problem, the corresponding constraint single-target optimization model (P1) is:
equations (3) - (11), (14) - (18), the calculation process to improve the epsilon-constraint method includes the following steps:
step 1: and inputting a train operation plan and the night information. The train operation plan comprises the number of trains, the train receiving and dispatching time of each train, the number of stations and intervals in the high-speed railway line, the minimum operation time of the intervals, the minimum operation time of the stations and the minimum tracking interval of adjacent trains. The delay information comprises the departure delay and arrival delay of the train at the station.
Step 2: an ideal point is calculated. Solving the optimization model (P0) without the formula (13) to obtainToSolving the optimization model (P0) without the formula (1) to obtain
And step 3: the worst point is calculated. Solving the optimization model (P0) without equation (13), adding additional constraintsTo obtainSolving the optimization model (P0) without equation (1), adding additional constraintsTo obtain
And 4, step 4: and determining a value range of the suboptimal target, and setting the constraint as the maximum value of the suboptimal target. The target function Z can be obtained according to the ideal point and the worst point2In the range ofTherefore, the maximum can be solvedThe individual constrained single object models (P1) result in corresponding Pareto fronts. First orderA corresponding first model is obtained (P1).
And 5: the adjustment scheme is calculated under the set constraints. Solving the model (P1) to obtain the current optimal solution x*Record solution [ Z ]1(x*),Z2(x*)]。
Step 6: the constraint value is updated. Let e2＝Z2(x*)-1。
And 7: judging whether the constraint value is the minimum value of the suboptimal target, if soAnd returning to the step 5, and continuously calculating the optimal solution corresponding to the new constraint model (P1). If it is notStep 8 is continued.
And 8: outputting Pareto leading edge. And (5) merging the solutions obtained by solving the model (P1) under different constraints in the step 5, removing the dominant solution, and finally forming the non-dominant front edge corresponding to the model (P0).
In addition, if the decision maker is interested in only part of Pareto frontier, it can be in the objective function Z2Range of (1)And (4) carrying out internal selection optimization solution on the partial model (P1) to obtain partial interested non-dominated solution. Examples of embodiments of the invention are, for example, as follows: CPLEX is a solver for efficiently solving linear programming problems. The parametrization variables, optimization objectives and constraints of the optimization problem can be described by a MATLAB environment. The YALMIP toolbox is used for calling the CPLEX solver. In order to illustrate the effectiveness of the scheme of the invention, part of trains of a part of stations and a part of descending lines in a main line of a special passenger station are used as model parameters, and the day shift planning time is the time (in minutes) for the train to enter the stations and leave the stations originally planned before adjustment. The train is subject to the dispatching station data in the minimum operation time of the station, the minimum running time of the section and the train tracking interval. According to historical data or the situation that trains are set to different delay points (delay point of arrival and delay point of departure) manually, three scenes are considered: scene 1: only including departure late; scene 2: only arrival late; scene 3: meanwhile, the system comprises departure late and arrival late. The comparison improves the epsilon-constraint method and the weighting method. The operation results are shown in fig. 2. It is clear that the non-dominated solution obtained by using the modified epsilon-constraint method is far superior to the weighting method.
By the above embodiments, the problem that a decision maker in the prior art needs to know the preference of different optimization objectives so as to determine the weight in the weighted sum method is solved. However, in practice global preference information for the problem is not accurately available. In addition, the corresponding non-convex front edge cannot be obtained by adopting a weighting method, unnecessary calculation is carried out except for the Pareto front edge, and meanwhile, the fact that whether the obtained solution is a point on all the Pareto front edges or not cannot be guaranteed, so that the technical problem that the solution time is greatly increased when the number of the optimization targets exceeds 2 is solved.
Example two
Fig. 4 is a block diagram illustrating a structure of a dual-target-based high-speed railway train operation adjusting apparatus according to an embodiment of the present invention, as shown in fig. 4, the apparatus includes:
the obtaining module 40 is configured to obtain train operation information of a high-speed railway, where the train operation information includes: operation plan, night information.
And a calculating module 42, configured to calculate an ideal point and a worst point according to the operation information.
And a generating module 44, configured to generate scheme adjustment data according to the ideal point and the worst point.
And an optimizing module 46, configured to adjust the data according to the scheme to obtain a pareto optimal leading edge.
Optionally, the generating module includes: the determining unit is used for determining the value range of the suboptimal target according to the ideal point and the worst point; the setting unit is used for setting a constraint numerical value as the maximum value of the value range of the suboptimal target; and the generating unit is used for generating the scheme adjusting data according to the constraint numerical value.
Optionally, the apparatus further comprises: the updating module is used for updating the constraint numerical value according to the scheme adjusting data; a judging module, configured to judge whether the constraint value is a minimum value of the value range of the suboptimal target, and generate a judgment result, where the judgment result includes: yes, no.
Optionally, the optimizing module includes: the generating unit is used for generating a pareto optimal solution according to the scheme adjusting data when the judging result is yes; and the calculation unit is used for calculating to obtain the pareto optimal leading edge according to the pareto optimal solution.
Specifically, as shown in fig. 1, an embodiment of the present invention provides a method for adjusting the operation of a high-speed railway train based on two targets, which mainly includes: firstly, establishing a double-target train operation plan adjustment model
(1) The train operation plan adjustment model takes into account the following assumptions:
1. the adjustment mode of the operation plan only comprises the adjustment of the arrival and departure time and the sequence of the train, and the train number cancellation is not considered;
2. the emergency event comprises a train interval running late point and a station running late point;
3. the high-speed train is considered to run on the complex line, and the uplink line and the downlink line are not interfered with each other. Only downlink adjustments are considered in the model;
4. stations and sections in the train operation diagram are numbered from top to bottom in sequence, and the total number of the stations is 1 more than the total number of the sections according to the sequence of the stations, the sections, the stations, the sections and the stations;
5. and the limitation of the butt joint departure capability of station tracks is not considered.
(2) The train operation plan adjustment model takes into account the following model parameters and decision variables, see table 1:
TABLE 1 model parameters and decision variable description
(3) The train operation plan adjustment model considers the following objective functions:
1. deviation of train running time
The train running time deviation in the model is mainly obtained by calculating the difference between the adjusted time and the time of the original plan, and is calculated as follows:
2. train operation adjustment cost
The train operation adjustment cost is mainly obtained by calculating the number of times of the train at a later point, and is calculated as follows:
in the formula, sign () is a sign function, and the sign of the corresponding parameter is returned. Since the train running time after adjustment is later than the original planning time, the symbolic function can calculate the adjustment times of the train due to the late point.
(4) The train operation plan adjustment model takes into account the following constraints:
in order to ensure the running safety of the train, the train receiving and dispatching capacity, the station passing capacity and the section passing capacity are reasonably utilized, and the constraint conditions of the model are discussed in detail.
1. Station minimum operating time constraint
The stop time of the train is enough to meet the requirement of passengers for getting on or off the train. Therefore, in the train operation adjustment plan, the stop time of the train must not be less than the minimum stop time of the train at the station.
2. Time division constraint for interval operation
The minimum running time of the interval is mainly related to the states of trains and lines, the speed limit of the interval and other factors. The method comprises start-stop time division and interval minimum operation time division. In addition, for the containing sectionk train to station late i, whichThe update is the sum of the actual interval running time and the arrival late time, namely
3. Minimum tracking interval constraint
In the formula, V-V and A represent respectively "or" and can be calculated by taking the maximum value and the minimum value of two numbers. Formulas (5) and (6) are respectively used for tracking interval constraint when the train starts and arrives, and formula (7) ensures that the sequence of two trains in the interval is unique.
For two or more trains running in the same interval, in order to ensure safety, a certain interval time exists between every two continuous trains. Here, it is assumed that the train operates at a constant speed in the interval, and therefore, it is only necessary to ensure that the train meets the constraint when entering and leaving the station.
4. Late train restraint
Equations (8) and (9) are station operation late point constraints and interval operation late point constraints, respectively.
5. Decision variable constraints
(5) Model processing
Since sign (·) sign function exists in equation (2), it needs to be converted into a linear model for processing. Defining an intermediate variable t1And t2The method comprises the following steps:
by replacing the parameters in the formula (2) with the parameters in the formula (12), a mixed integer linear programming model can be obtained. The model (P0) was established as follows:
solving by adopting an improved epsilon-constraint method and a GUROBI solver:
aiming at the dual-target mixed integer linear programming model, solving is carried out by improving an epsilon-constraint method and combining a GUROBI solver, and train operation plans under different adjustment costs are obtained. In the above two-target train operation adjustment problem, the corresponding constraint single-target optimization model (P1) is:
equations (3) - (11), (14) - (18), the calculation process to improve the epsilon-constraint method includes the following steps:
step 1: and inputting a train operation plan and the night information. The train operation plan comprises the number of trains, the train receiving and dispatching time of each train, the number of stations and intervals in the high-speed railway line, the minimum operation time of the intervals, the minimum operation time of the stations and the minimum tracking interval of adjacent trains. The delay information comprises the departure delay and arrival delay of the train at the station.
Step 2: an ideal point is calculated. Solving the optimization model (P0) without the formula (13) to obtainSolving the optimization model (P0) without the formula (1) to obtain
And step 3: the worst point is calculated. Solving the optimization model (P0) without equation (13), adding additional constraintsTo obtainSolving the optimization model (P0) without equation (1), adding additional constraintsTo obtain
And 4, step 4: and determining a value range of the suboptimal target, and setting the constraint as the maximum value of the suboptimal target. The target function Z can be obtained according to the ideal point and the worst point2In the range ofTherefore, the maximum can be solvedThe individual constrained single object models (P1) result in corresponding Pareto fronts. First orderA corresponding first model is obtained (P1).
And 5: the adjustment scheme is calculated under the set constraints. Solving the model (P1) to obtain the current optimal solution x*Record solution [ Z ]1(x*),Z2(x*)]。
Step 6: the constraint value is updated. Let e2＝Z2(x*)-1。
And 7: judging whether the constraint value is the minimum value of the suboptimal target, if soAnd returning to the step 5, and continuously calculating the optimal solution corresponding to the new constraint model (P1). If it is notStep 8 is continued.
And 8: outputting Pareto leading edge. And (5) merging the solutions obtained by solving the model (P1) under different constraints in the step 5, removing the dominant solution, and finally forming the non-dominant front edge corresponding to the model (P0).
In addition, if the decision maker is interested in only part of Pareto frontier, it can be in the objective function Z2Range of (1)And (4) carrying out internal selection optimization solution on the partial model (P1) to obtain partial interested non-dominated solution. Examples of embodiments of the invention are, for example, as follows: CPLEX is a solver for efficiently solving linear programming problems. The parametrization variables, optimization objectives and constraints of the optimization problem can be described by a MATLAB environment. The YALMIP toolbox is used for calling the CPLEX solver. In order to illustrate the effectiveness of the scheme of the invention, part of trains of a part of stations and a part of descending lines in a main line of a special passenger station are used as model parameters, and the day shift planning time is the time (in minutes) for the train to enter the stations and leave the stations originally planned before adjustment. The train is subject to the dispatching station data in the minimum operation time of the station, the minimum running time of the section and the train tracking interval. According to historical data or the situation that trains are set to different delay points (delay point of arrival and delay point of departure) manually, three scenes are considered: scene 1: only including departure late; scene 2: only arrival late; scene 3: meanwhile, the system comprises departure late and arrival late. The comparison improves the epsilon-constraint method and the weighting method. The operation results are shown in fig. 2. It is clear that the non-dominated solution obtained by using the modified epsilon-constraint method is far superior to the weighting method.
According to another aspect of the embodiment of the invention, a nonvolatile storage medium is further provided, and the nonvolatile storage medium comprises a stored program, wherein the program controls equipment where the nonvolatile storage medium is located to execute a dual-target-based high-speed railway train operation adjustment method during operation.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions are executable to perform a dual-target based adjustment method for high speed railway train operation.
By the above embodiments, the problem that a decision maker in the prior art needs to know the preference of different optimization objectives so as to determine the weight in the weighted sum method is solved. However, in practice global preference information for the problem is not accurately available. In addition, the corresponding non-convex front edge cannot be obtained by adopting a weighting method, unnecessary calculation is carried out except for the Pareto front edge, and meanwhile, the fact that whether the obtained solution is a point on all the Pareto front edges or not cannot be guaranteed, so that the technical problem that the solution time is greatly increased when the number of the optimization targets exceeds 2 is solved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. 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 Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.