CN112396251A - Motor train station dispatching method based on uncertain processing framework and rule combination algorithm - Google Patents
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Abstract
The invention provides a bullet train dispatching method based on an uncertain processing frame and a rule combination algorithm, which comprises the following steps: aiming at the scheduling problem of the motor train unit, extracting a set, a deterministic parameter, an uncertain parameter and a variable which are involved in the scheduling of the motor train unit; establishing a shunting model of a motor car under an uncertain condition; for the shunting model, acquiring a converted shunting model according to the interval variation range of the parameters varying in the interval, the probability distribution of the parameters with random attributes and the expectation of the parameters with fuzzy characteristics; solving the converted shunting model to obtain an optimal scheduling scheme; and dispatching the vehicles of the motor train unit according to the optimal dispatching scheme. The shunting model which contains uncertain factors and is difficult to solve is converted into the deterministic robust equivalent model which is easy to solve, and when the motor train has uncertain factors, the precise dispatching of the motor train can be realized.
Description
Technical Field
The invention relates to the technical field of vehicle scheduling and computers, in particular to a bullet train scheduling method based on an uncertain processing framework and a rule combination algorithm.
Background
In order to ensure safe and stable operation of the motor train unit, necessary maintenance is required after the motor train unit returns to a motor train station. The most common first-level maintenance includes cleaning, maintenance, line changing, parking, etc. Some scholars have conducted relevant research aiming at the scheduling problem of the shunting operation of the motor train station. In one prior art, a minimum total delay time of a motor train unit is taken as a target, a shunting operation plan optimization model of the motor train unit is established, and a micro-evolution and operation line distribution algorithm is designed to solve.
In the other prior art, the minimum shunting total hook number is taken as a target, an integer programming model of a shunting operation plan of a motor train station is constructed, and a particle swarm optimization algorithm is designed for solving. In the prior art, the minimum time for completing the maintenance of the last train of motor train unit is taken as a target, a maintenance operation plan optimization model of the first-stage motor train unit is established, and a self-adaptive genetic algorithm is adopted for solving. In the prior art, the aim of reducing invalid occupation time of a maintenance area and shunting path cost is to establish a scheduling optimization model of shunting operation of a motor train station and solve the problem by adopting an improved maximum and minimum ant colony system.
In the shunting operation process of the motor train unit, various uncertain factors can interfere with the motor train unit, such as delayed arrival of the motor train unit, failure of a maintenance machine, fluctuation of maintenance duration and the like, so that certain deviation exists between the actual execution condition and the operation plan, and even the operation plan of the motor train unit can be influenced in severe cases.
In the prior art, a dispatching operation planning model of a motor train station is established or an intelligent optimization algorithm is designed to solve the dispatching problem, but uncertain factors are rarely considered to generate interference on the dispatching process of the motor train unit, and a robust dispatching scheme capable of absorbing uncertain disturbance is also lacked.
Therefore, a new method for dispatching the motor train based on the uncertain processing framework and the rule combination algorithm is needed.
Disclosure of Invention
The invention provides a motor train unit dispatching method based on an uncertain processing framework and a rule combination algorithm, which is used for solving the problem that uncertain factors can not interfere with the motor train unit dispatching process in the prior art and realizing accurate dispatching of the motor train unit.
The invention provides a bullet train dispatching method based on an uncertain processing frame and a rule combination algorithm, which comprises the following steps:
aiming at the scheduling problem of the motor train unit, extracting a set, a deterministic parameter, an uncertain parameter and a variable which are involved in the scheduling of the motor train unit, wherein the uncertain parameter comprises a parameter which changes in an interval, a parameter with random attributes and a parameter with fuzzy characteristics;
establishing a shunting model of the motor train station under the uncertain condition based on the set, the uncertain parameters and the variables;
for the shunting model, acquiring a determined interval planning peer-to-peer model according to an interval variation range of parameters varying in an interval, acquiring a determined chance constraint peer-to-peer model according to probability distribution of parameters with random attributes, acquiring a determined expectation constraint peer-to-peer model according to expectation of parameters with fuzzy characteristics, and finally acquiring a converted shunting model;
solving the converted shunting model to obtain an optimal scheduling scheme;
and dispatching the vehicles of the motor train unit according to the optimal dispatching scheme.
The invention also provides a bullet train dispatching system based on the uncertain processing framework and the rule combination algorithm, which comprises the following steps:
the parameter extraction module is used for extracting a set, a deterministic parameter, an uncertain parameter and a variable which are related to the dispatching of the motor train unit aiming at the dispatching problem of the motor train unit, wherein the uncertain parameter comprises a parameter which changes in an interval, a parameter with random attributes and a parameter with fuzzy characteristics;
the shunting model module is used for establishing a shunting model of the motor train station under the uncertain condition based on the set, the deterministic parameter, the uncertain parameter and the variable;
the model conversion module is used for acquiring a determined interval planning peer-to-peer model according to an interval variation range of parameters varying in an interval for the shunting model, acquiring a determined chance constraint peer-to-peer model according to probability distribution of the parameters with random attributes, acquiring a determined expectation constraint peer-to-peer model according to expectation of the parameters with fuzzy characteristics, and finally acquiring a converted shunting model;
the dispatching solving module is used for solving the converted shunting model to obtain an optimal dispatching scheme;
and the vehicle scheduling module is used for scheduling the vehicles of the motor train unit according to the optimal scheduling scheme.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the method for dispatching the motor train based on the uncertain processing framework and the rule combination algorithm.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for dispatching a railcar based on the uncertainty processing framework and the rule-combination algorithm as described in any one of the above.
According to the motor train dispatching method based on the uncertain processing framework and the rule combination algorithm, in the aspect of processing uncertainty, by introducing uncertain parameters, fluctuation degrees of the uncertain parameters and violation degrees of resource constraints are respectively described, a shunting model which contains uncertain factors and is difficult to solve is converted into a deterministic robust equivalent model which is easy to solve, a robust optimization method capable of processing various uncertain factors is provided, and when the motor train has the uncertain factors, accurate dispatching of the motor train can be achieved.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for dispatching a bullet train based on an uncertain processing framework and a rule combination algorithm, provided by the invention;
FIG. 2 is a schematic view of a first-level maintenance process of the motor train unit;
FIG. 3 is a Gantt chart of a determined case II13 motor train shunting station in accordance with the present invention;
FIG. 4 is a Gantt chart of the shunting of II13 motor cars when the time of the entering and exiting is uncertain;
FIG. 5 is a Gantt chart of II13 shunting of a motor car when the operation time is uncertain in the present invention;
FIG. 6 is a schematic structural diagram of a motor train dispatching system based on an uncertainty processing framework and a rule combination algorithm, provided by the invention;
fig. 7 is a schematic physical structure diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, 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.
Due to differences of information acquisition forms, frequencies and the like of the motor train, characteristics acquired by various uncertain factors are different, some factors only know the change interval of the factors, and some factors can clarify the random distribution form or fuzzy characteristics obeyed by the factors. The change rule of the uncertain factors is researched, an optimization method for absorbing the uncertain disturbances is sought, a robust shunting scheme with immunity to the uncertain factors is obtained, and the method has important theoretical value and application prospect.
The embodiment of the invention provides a robust shunting planning model of a motor train station under the uncertain condition aiming at various uncertain factors in the internal and external environments of the motor train station, a rule combination algorithm for solving the model is designed, and finally, the model and the algorithm effectiveness are verified by using different cases.
The embodiment of the invention provides a bullet train dispatching method based on an uncertain processing framework and a rule combination algorithm, as shown in figure 1, the method comprises the following steps:
firstly, a robust shunting model of a motor train station under an uncertain condition needs to be established, and before the robust shunting model is established, the scheduling problem of shunting operation of the motor train station needs to be described, wherein the scheduling problem of the shunting operation is specifically as follows:
as shown in fig. 2, after the motor train unit enters, the motor train unit passes through the parking area 1, the operation area and the parking area 2 in sequence to prepare a station. Cleaning and maintenance are completed in the operation area, the duration of each operation needs to meet the given operation time, and the sequence between the two operations is not fixed and can be performed when the two operations are mature.
The parking area 1 is used for parking the motor train unit which is just entered, and when the operation area has available tracks, the motor train unit can directly enter the operation area without staying in the parking area 1;
and the parking area 2 parks the motor train unit which finishes the maintenance task and waits for the motor train unit to be discharged.
The shunting path of the motor train unit in the motor train station comprises a transfer line from the parking area 1 to the operating area, a transfer line from the inside of the operating area and a transfer line from the operating area to the parking area 2, and is carried out according to standard transfer time. And if the station track of the next operation area is completely occupied during line switching, the motor train unit must stop at the current station track to wait.
Therefore, the shunting problem of the motor train can be described as an Open-Shop scheduling problem with indefinite processing time and indefinite processing sequence.
S1, extracting a set, a deterministic parameter, an uncertain parameter and a variable which are related to the dispatching of the motor train unit aiming at the dispatching problem of the motor train unit, wherein the uncertain parameter comprises a parameter which changes in an interval, a parameter with random attributes and a parameter with fuzzy characteristics;
specifically, aiming at the vehicle scheduling problem of the motor train unit, the scheduling problem refers to the previously determined Open-Shop scheduling problem, and sets, determined parameters, uncertain parameters and variables related to the motor train unit in the scheduling process are extracted, wherein the sets comprise a motor train unit set, an operating area set, an operating line set and a transfer line and station track set; the determined parameters comprise standard line switching time, nth operation executed on each station track and the ith operation executed by each motor train unit; the uncertain parameters comprise uncertain operation time of each operation area, uncertain entering time of the motor train unit e and uncertain entering time of the motor train unit e.
The uncertain operation time of each operation area belongs to parameters which change in an interval or have random attributes or fuzzy characteristics, and the uncertain entering time and the uncertain leaving time of the motor train unit belong to random parameters obeying Poisson distribution.
The variables comprise the starting time of the motor train unit e occupying the working area z, the starting time of the nth operation of the station track d, the execution of the first operation of the motor train unit e in the working area z, the ending time of the motor train unit e occupying the working area z, the ending time of the nth operation of the station track d and the station track of the motor train unit e in the working area zdThe nth operation performed.
S2, establishing a shunting model of the motor train station under the uncertain condition based on the set, the deterministic parameters, the uncertain parameters and the variables;
then, based on the sets, the deterministic parameters, the uncertain parameters and the variables extracted above, table 1 is a schematic table describing and characterizing various sets, parameters and related variables in the embodiment of the invention, and as shown in table 1, a shunting model of the motor train station under the uncertain conditions is established. By extracting the uncertain parameters and establishing the shunting model, all the parameters in the shunting model of the motor train unit can be quantized.
TABLE 1
On the basis of the extracted parameters, a robust shunting model of the motor train under the uncertain condition is established, and the specific formula is as follows:
wherein, the formula (1) represents that the objective function maximizes the total reserved time.
And the equations (2-6) are the resource allocation constraint of the stock path.
The formula (2-3) shows that each motor train unit needs to be cleaned and overhauled, and each operation needs to be executed only once; and there is no fixed sequence between the two operations.
The formula (4) shows that each motor train unit needs to go through four operation areas including a parking area 1, a cleaning area, a maintenance area and a parking area 2; in each operation area, each motor train unit can only occupy one of the station tracks.
Equation (5) represents that the motor train unit e is assigned to the nth operation of the station track d in the operation area z, and can be assigned only once at most.
Equation (6) indicates that on any given track d, a subsequent job can be allocated after a previous job is allocated.
Equation (7-12) is the maintenance operation timing constraint.
Equation (7-10) indicates that if the motor train unit e is assigned to the nth operation of the track d of the operation zone z, the start-stop time of the operation is equal to the start-stop time of the motor train unit e on the operation zone z, where M is a sufficiently large number.
The expression (11) shows that if the motor train unit e 'enters the station track d of the operation zone z after the motor train unit e, the starting time of the motor train unit e' in the operation zone is not less than the ending time of the motor train unit e in the operation zone.
The formula (12) shows that the stay time of the motor train unit e in the working area z is not less than the given working time of the working area.
The equation (13-14) is the time length constraint of the line switching operation. Namely, the end time of the motor train unit in the previous operation area plus the standard operation time of passing through the diversion station track is equal to the start time of the next operation area.
Equations (15-16) are motor vehicle operation plan constraints.
The formula (7) shows that the motor train unit can be maintained only after entering the station,
the equation (8) shows that the motor train unit must be repaired before the departure time specified by the operation plan.
S3, for the shunting model, obtaining a determined interval planning peer-to-peer model according to the interval variation range of the parameter varying in the interval, obtaining a determined chance constraint peer-to-peer model according to the probability distribution of the parameter with random attribute, obtaining a determined expectation constraint peer-to-peer model according to the expectation of the parameter with fuzzy characteristic, and finally obtaining a converted shunting model;
because the established shunting model contains various uncertain parameters and adopts different uncertain expression forms, the MIP model can not be effectively solved by utilizing the existing deterministic optimization method.
Therefore, a robust optimization framework oriented to various uncertain factors needs to be researched, and uncertain problems which are difficult to solve are converted into a deterministic robust peer-to-peer model which is easy to solve by using the uncertain processing framework.
Therefore, in the embodiment of the invention, the interval planning peer-to-peer model is determined according to the interval variation range of the variable parameters in the interval, the opportunity constraint peer-to-peer model is determined according to the probability distribution of the parameters with random attributes, and the expectation constraint peer-to-peer model is determined according to the expectation of the parameters with fuzzy characteristics. The uncertain constraints in the shunting model are converted into the definite constraints in the three aspects, so that the converted shunting model can be conveniently solved subsequently.
The method comprises the following steps: interval-varying uncertain parameters and its Interval planning Peer-to-Peer model (IPCM)
The general interval linear programming problem can be described as:
wherein,
are all the number of the intervals,
for uncertain constraints, it is common to measure how well they are satisfied by an order relationship between them. For any solution X, the degree of likelihood is called
Is the satisfaction level of X to the constraint j. From this definition, one can derive:
under the condition that the constraint satisfies the level omega,
can be converted into deterministic constraints:
assuming uncertain parameters
Within the interval
And (4) changing.
From equation (17), equation (12) can be converted to a deterministic constraint of equation (18):
the interval planning peer-to-peer model (IPCM) of the original uncertain MIP can be obtained by integrating the models (1-11), (13-16) and (18) so as to solve the problem that the model contains mathematical planning of uncertain parameters changing in an interval.
Step two: uncertain parameters with stochastic properties and their chance constrained peer-to-peer model (CCCM)
The formula (12) is abbreviated as a general expression shown in the formula (19). Where x is a continuous variable, y is a 0-1 variable, A, B are coefficient matrices, and P is a column vector, where uncertainty in parameters generally means that there may be uncertainty in A, B, and P.
Ax+By≤P， (19)
Considering that the uncertain variables fluctuate around the theoretical value, the uncertain variables can be represented by the theoretical value part and the random fluctuation part:
wherein,
actual values of uncertain parameters A, B and P are respectively; a, B and P are respectively theoretical values of A, B and P; xia,ξb,ξpRespectively representing the independent variation degrees of uncertain variables A, B and P, and obeying probability distribution; ε (ε > 0) controls the amplitude of the fluctuation of all uncertain parameters, called the uncertainty level.
To achieve a trade-off between scheduling schemes and the extent of permissible violations of constraints, the extent of permissible violations of constraints is described by an infeasible limit κ.
Assume random variable xia,ξb,ξpAll obey a certain probability distribution with function of F (xi), the corresponding inverse function is F-1(ξ). The expression of the quantile lambda with respect to kappa is
From the equation (20), the actual processing time
Can describeComprises the following steps:
substituting equation (21) can transform equation (12) to equation (22) to determine the type constraint:
the comprehensive formulas (1-11), (13-16) and (22) can obtain an opportunity constrained peer-to-peer model (CCCM) of the original uncertain MIP so as to solve the problem of mathematical programming of uncertain parameters containing random attributes in the model.
Step three: uncertain parameters with fuzzy features and their expectation constraint peer-to-peer model (ECCM)
The triangular fuzzy number of the fuzzy soft set can be defined as a vector
Wherein x is equal to R, 0 < a1≤a2≤a3. Expectation is a numerical feature that describes the size of the fuzzy number, and is a way to sort fuzzy numbers [9 ]]. Defining a blur coefficient mu, then triangulating the blur number
The expected values of (c) may be described as:
if p isz1、pz2、pz3Respectively, the shortest operation time, the most possible operation time and the longest operation time of the operation area z of the motor train station, the operation time of the operation area z can be described as a triangular fuzzy number
The expected values are:
expectation reduction is a common and effective uncertainty reduction method, i.e., replacing fuzzy variables with expectation values. Then, equation (12) can be converted to the deterministic constraint of equation (25):
and (4) synthesizing (1-11), (13-16) and (25) to obtain an expected constraint peer-to-peer model (ECCM) of the original uncertain MIP so as to solve the problem of mathematical programming of uncertain parameters containing fuzzy features in the model.
S4, solving the converted shunting model to obtain an optimal scheduling scheme;
and then solving the converted shunting model to obtain an optimal scheduling scheme.
The robust peer-to-peer model is a combined optimization problem with space-time characteristics and complex constraint conditions. As the number of station resources and motor train units increases, "combined explosions" will occur. And (3) in consideration of the usability of the shunting plan, designing a Rule Combination Algorithm (RCA) to solve.
Rule one is as follows: first come first process initialization rules
And determining the arrival time of the motor train unit according to the motor train unit operation plan, and initializing the operation sequence of the motor train unit according to a first-come first-process principle.
Rule two: earliest free track priority rule
Traversing operation zone z track DZAnd finding out the earliest idle track to allocate to the task to be operated in the operation area z under the current occupation condition. The method comprises the following specific steps: suppose Ez,dIndicating the earliest time of availability of track d in work zone z, E when track d has not been usedz,dWhen equal to 0, initialize Ez,dAnd (4) collecting. When there is a task to be worked on, the earliest free track in the working area z is selected
For the optimal track
Performing distribution, updating Ez,d. When d is*And when the number is more than or equal to 1, randomly selecting one to distribute. And circulating the step until all the tasks to be worked in the working area are distributed.
Rule three: track space-time compatibility constraint rule
Suppose that the operation area of the i-th operation and the i + 1-th operation of the motor train unit is ziAnd zi+1The operation tracks to be allocated are respectively
And
for determining current working track
Occupying time, assuming that the motor train unit e occupies the station track
Respectively at the start and stop moments of
Wherein
And as the work area ziWhen the vehicle is stored in the parking area 1,
then, it is judged
And the thigh road
Earliest moment of availability
The relationship of (1): (1) when in use
When the constraint of space-time compatibility of the track is satisfied, the method has
(2) Similarly, when
When the constraint of space-time compatibility of the track is satisfied, the method has
In summary, the operation station tracks of the motor train unit in the first three stages
Cut-off time
The start time of the next stage operation area is
Rule four: conflict resolution rule of shunting plan
Before determining the occupation time of the motor train unit in the operation station track 2 of the parking area, order
If Se,4≤F′e,4Then, then
Last stage occupying track
Respectively is Se,4、Fe,4(ii) a If Se,4＞F′e,4When the time when the motor train unit e enters the parking area 2 exceeds the time specified by the operation plan, the shunting operation conflicts with the operation plan,shunting operation is not feasible. Starting to backtrack and search the distributed motor train units, and finding the motor train unit occupying the parking area 2 with the longest track time
And pi (e-1) represents the assigned motor train unit operation sequence of the previous e-1 motor train unit. Exchange e, e*And after the operation sequence is finished, generating a new operation sequence pi' (e) of all the motor train units on the basis of pi (e), and continuously distributing the motor train units according to the rule until all shunting conflicts are resolved.
And S5, scheduling the vehicles of the motor train unit according to the optimal scheduling scheme.
And dispatching the vehicles of the motor train unit according to the optimal dispatching scheme.
According to the motor train dispatching method based on the uncertain processing framework and the rule combination algorithm, in the aspect of processing uncertainty, by introducing uncertain parameters, fluctuation degrees of the uncertain parameters and violation degrees of resource constraints are respectively described, a shunting model which contains uncertain factors and is difficult to solve is converted into a deterministic robust equivalent model which is easy to solve, a robust optimization method capable of processing the uncertain factors is provided, and when the motor train has the uncertain factors, accurate dispatching of the motor train can be achieved.
The following description is provided to illustrate embodiments of the present invention in connection with different types of cases, and not to limit the scope of the present invention.
The method comprises the following steps: case parameter and evaluation index design
Table 2 shows a schedule for practical operation of a motor vehicle according to an embodiment of the present invention, and the maintenance parameter is p as shown in Table 2ZThe operation time (min) for the parking area 1, the cleaning area, the maintenance area, and the parking area 2 is represented by {0,30,180,0}, respectively. The standard line-changing time is tau r5 min. Assuming that the track resources are variable, the number of tracks in different areas is set, taking the track state I as an example, I:4-2-2-6 indicates that the number of the storage lines in the parking area 1 is 4, the number of the cleaning lines is 2, the number of the maintenance lines is 2, and the number of the storage lines in the parking area 2 is 6. And setting the stock path resource state II to be 4-2-3-6 in the same way. In the following experimentsIn the middle, different experimental examples are obtained by respectively changing the number of the motor train units and the state of the station track resources.
TABLE 2
Designing and solving quality evaluation indexes: the total overhaul time of the station track is
Total occupied time of operation area is
Effective utilization rate of cleaning line is eff1＝f1/f2(z is 2), the effective utilization rate of the maintenance line is eff2＝f1/f2(z-3). The ith better solution of 10 runs of the algorithm is fiThe optimal solution is fbestThe average value of the solution is
The preferred solution interval is
Average solution time of
The number of solved times is num, and the average effective utilization rate of the cleaning line and the maintenance line is
The relative error index is
The standard deviation index is
Step two: experimental results for different types of cases
In order to verify the effectiveness of a shunting model of a motor train station under an uncertain condition, a robust optimization method and a solving algorithm, the solving performance of the proposed algorithm is verified based on three cases of a deterministic type, an uncertain condition of time of entering and leaving stations and an uncertain condition of operation time;
the experimental configuration is that Intel (R) core (TM) i7-8565U CPU @1.80GHz, the internal memory is 8.00GB, the proposed RCA algorithm is compiled by Matlab R2016a, and a precise algorithm Solver (MIP-Solver) is used for solving GAMS 24.8/Cplex.
Case 1: definitive case
Defining case1 as the problem type corresponding to the time when the motor train unit enters and exits and the operation time are both determined values. The case1 was solved by the RCA algorithm, and Table 3 shows the result table of the case1 solved by the RCA algorithm, as shown in Table 3 and FIG. 3.
TABLE 3
From table 3, it can be seen that:
(1) the maximum maintenance capacity of the motor car is given under the condition of the station track resource state, and the maintenance capacity is increased when the station track resource is increased, which shows that the algorithm can predict the maximum maintenance capacity of the motor car under different station track resource states.
(2) The total reserved time of the motor train unit is an interval value, which indicates that the algorithm is a better solution obtained based on a combination rule; the running time of the algorithm is about 0.1 second, and when the problem scale is increased, the solving time of the algorithm is not obviously increased, which shows that the algorithm can rapidly obtain better solutions of various scales and even larger-scale cases.
(3) The utilization rate of a motor train station maintenance line is close to 100%, the full-load operation of a maintenance operation area is a key area of the whole maintenance operation, and the motor train needs to enhance the detection and maintenance of the maintenance operation line and ensure that the maintenance plan of the motor train station is not interrupted.
(4) The relative error and the standard deviation do not exceed 1 percent, which shows that the accuracy and precision of the solution of the algorithm are very high, and when the scale of the problem is increased, the error and the deviation of a better solution are even smaller.
Case 2: case of uncertain time of entering and leaving
Defining case2 as the time of motor train unit passing in and out obeys Poisson distribution, and the operation time is the corresponding problem type when determining the value. The station track resource states I and II are selected, the number of the motor train units is 7-13, cases 2 are solved by using an RCA algorithm, and a table 4 is a list result table of cases 2 solved by the RCA algorithm, as shown in the table 4 and the graph 4.
TABLE 4
When the time of entry and exit is uncertain compared to the deterministic case 1: (1) the maximum maintenance capacity of the motor car is not affected; (2) the lower bound of the total reserved time of the motor train unit is smaller, the upper bound is larger, and the fluctuation range of the system target is increased; (3) the average effective utilization rate of the operating line is slightly reduced, which shows that the algorithm can solve with less efficiency loss; (4) the running time of the algorithm is slightly increased, and the times of obtaining a better solution are slightly reduced, which shows that the algorithm can be solved with smaller time cost and solving times; (5) the average relative error REI and the average standard deviation RSD are slightly increased and are not more than 5%, and the algorithm can solve with small accuracy and precision loss.
Therefore, when the time of the access is uncertain, the algorithm can obtain a robust optimal solution capable of absorbing the disturbance of the time of the access in a short time with small target deviation and efficiency loss so as to realize the compromise between the time fluctuation of the access and the shunting scheme.
Case 3: case of uncertain operation time
When maintenance work is performedWhen not determined, (1) when a certain interval fluctuates
(2) When having random attribute
(3) When having the fuzzy characteristic
When the time of the motor train unit entering and exiting is a determined value, case3 is defined as a corresponding problem type when the operation time changes within an interval; defining case4 as the corresponding question type when the job time has random attribute; case5 is defined as the type of problem that corresponds to when the job time has a fuzzy characteristic. And (3) selecting station track resource states I and II, wherein the number of the motor train units is 7-13, and solving case3-5 by using the provided RCA algorithm. After the program is run for 10 times, the average effective utilization rates of the better solution interval, the cleaning line and the maintenance line are obtained, and table 5 is a utilization rate table of the better solution interval and the working line, and the results are shown in table 5 and fig. 5.
TABLE 5
When the time of operation is uncertain, compared to the deterministic case 1: (1) the maximum maintenance capacity of the motor car is not affected; (2) the lower bound of the total reserved time of the motor train unit is smaller, the upper bound is larger, and the case3-5 has volatility compared with the optimal solution; (3) the average effective utilization rate of the operation line is slightly reduced, and the case4 is more than 3 and more than 5 on the utilization rate index.
The program runs for 10 times to obtain the algorithm running time, the times of solution, the relative error and the relative standard deviation, table 6 is an indication table of the quality index of the algorithm solution in the embodiment of the present invention, and the result is shown in table 6.
TABLE 6
When the time of operation is uncertain, compared to the deterministic case 1: (1) the algorithm solving time is slightly increased, the times of obtaining better solutions are slightly reduced, and case5 is greater than 4 and greater than 3 in the index of the solving times, which shows that the algorithm can solve with smaller time cost and solving times. (2) The average relative error REI and the average standard deviation RSD are slightly increased and are not more than 4%, and case4 is larger than 3 and is larger than 5 on the deviation index, which shows that the algorithm can solve with smaller accuracy and precision loss.
Therefore, when the working time is uncertain, the algorithm can obtain a robust optimal solution capable of absorbing the working time disturbance in a short time with small target deviation and efficiency loss so as to realize the compromise between the working time fluctuation and the shunting scheme.
The invention provides a robust shunting method for a bullet train station based on an uncertain processing framework and a rule combination algorithm, which is an optimization method which is easy to use for the bullet train station, can rapidly process uncertain data and can efficiently obtain a robust scheduling scheme, and has the key point of solving the rule combination algorithm of a deterministic robust peer-to-peer problem. The method flow design, the uncertain solving mechanism and the conflict resolution rule are important protection points of the embodiment of the invention. The algorithm principle and the specific implementation steps are as follows:
the method comprises the following steps: initializing a motor train station level overhaul resource set (E, Z, D)ZR) and parameters
Setting a track use time window time (d, n) and an earliest available time Ez,d0; and determining the bullet train group distribution sequence pi (e) according to the rule one.
Step two: sequentially selecting a motor train unit e according to pi (e) to carry out operation distribution; determining the earliest idle track d of the overhaul area and the cleaning area according to the rule II*(ii) a By d*Determining the operation zone Z of the second stage2To determine the whole operation sequence Z of the motor train unit (Z)1、z2、z3、z4}。
Step three: the first three phases i ═ 1,2,3 are assigned according to ZThe business track and the track occupation time. The motor train unit is enabled to operate at the initial moment of the first stage
Sequentially determining the current operation track according to the rules two and three
And its occupancy cutoff time
And at the beginning of the second stage of track occupation
Occupying time interval of track
Time interval converted into track time window time (d, n)
And update
Shift to the second stage operation station track
This step is cycled through until the third stage is completed.
Step four: before the fourth stage is assigned, S is judgede,4And F'e,4The relationship (2) of (c). Order to
If S ise,4＜F'e,4Determining the last stage track according to the third rule
Occupancy start-stop time (S)e,4,Fe,4) (ii) a Otherwise, finding the motor train unit e according to the rule four*And regenerating the distribution sequence pi' (e) of the motor train unit. Returning to the step 1 to continue the distribution,until a feasible shunting operation plan is generated. Will be provided with
Time interval converted into track time window time (d, n)
And update
Step five: and e +1, returning to the step one until all the motor train units are distributed, and finishing the dispatching operation planning.
In conclusion, various uncertain factors exist in an actual maintenance site. For example, the time of getting in and out of a motor train unit is usually variable, the time of operating equipment by workers is usually varied in a certain interval, the time of maintaining the machine is usually random uncertain, and the time of repairing common faults is usually fuzzy uncertain. In the prior art, a dispatching operation planning model of a motor train station is established or an intelligent optimization algorithm is designed to solve the dispatching problem, but uncertain factors are hardly considered to generate interference on the dispatching process of the motor train station, and a robust dispatching operation planning method of the motor train station capable of absorbing uncertain disturbance is also lacked. Compared with the prior art, the application proposal has the remarkable technical advantages that:
in the aspect of uncertain representation, parameters such as the incoming time, the outgoing time, the cleaning operation time, the maintenance operation time and the like of the motor train unit can present different change rules, the change rules of the uncertain factors are researched and are merged into an uncertain MIP mathematical model in a mathematical representation mode, and a robust shunting model of the motor train unit under the uncertain condition is established.
In the aspect of processing uncertainty, by introducing uncertainty parameters, respectively depicting the fluctuation degree of uncertainty variables and the violation degree of resource constraints, converting a shunting model which contains uncertainty factors and is difficult to solve into a deterministic robust peer-to-peer model which is easy to solve, and providing a robust optimization method capable of determining the uncertainty factors.
In the aspect of solving uncertainty, the uncertainty parameters are fused into a solving mechanism of a rule combination algorithm, and a robust shunting scheme with immunity to the uncertainty factors is quickly obtained by distributing a proper operation station track to each operation of the motor train unit and determining the execution sequence of each operation of the motor train unit.
Therefore, the embodiment of the invention provides a method for rapidly processing uncertain factors for a motor train, can rapidly and efficiently obtain a robust shunting scheme with immunity to uncertainty, and is a robust dispatching method for the motor train based on an uncertain processing frame and a rule combination algorithm. The following description of the embodiments of the present application will be made in conjunction with various types of cases, without limiting the scope of the present application.
As shown in fig. 6, the system for dispatching a bullet train based on an uncertain processing framework and a rule combination algorithm according to an embodiment of the present invention includes a parameter extraction module 1201, a shunting model module 1202, a model conversion module 1203, a dispatching solution module 1204, and a vehicle dispatching module 1205, where:
the parameter extraction module 1201 is used for extracting a set, a deterministic parameter, an uncertain parameter and a variable related to the dispatching of the bullet train according to the dispatching problem of the bullet train, wherein the uncertain parameter comprises a parameter which changes in an interval, a parameter with random attributes and a parameter with fuzzy characteristics;
the shunting model module 1202 is configured to establish a shunting model of the motor train station under an uncertain condition based on the set, the deterministic parameter, the uncertain parameter, and the variable;
the model conversion module 1203 is configured to, for the shunting model, obtain a determined interval planning peer-to-peer model according to an interval variation range of a parameter varying within an interval, obtain a determined chance constraint peer-to-peer model according to probability distribution of a parameter having a random attribute, obtain a determined expectation constraint peer-to-peer model according to an expectation of a parameter having a fuzzy feature, and finally obtain a converted shunting model;
the dispatching solving module 1204 is configured to solve the converted shunting model to obtain an optimal dispatching scheme;
the vehicle scheduling module 1205 is configured to schedule the vehicle of the bullet train according to the optimal scheduling scheme.
The present embodiment is a system embodiment corresponding to the above method, and please refer to the above method embodiment for details, which is not described herein again.
As shown in fig. 7, an electronic device provided in an embodiment of the present invention may include: a processor (processor)1310, a communication Interface (Communications Interface)1320, a memory (memory)1330 and a communication bus 1340, wherein the processor 1310, the communication Interface 1320 and the memory 1330 communicate with each other via the communication bus 1340. The processor 1310 may invoke logic instructions in the memory 1330 to perform a method for scheduling trains based on the indeterminate processing framework and the rule combination algorithm, the method comprising:
aiming at the scheduling problem of the motor train unit, extracting a set, a deterministic parameter, an uncertain parameter and a variable which are involved in the scheduling of the motor train unit, wherein the uncertain parameter comprises a parameter which changes in an interval, a parameter with random attributes and a parameter with fuzzy characteristics;
establishing a shunting model of the motor train station under the uncertain condition based on the set, the uncertain parameters and the variables;
for the shunting model, acquiring a determined interval planning peer-to-peer model according to an interval variation range of parameters varying in an interval, acquiring a determined chance constraint peer-to-peer model according to probability distribution of parameters with random attributes, acquiring a determined expectation constraint peer-to-peer model according to expectation of parameters with fuzzy characteristics, and finally acquiring a converted shunting model;
solving the converted shunting model to obtain an optimal scheduling scheme;
and dispatching the vehicles of the motor train unit according to the optimal dispatching scheme.
In addition, the logic instructions in the memory 1330 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. 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.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, which when executed by a computer, enable the computer to perform the method for dispatching trains based on the uncertain processing framework and the rule combination algorithm provided by the above methods, the method comprising:
aiming at the scheduling problem of the motor train unit, extracting a set, a deterministic parameter, an uncertain parameter and a variable which are involved in the scheduling of the motor train unit, wherein the uncertain parameter comprises a parameter which changes in an interval, a parameter with random attributes and a parameter with fuzzy characteristics;
establishing a shunting model of the motor train station under the uncertain condition based on the set, the uncertain parameters and the variables;
for the shunting model, acquiring a determined interval planning peer-to-peer model according to an interval variation range of parameters varying in an interval, acquiring a determined chance constraint peer-to-peer model according to probability distribution of parameters with random attributes, acquiring a determined expectation constraint peer-to-peer model according to expectation of parameters with fuzzy characteristics, and finally acquiring a converted shunting model;
solving the converted shunting model to obtain an optimal scheduling scheme;
and dispatching the vehicles of the motor train unit according to the optimal dispatching scheme.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the method for dispatching trains based on the uncertain processing framework and the rule combination algorithm provided above, the method comprising:
aiming at the scheduling problem of the motor train unit, extracting a set, a deterministic parameter, an uncertain parameter and a variable which are involved in the scheduling of the motor train unit, wherein the uncertain parameter comprises a parameter which changes in an interval, a parameter with random attributes and a parameter with fuzzy characteristics;
establishing a shunting model of the motor train station under the uncertain condition based on the set, the uncertain parameters and the variables;
for the shunting model, acquiring a determined interval planning peer-to-peer model according to an interval variation range of parameters varying in an interval, acquiring a determined chance constraint peer-to-peer model according to probability distribution of parameters with random attributes, acquiring a determined expectation constraint peer-to-peer model according to expectation of parameters with fuzzy characteristics, and finally acquiring a converted shunting model;
solving the converted shunting model to obtain an optimal scheduling scheme;
and dispatching the vehicles of the motor train unit according to the optimal dispatching scheme.
The above-described embodiments of the apparatus are merely illustrative, and 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 network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A motor train dispatching method based on an uncertain processing framework and a rule combination algorithm is characterized by comprising the following steps:
aiming at the scheduling problem of the motor train unit, extracting a set, a deterministic parameter, an uncertain parameter and a variable which are involved in the scheduling of the motor train unit, wherein the uncertain parameter comprises a parameter which changes in an interval, a parameter with random attributes and a parameter with fuzzy characteristics;
establishing a shunting model of the motor train station under the uncertain condition based on the set, the uncertain parameters and the variables;
for the shunting model, acquiring a determined interval planning peer-to-peer model according to an interval variation range of parameters varying in an interval, acquiring a determined chance constraint peer-to-peer model according to probability distribution of parameters with random attributes, acquiring a determined expectation constraint peer-to-peer model according to expectation of parameters with fuzzy characteristics, and finally acquiring a converted shunting model;
solving the converted shunting model to obtain an optimal scheduling scheme;
and dispatching the vehicles of the motor train unit according to the optimal dispatching scheme.
2. The method for dispatching the bullet trains based on the uncertain processing framework and the rule combination algorithm according to claim 1, wherein the determined interval planning peer-to-peer model is obtained according to the interval variation range of the parameters varying in the interval, and the specific calculation formula is as follows:
wherein, BezShows the starting time T of the motor train unit e occupying the operating area zezRepresents the ending time of the motor train unit e occupying the operation area z, omega represents the constraint level,
the upper limit of the range of variation of the interval is shown,
represents the lower limit of the interval variation range, D ' represents a stock line, D represents a line set, N ' represents the N ' th operation executed on each stock line, Nd′Indicates the maximum number of operations, Y, of the track dezdnThe nth operation executed by the motor train unit e on the station track d of the operation area z is shown.
3. The method for dispatching a motor train unit based on the uncertain processing framework and the rule combination algorithm according to claim 1, wherein the determined chance constraint peer-to-peer model is obtained according to the probability distribution of the parameters with random attributes, and the specific calculation formula is as follows:
wherein, BezShows the starting time T of the motor train unit e occupying the operating area zezShowing the ending time of the motor train unit e occupying the operating area z, wherein epsilon represents the uncertain level and xi1And xi2Denotes a random variable,. kappa.denotes an infeasible limit,. pzRepresenting the theoretical working time of a working area z, D ' representing a stock line, D representing a set of working lines, N ' representing the nth ' job executed on each stock line, Nd′Indicates the maximum number of operations, Y, of the track dezdnThe nth operation executed by the motor train unit e on the station track d of the operation area z is shown.
4. The method for dispatching trains based on uncertain processing framework and rule-based combination algorithm according to claim 1, wherein the determined expectation constraint peer-to-peer model is obtained according to the expectation of parameters with fuzzy characteristics, and the specific calculation formula is as follows:
wherein, BezShows the starting time T of the motor train unit e occupying the operating area zezRepresents the ending time of the motor train unit e occupying the operation area z, mu represents the fuzzy coefficient, and pz1、pz2、pz3Respectively representing the shortest operation time, the most possible operation time and the longest operation time of the operation area z, D ' representing a stock line, D representing a set of operation lines, N ' representing the N ' th operation executed on each stock line, Nd′Indicates the maximum number of operations, Y, of the track dezdnThe nth operation executed by the motor train unit e on the station track d of the operation area z is shown.
5. The motor train unit dispatching method based on the uncertain processing framework and the rule combination algorithm as claimed in any one of claims 1 to 4, wherein the uncertain parameters specifically comprise uncertain operation time of each operation area, uncertain entering time of the motor train unit and uncertain entering time of the motor train unit.
6. The method for dispatching the bullet trains based on the uncertain processing framework and the rule combination algorithm as claimed in any one of claims 1 to 4, wherein the dispatching problem is an Open-Shop dispatching problem with uncertain processing time and uncertain processing sequence.
7. The method for dispatching a bullet train based on the uncertain processing framework and the rule combination algorithm according to any one of claims 1 to 4, wherein the step of solving the converted shunting model to obtain the optimal dispatching scheme specifically comprises the following steps:
solving the converted shunting model according to a preset rule combination algorithm to obtain the optimal scheduling scheme;
the preset rule combination algorithm comprises a first-come first-process initialization rule, an earliest idle track priority rule, a track space-time compatibility constraint rule and a shunting plan conflict resolution rule.
8. A motor train dispatching system based on an uncertain processing framework and a rule combination algorithm is characterized by comprising the following steps:
the system comprises a parameter extraction module, a parameter selection module and a parameter selection module, wherein the parameter extraction module is used for extracting a set, a deterministic parameter, an uncertain parameter and a variable which are involved in the dispatching of the bullet train aiming at the dispatching problem of the bullet train, and the uncertain parameter comprises a parameter which changes in an interval, a parameter with random attributes and a parameter with fuzzy characteristics;
the shunting model module is used for establishing a shunting model of the motor train station under the uncertain condition based on the set, the deterministic parameter, the uncertain parameter and the variable;
the model conversion module is used for acquiring a determined interval planning peer-to-peer model according to an interval variation range of parameters varying in an interval for the shunting model, acquiring a determined chance constraint peer-to-peer model according to probability distribution of the parameters with random attributes, acquiring a determined expectation constraint peer-to-peer model according to expectation of the parameters with fuzzy characteristics, and finally acquiring a converted shunting model;
the dispatching solving module is used for solving the converted shunting model to obtain an optimal dispatching scheme;
and the vehicle scheduling module is used for scheduling the vehicles of the bullet train according to the optimal scheduling scheme.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for dispatching trains based on an uncertain processing framework and a rule combination algorithm according to any of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the method for dispatching trains based on the uncertain processing framework and the rule-combination algorithm according to any of claims 1 to 7.
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