CN114723106B - Inter-station goods train cooperative flow distribution method based on fixed-point aggregation mode under mixed condition - Google Patents
Inter-station goods train cooperative flow distribution method based on fixed-point aggregation mode under mixed condition Download PDFInfo
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Abstract
The invention relates to a fixed point aggregation mode-based inter-station goods train cooperative flow distribution method under a mixed condition, which comprises the following steps: selecting the minimum station-staying time of at least two adjacent technical station vehicles as an objective function, and establishing a goods train cooperative flow distribution model between the technical stations by using arrival train disassembly operation constraint, departure train marshalling operation constraint, arrival train flow connection constraint and cooperative flow distribution adjustment constraint as constraint conditions; the order of the cargo trains is optimized through an improved Hui wolf algorithm, and a more optimal order of the cargo trains is searched by continuously updating the individual positions of the technical stations in an optimizing mode, so that the optimal order of the cargo trains and a flow distribution scheme are obtained. The invention has the advantages that: when the distribution plan is compiled in the technical station, the fixed-point aggregation mode under the mixed condition has higher applicability than the fixed-point aggregation mode, and the line loss condition of the operation diagram can be reduced; and a continuous traffic flow is provided for a rear technical station, and the overall transportation benefit is improved.
Description
Technical Field
The invention relates to the technical field of railway transportation, in particular to a method for coordinated flow distribution of goods trains among technical stations based on a fixed-point aggregation mode under a mixed condition.
Background
The technical stations are important nodes for railway network traffic flow organization, and the operation organization mode directly influences the turnover efficiency of the trucks. Distribution is used as a core problem of production scheduling of a railway technology station, a single technology station is mostly used as a research object for a long time, single-point information is isolated, and operation plan decision is limited. If the adjacent technical stations of the regional road network are regarded as a whole, the operation organization of the technical stations is perfected from the perspective of cooperation of operation between the technical stations, and the operation efficiency of the technical stations and the turnover efficiency of trucks can be effectively improved.
At present, research on the problem of flow distribution of a technical station can be roughly divided into three aspects of static flow distribution, dynamic flow distribution and comprehensive flow distribution, wherein the static flow distribution problem refers to flow distribution under the condition of determining the de-coding sequence and is the basis of dynamic flow distribution and comprehensive flow distribution, and a commonly used research method for static flow distribution comprises the following steps: a spreadsheet, a netflow, a woguer, a commodity transaction machine, etc.; the dynamic flow distribution problem is flow distribution under the condition that the decoding sequence is uncertain, essentially belongs to the uncertain resource distribution problem, and is more complex than the static flow distribution problem; the comprehensive distribution problem refers to comprehensive integrated research on sub-problems of station arrival and departure line application, dispatching application, train operation system selection and the like by taking technical station distribution as a center.
Therefore, the existing research on the operation optimization problem of the technical station mostly starts from static matching of station capacity, focuses on traffic flow connection of a single technical station, and considers cooperative compilation of operation plans between the technical stations and a mixed aggregation mode combining fixed aggregation and fixed aggregation, that is, less research is performed on the overall optimization of traffic flow connection under the fixed aggregation strategy by adopting a relaxed condition fixed aggregation strategy for under-axle trains meeting the conditions.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a cooperative flow distribution method for goods trains between technical stations based on a fixed-point aggregation mode under a mixed condition, and solves the problems that the prior research on the operation optimization problem of the technical stations mostly takes static matching of station capacity as a starting point and focuses on traffic flow connection of a single technical station.
The purpose of the invention is realized by the following technical scheme: a coordinated distribution method of goods trains between technical stations based on a fixed-point aggregation mode under mixed conditions comprises the following steps:
selecting the minimum station-staying time of vehicles at least two adjacent technical stations as a target function, and establishing a goods train cooperative flow distribution model between the technical stations by taking arrival train disassembly operation constraint, departure train marshalling operation constraint, arrival train flow connection constraint and cooperative flow distribution adjustment constraint as constraint conditions;
the optimal solution sequence and the optimal distribution scheme of the freight train are obtained by optimizing the solution sequence of the train through an improved wolf algorithm and continuously updating the individual positions of the technical station in an optimization searching mode to search for a more optimal solution sequence.
The arriving train disassembly operation constraint comprises: when the i-th arriving train has a train disassembly sequence number of 1, that is
When the train is disassembled, the actual starting time of the train disassembly operation is the earliest starting time; when the ith train release serial number is not 1, two situations exist, wherein the first situation is that the actual ending time of the current train release operation is greater than or equal to>
Later than the earliest starting time of the disassembly work of the train>
When the train breaks down, the actual starting time of the train breaking down operation is->
Second situation the actual end time ≥ based on the current sequential vehicle disassembly operation>
Is earlier than the earliest starting time of the disassembly work of the train>
Then the actual starting time of the disassembly operation of the train is->
Namely, the condition that the actual starting moment of the train disassembly operation meets the constraint condition is as follows:
wherein
To arrive at the train i the previous train to disintegrate.
The departing train grouping operation constraint includes: when the starting train group number of the jth train is the last train, that is
The actual end time of the train marshalling operation is the latest end time; when the jth departure train consist No. is not m, there are two situations, the first of which is when the actual start time of a subsequent train consist operation >>
Before the latest finishing time of the train marshalling operation>
When the train marshalling operation actually ends, the actual time is ^ greater than or equal to>
Second situation when the actual start time of a subsequent train consist operation is->
Later than the latest finishing time ^ of the train marshalling operation>
When the train marshalling operation actually ends, the actual time is ^ greater than or equal to>
Namely, the actual finishing time of the starting train marshalling operation meets the constraint conditions:
wherein
A train is marshalled for the next time the departure train j is marshalled.
The traffic flow connection constraint of the arriving train comprises the following steps: the number of the k-direction vehicles provided by the arriving train i for the departing train is required to be less than or equal to the total number of the k-direction vehicles in the arriving train i; when the departure train j goes to the traffic flow which is not coded k according to the formation rule, the departure train j does not absorb the traffic flow in the direction, namely the going direction of the traffic flow distributed by the departure train must be within the specified going direction of the formation plan; the method comprises the following steps that a traffic flow connection relation exists between an arriving train i and a departing train j, and the necessary condition is that the start time of the formation of the departing train j is necessarily behind the disassembly end time of the arriving train i; and (5) starting the train and fully constraining the train.
The coordinated flow adjustment constraints include:
under the fixed point aggregation mode of the relaxation conditions, whether the number of the trains which are not sent out by the original default axle meets the minimum train-forming number V of the fixed point aggregation mode of the relaxation conditions is judged m ' in Simultaneously less than the maximum marshalling number specified for the train starting from the first technical station
If so, carrying out marshalling and departure according to the regulations;
if the train flow is not met, the train flows of the first train and the second train in the same direction are distributed and adjusted, when the train flow of the second train does not meet the minimum number of the formed trains, but the sum of the train flows of the front and rear two trains in the same direction meets the condition of sending out the two trains with the minimum number of the formed trains, the first train set marshals part of the train flows to the second train set on the basis of meeting the condition of ending the set-point mode aggregation of the relaxation condition, so that the front and rear two trains can both meet the minimum number of the formed trains, and the just-in-time train sending is ensured.
The step of continuously updating the individual positions of the technical station in an optimizing manner to find a more optimal solution sequence comprises the following steps:
a1, inputting algorithm related parameters, enabling a wolf colony algebra gen =1, initializing alpha, beta and delta wolf individual positions to be null, enabling the grey wolf individual position information integral number to be formed by two parts of a disassembly sequence of the front n positions and a marshalling sequence of the rear m positions to adjust the adjacent train disassembly sequence, respectively generating an initial population CF of a departure train marshalling sequence and an initial population DD of an arrival train disassembly sequence, and combining the two to generate an initial grey wolf population with a popsize;
a2, performing static flow distribution on a solution-editing sequence corresponding to each wolf location by adopting a generalized static flow distribution method, calculating the fitness of wolf individuals in an initial population according to a flow distribution process objective function and constraint conditions to obtain the optimal value of each individual and the optimal value of a wolf population, and determining alpha, beta and delta wolfs of a first generation according to the fitness;
a3, carrying out cross operation on position codes of alpha, beta and delta of the three wolfs head to realize the exchange of high-quality information of the wolf population;
a4, approaching the omega wolf to three head wolfs, updating the position of the grey wolf population, recalculating the fitness value of the updated wolf population, sequencing the wolf population from small to large, and determining new alpha, beta, delta and omega wolfs;
and A5, judging whether gen is less than or equal to gen _ max, if not, enabling gen = gen +1, and executing the step A2 again, otherwise, outputting an optimal value.
The operation of interleaving the position codes of the three wolf heads alpha, beta and delta comprises the following steps:
by passing
The position information of 3 wolfs is crossed, the searching range of the wolfs is expanded, the information exchange among the high-quality wolfs is realized, wherein, the wolfs is judged and judged>
Is a new position after the communication of 3 head wolves information, and is/is>
Represents the positions of two wolfs, r [ d, e ], of any Geng generation of alpha, beta and delta wolfs]For the randomly generated distance of the crossover operation,
represents a pair->
Is carried out for a length r [ d, e ]]In which d denotes->
e then represents->
The interleaving is mainly related to the positions of d and e, namely interleaving operations are carried out on the d-th item to the e-th item in the disassembly sequence.
The approaching of the omega wolf to three head wolfs and the updating of the gray wolf population position comprise the following steps:
by the formula
With probability r m Selecting three wolfs and approaching the selected wolfs;
to the selected head wolf
Is not disturbed and is taken off>
Is indicated to be at>
Randomly selecting two positions u and v, interchanging the two positions, and if the generated random number is less than r c Taking the head wolf position after the disturbance operation as an object for the omega wolf to approach, otherwise, directly taking the selected head wolf position as the object for the omega wolf to approach;
simulating the hunting, enveloping and attacking process of the hunting objects under the leading of alpha, beta and delta wolfs by the convergence factor a according to the
Determining the moving distance a [ d, e ] of wolf group individual]，a[d,e] max The sum of the length of the individual positions of the wolf, namely the number of the train to be sent, is expressed as a [ d, e ]]Approaching to the selected position.
The invention has the following advantages:
1. when the distribution plan is compiled in the technical station, the fixed-point aggregation mode under the mixed condition has higher applicability than the fixed-point aggregation mode, the condition of line loss of the operation diagram can be reduced, the continuous traffic flow is provided for the rear technical station, and the improvement of the overall transportation benefit is facilitated.
2. Compared with independent distribution, the cooperative operation can effectively improve the working efficiency in the station and promote the global transportation optimization. The calculation results of the examples show that compared with the flow distribution operation of a single technical station, the flow distribution operation of the technical station cooperation flow distribution operation saves the total stop time of vehicles at two stations by 21.9h, the number of right-point departure vehicles in the phase plan is increased by 2 rows, and the model can effectively improve the traffic flow organization efficiency.
3. Compared with the GWO algorithm, the HDGWOO algorithm solving technology interstation cooperation flow distribution model has the advantages that convergence speed and solving precision are obviously improved, and the improved Huidong algorithm is applicable and feasible.
Drawings
FIG. 1 is a schematic illustration of the location of a technical station of the present invention;
FIG. 2 is a flow chart of distribution of cargo trains between stations in a hybrid aggregation mode;
FIG. 3 is a schematic diagram showing the presence or absence of an influence of a disassembly operation of a preceding train;
FIG. 4 is a schematic diagram showing the influence of the subsequent train disassembly operation;
FIG. 5 is a schematic diagram of integer coding based on the decoding order;
FIG. 6 is a schematic view of a crossover operation;
FIG. 7 is a flow chart of an improved Grey wolf algorithm;
FIG. 8 is a graph comparing the convergence curves of the HDGWO algorithm and the GWO algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application provided below in connection with the appended drawings is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the traffic flow from station C to station D and from station C to station D in fig. 1 will be described as an example. Part of the departure train and its absorbed traffic flow in the phase plan are shown in table 1. The train from the technical station B to the station C absorbs the traffic flow of 3 group numbers. The train from the technical station C to the station D absorbs the traffic flow of 2 group numbers. If the train B → C fails to have full train at the specified departure time due to insufficient traffic flow connection (full train means that the specified number of trains, weight and length standards of the train operation diagram are met, for example, the number of full trains is 45, the number of trains departing in the fixed-braiding aggregation mode can be issued only after 45 trains are met), the train is lost. At this time, if consideration is also given to the cooperative flow distribution of the technical stations B and C, there are two cases depending on whether the departure train C → D can be departed at the full-axle main point. If yes, train B → C really loses the line; if not, considering B, C factors such as train running time between two stations, technical operation time, departure requirement of minimum train marshalling number and the like, and comprehensively evaluating and considering the economic rationality of B → C under-off-axle running to C station and supplementing C → D with axle. If it is advantageous, then the station B cooperates with C to match the flow, and the B → C under-axis operation will not take care of the missing line.
Table 1 phase plan (part) of the network of fig. 1
| Serial number | Starting train | Absorb traffic flow |
| 1 | B→C | B → C, B → D, B → D and above |
| 2 | C→D | C → D, C → D and beyond |
When the line loss condition appears in the technical station, the influence can be caused to the operation of the station, and chain reaction can also lead to the fact that the traffic flow of the downstream technical station is not continuous enough. In order to optimize the flow distribution of the two stations at the same time, an operation mode for effectively reducing the number of lost lines is considered from the perspective of the cooperative operation of adjacent technical stations. In the fixed compilation aggregation mode, if the departure time of the departure train in the train operation chart does not meet the full-axle requirement, the departure cannot be performed, and only the line loss treatment can be performed; the fixed-point aggregation mode takes the guarantee of the train being dispatched at the right moment as a primary task, and strictly executes the dispatching according to the train operation diagram, namely dispatching according to the operation line arrangement sequence and the moment specified by the train operation diagram, and the train can not meet the full-axle requirement when dispatching; the condition fixed point aggregation mode is relaxed, the aggregation can be finished when the minimum train braiding number is reached in the specified time, otherwise, the lost line processing is carried out, the mode gives consideration to the advantages of fixed braiding and fixed point aggregation, and the occurrence of the lost line condition is effectively reduced.
As shown in fig. 2, the technical station uses a fixed-braiding aggregation mode to perform initial flow distribution, and determines whether the downstream technical station in the same direction will have insufficient traffic flow connection for the possible loss of the line in the mode. If yes, accumulating the vehicles by the under-axle train according to a fixed point aggregation strategy under the relaxation condition, and dispatching the train when the number of aggregated vehicles meets the minimum train aggregation number; otherwise, the under-axle train carries out the line loss treatment. And other trains continue to accumulate the vehicles according to the fixed aggregation strategy, and the vehicles are dispatched when the number of aggregated vehicles reaches the number of the original full-axle aggregated vehicles of the train. The fixed-point aggregation of the relaxation condition is based on the complete fixed-point aggregation, the aggregation time of the train is relaxed, and the minimum condition of the marshalling number is set, so that the train can marshal the trains as much as possible under the precondition of departure according to the diagram. It can be understood that: the fixed compilation aggregation mode is a mode traditionally used by railways, and can be issued only after the number of full trains is reached, so that the time for issuing the trains is not required.
The fixed point aggregation mode under the condition is relaxed, so that the number of times of line loss can be effectively reduced, the turnover of vehicles is accelerated, and the overall transportation efficiency is improved; but at the same time it is limited by shipping efficiency, shipping costs and throughput. Therefore, relaxing the condition fixed point aggregation allows the car to be sent out under the shaft, but cannot be unconditionally sent out under the shaft. When the mode is adopted, the influence of the operation of the train with the lower axle must be carefully analyzed so as to find out a reasonable minimum train formation number under the condition of the existing passing capacity.
The influence factors of the minimum train formation number mainly comprise two categories of factors of determining the value of the formation number of the goods train and factors of evaluating the quality of the value of the formation number of the goods train, namely the minimum train formation number under the relaxation condition fixed point aggregation mode meets the constraint of the railway transportation capacity on the formation number of the trains, and improves the transportation efficiency and the benefit of the fixed aggregation mode, so that the minimum train formation number based on the transportation capacity and the minimum train formation number based on the transportation benefit can be divided into the value of the minimum train formation number based on the transportation capacity and the value of the minimum train formation number based on the transportation benefit.
The invention assumes sufficient railway transportation capacity, and analyzes the operation influence of the under-axle train with the minimum train formation number based on transportation benefits:
(1) Yield analysis of open train
The benefit of operating an under-axle train is mainly reflected in the utilization of the line capacity. Compared with the line loss treatment, the method has the advantages that the partial line capacity is utilized for the operation of the under-axle train, and the line capacity benefit is the benefit generated by the under-axle train in the operation section. The benefit of operating an off-axle train is expressed as follows:
Z 1 ＝V m ' in ·C 1 ·L
in the formula: z 1 Earnings for opening the under-axle train; v m ' in Compiling a minimum train formation number for the station B in the relaxed condition fixed point aggregation mode, C 1 The per kilometer profit for the truck; l is B, C distance length between two stations.
(2) Loss analysis of open under-axle train
The cost of operating the train with the lack of the axle is mainly concentrated on the loss of the locomotive, compared with the line loss treatment, the total transportation task is the same in the stage time, the number of the trains which are compiled is reduced, more starting trains are formed, and the cost of the locomotive is increased. The loss of the open train is expressed as follows:
in the formula: z 2 The loss of the train with the lower axle is started; c 2 Cost per hour for the locomotive; beta is a beta 1 Assisting the running rate of the locomotive along the line; c 3 Cost per kilometer expenditure for locomotives; beta is a 2 The auxiliary running rate of the locomotive.
According to the analysis, the minimum train formation number under the condition-relaxed fixed-point aggregation mode is to ensure that the operation income of the under-axle train is larger than the operation loss, namely
Z 1 -Z 2 ≥0
Taking station B as an example, the invention considers the operation requirement of station C and constructs a collaborative flow distribution model. To simplify the complexity of the problem, set: the technical station adopts a single-push single-slide operation mode; each yard in the technical station meets the requirements of train receiving and dispatching operation; the train arrival capacity of the technical station is sufficient, and continuous train receiving and departure can be realized; the conditions of debugging faults, servicing and maintenance are not considered; the departure train which conflicts with the train with the shaft lacking can be provided with traffic flow by the station parking. Wherein, the definition of main parameters and variables is shown in tables 2 and 3;
TABLE 2 parameters and their symbolic description
TABLE 3 variables and their symbolic illustrations
In the course of considering the traffic stream connection of adjacent departure trains in the same direction, introducing
And judging a disassembled train which can provide traffic flow for the k-direction off-axis train in the fixed point aggregation mode under the relaxation condition, thereby constructing adjacent technical stations to adjust the marshalling scheme of the off-axis train in cooperation with flow distribution adjustment constraint, enabling the off-axis train to reach the departure condition as far as possible, and optimizing the flow distribution result of the double technical stations. />
In order to judge the effectiveness of the inter-station cooperative flow distribution by adopting a hybrid aggregation mode more intuitively, the method selects the minimum total station residence time of vehicles of two adjacent technical stations as the optimization target of the model of the invention by referring to a traditional technical station flow distribution model, namely:
and (3) limitation of disassembly operation of the arriving train: when the i-th arriving train disassembly number is 1, i.e.
Since there is no previous disassembly work, the actual start time of the train disassembly work is the earliest possible start time. When the i-th train disassembly number is not 1, the situation is complicated due to interference of the preamble train disassembly work, and there are two cases, as shown in fig. 3. The first case: actual end time->
Later than the earliest starting time of the disassembly work of the train>
Then, as shown in FIG. 3 (a), the actual start time of the train disassembly operation is ^ based on ^ s>
The second situation is that: actual end time->
Before the earliest start time of the disassembly work of the train>
Then, as shown in FIG. 3 (b), the actual start time of the train disassembly operation is ^ based on the determined value>
The actual starting time of the arriving train disassembly operation meets the following constraint:
wherein
To reach the train i disassembled last time, obviously there is ^ H>
Constraint of starting train marshalling operation: when the starting train formation serial number of the jth train is the last train, that is
Since there is no train formation work any more in the following, the actual end time of the train formation work is the latest possible end time thereof. When the jth departure train formation sequence number is not m, there are two cases due to interference of the subsequent train formation work, as shown in fig. 4. The first case: when the actual start time of a subsequent train consist operation is pick>
Before the latest possible end time ^ of the train marshalling operation>
In the meantime, as shown in fig. 4 (a), the actual completion time of the train marshalling operation is £ er>
The second case: when the actual start time of a subsequent train consist operation is pick>
Is later than the latest possible end time->
Then, as shown in FIG. 4 (b), the actual completion time of the train marshalling operation is ^ based on the judgment result>
The actual finishing time of the starting train marshalling operation meets the following constraint:
wherein
For the next consist train of the consist of the departure train j, it is obvious that ≥ is present>
And (3) traffic flow connection constraint of the arriving train: considering that the distribution operation cannot completely distribute all the arriving train vehicles in the phases to the departing trains in the phases, the train parking at the station may exist after the operation is finished. The number of vehicles in the k direction provided by the arriving train i for the departing train must be less than or equal to the total number of vehicles in the k direction in the arriving train i.
When the departure train j goes to the traffic flow which specifies the departure k according to the formation, the departure train j does not absorb the traffic flow in the direction, namely, the departure of the traffic flow assigned by the departure train must be within the specified departure of the formation plan.
In the formula, M is a large normal number and can be used for compiling train into train numbers.
The arrival train i and the departure train j have a traffic connection relationship, and the necessary condition is that the starting time of the train formation of the departure train j is after the ending time of the disassembly of the arrival train i.
And (5) starting the train and fully constraining the train. Considering the research in the text focuses on the effectiveness of coordinated distribution of goods trains between technical stations in a hybrid aggregation mode (namely, a conditional fixed point aggregation mode is adopted for under-axle trains on the basis of the fixed aggregation mode), and in order to ensure economic benefit, the minimum number of formation vehicles in the relaxed condition fixed point aggregation mode is defined. In order to ensure the effectiveness of subsequent cooperative flow distribution and avoid section crossing caused by different standards, the invention only selects the number of the compiled trains as the starting judgment condition of the train.
And (3) coordinating flow distribution adjustment constraint: in the fixed-marshalling aggregation mode, the train which is not sent out because the number of the marshalling trains is not reached is assumed to be the R-th list sending train in the direction of the K station of the B station. When the cooperative flow distribution adjustment is carried out by adopting a relaxation condition fixed point aggregation mode, the following judgment steps are carried out:
aggregation end condition limiting:
and under the fixed point aggregation mode of the relaxation condition, judging whether the number of the vehicles of the original default axle which do not send out the trains meets the minimum train aggregation number of the fixed point aggregation mode of the relaxation condition.
Generating the relevant information of the distribution scheme under the original compiling and binding mode,
a) The R-1 st train goes to a starting train in the k direction, namely a previous train in the same direction with the under-train;
b) Sequence number of first train of disassembled trains after starting train of R-1 st train to k direction finishes assembling
Namely, a first train disassembly train sequence number of the train flow can be provided for the train with the lower train;
c) The R-th train is a starting train which is opened in the k direction, namely an under-axle train discussed herein;
d) The last train before the starting train in the R-th train which starts to the k direction finishes the aggregation is disassembledSequence number of train
Namely, the train with the train flow can be provided with the sequence number of the last train disassembly of the train flow.
And combining the information to obtain arriving trains capable of providing traffic flows for the under-cut trains, and obtaining the number of the vehicles capable of providing the under-cut departing trains. Judging whether the number of the vehicles obtained by the under-axle train at the moment meets the minimum compiled number V under the relaxation condition fixed point aggregation mode m ' in And at the same time, the number of the trains is less than the maximum marshalling number specified by the train starting from the B station
If the requirements are met, marshalling and departure can be carried out according to the operation diagram.
If not, aiming at the train which is not sent out by the lack of the axle
Considering the adjustment of the traffic flow distribution of the first-order train and the second-order train in the same direction, when the following formula is satisfied,the method is characterized in that the number of train flows of a rear-knitting underbeam train does not meet the minimum number of finished trains, but the sum of the flows of two front and rear trains in the same direction can send out two starting trains under the condition of the minimum number of finished trains, and on the basis that the train set meets the condition of relaxation, the fixed-point mode aggregation ending condition, part of the flows are organized into the rear-knitting underbeam train set, so that the front and rear trains can meet the minimum number of finished trains, and the train is ensured to be started at the right moment.
The adjustment measures of the distribution plan of the station B are carried out in the optimal solution order generated by the fixed compilation aggregation mode, and in order to ensure that the operation of the underbalanced train does not influence the subsequent train, so that a new additional underbalanced train is generated, the operation requirement of the underbalanced train can be met by meeting the station parking under the original distribution flow plan, and therefore, in the new distribution plan, even if the operation of the underbalanced train uses the original traffic flow which is distributed into the subsequent train, the influence on the operation of the underbalanced train can be reduced to the greatest extent.
Through analysis of the problem of cooperative flow distribution among technical stations, the problem can be described as a multi-constraint optimization problem, the difficulty is in processing constraint conditions, and the wolf algorithm can effectively solve the multi-constraint problem; the gray wolfs mostly like the living life, follow the strict pyramid social level hierarchy, and can be divided into four levels of Alpha (Alpha) wolfs, beta (Beta) wolfs, delta (Delta) wolfs and Omega wolfs according to the social level, wherein the Omega wolfs search, surround, hunt, attack and the like on the hunting objects under the collar of the three head wolfs.
The standard gray wolf algorithm is updated by the formula:
a＝2-t/t max
A＝2a·r 1 -a
C＝2r 2
where t is the current iteration algebra, t max The maximum iteration number of the algorithm is obtained; a is an adjustment parameter (convergence factor) that decreases linearly from 2 to 0 with iteration; r is 1 And r 2 Is [0,1]Wherein A and C are coefficient vectors; d α 、D β 、D δ Respectively represent the distances between alpha, beta, delta and other individuals, X α (t)、X β (t)、X δ (t) represents the current positions of α, β, and δ, respectively, X 1 (t)、X 2 (t)、X 3 (t) represents the direction and step size of the ω wolf heading toward α, β, δ, respectively. X (t + 1) is the final position of the omega wolf.
The gray wolf algorithm usually adopts a real number coding form in coding to solve a continuous optimization problem. And in consideration of the particularity of the traffic flow connection problem of the technical station, designing an integer coding strategy based on the de-coding sequence arrangement. During coding, the sequence arrangement of the train sending is adopted for coding, wherein the first n integers represent the disassembly sequence of the arriving train, and the last m integers represent the marshalling sequence of the departing train; integer value is equivalent to the de-coding orderAnd therefore no decoding process is required. As shown in figure 5 of the drawings,
wherein->
A position vector for the gen generation individuals i with respect to the arriving train disassembly sequence; CF (compact flash) i gen Position vectors for the gen generation individuals i arranged in sequence with respect to the starting train consist. Marking/conjunction>
As shown in fig. 6, during the gray wolf algorithm optimization process, the ω wolf is updated based on the position information of α, β, δ, so as to realize the enclosing, hunting, attacking, searching and other operations of the hunting, and find the optimal solution, but there is no information exchange among the α wolf, β wolf, δ wolf as the 3 head wolfs carrying the optimal information in the wolf group. Therefore, in order to search a more optimal solution area using the high-quality information of the wolf, the positional information of 3 wolfs is subjected to a cross operation by the following expression, thereby expanding the search range near the wolf and realizing information exchange between the high-quality wolfs.
Wherein, the first and the second end of the pipe are connected with each other,
the new position is 3 new positions after the information of the wolf head is exchanged; />
The positions of two wolfs of the Geng generation alpha, beta and delta wolfs are arbitrarily selected; r [ d, e ]]A distance for a randomly generated crossover operation; />
Represents a pair->
Run length of r [ d, e ]]In which d denotes->
e then represents->
The intersection is mainly related to the positions of d and e, namely the intersection operation is carried out on the d-th item to the e-th item in the disassembly sequence; unreasonable individuals are easily generated by carrying out the cross operation on the marshalling sequence, and in order to ensure the feasibility of the individuals, only the disassembly sequence part in the Hui wolf position is subjected to the cross operation, so that d is more than or equal to 1 and less than or equal to e and less than or equal to n.
According to
And (3) performing information exchange on the 3 wolfs, mixing the regenerated 6 wolf individuals with the wolf population, recalculating fitness, and establishing new alpha, beta and delta wolfs.
Because the coding modes are different, the individual updating formula under the real number coding rule can not be directly used, aiming at the characteristics of the integer coding mode, the following grey wolf individual updating formula based on the discrete problem solution space mechanism is adopted, the following formula represents the individual position updating, and the individual position updating is firstly carried out
Namely, one head wolf is selected to be close to the head wolf; based on which a decision is made to->
Namely, the selected wolf head is disturbed; finally make a decision on whether or not>
Namely, the whole gray wolf group is close to the wolf. The whole individual updating process is completed by the following three steps.
With probability r m And selecting the three head wolves, and approaching the selected head wolves.
to the selected head wolf
The disturbance operation of (2): />
Namely, represent is at>
Two positions u and v are randomly selected and exchanged. If the generated random number is less than r c Taking the head wolf position after the disturbance operation as an object for the omega wolf to approach; otherwise, directly taking the selected head wolf position as the object of the omega wolf approach.
simulating the leading of omega wolf group under the conditions of alpha, beta and delta wolf by using the convergence factor a, and the method is used for solving the problem of the existing methodHunting, enveloping, attacking Process of Hunting objects a [ d, e ]] max The length of the individual location of the wolf (i.e. the sum of the number of trains coming out) to
Determining the moving distance a [ d, e ] of wolf group individual]With a [ d, e ]]Approaching to the selected position.
As shown in fig. 7, the flow of the improved graying algorithm of the present invention includes:
step1 initialization: inputting algorithm related parameters, initializing the individual positions of alpha, beta and delta wolfs to be empty, wherein the integral number of the position information of the wolfs is composed of a disassembly sequence of the first n positions and a grouping sequence of the last m positions; adjusting the adjacent train disassembly sequence, and respectively generating an initial population CF of a starting train marshalling sequence and an initial population DD of an arriving train disassembly sequence; combining the two to generate an initial population of wolfs of size popsize.
And (3) calculating the Step2 fitness: static flow allocation is carried out on the solution-editing sequence corresponding to each grey wolf position by adopting a generalized static flow allocation method, fitness calculation is carried out on the grey wolf individuals in the initial population according to a flow allocation process objective function and constraint conditions, and the optimal value of each individual and the optimal value of the grey wolf population are obtained; and determining the first generation of alpha, beta and delta wolfs according to the fitness.
Step3, exchanging the wolf information: the position codes of the alpha, beta and delta of the three wolfs are crossed to realize the exchange of the high-quality information of the wolf population.
Step4 individual position updating: and according to the individual position updating step, the omega wolf is close to three wolfs, the grey wolf population position is updated, the fitness value of the updated wolf population is recalculated, the updated wolfs are sorted from small to large, and new alpha, beta, delta and omega wolfs are determined.
Step5, judging whether the termination condition is met: gen = gen +1, if gen is less than or equal to gen _ max, step2 is carried out; otherwise, the program ends and the optimal value is output.
The invention has the following calculation and algorithm parameter settings: the technical operation time of arriving trains and departing trains is 30min, the technical operation time of the trains is 15min, the number of the fixed-marshalled trains under the fixed-marshalled stations is 45, the technical station in front of the train operation is a C station, the distance between the B, C and the two technical stations is 160km, and the speed per hour of the ordinary freight train is 80km/h. The cost calculation parameters are shown in table 4, and the minimum train formation number in the relaxation condition fixed point aggregation mode from the station B to the station C is calculated to be 25; the size of the Huidou wolf population is 50, the maximum iteration number is 100, and the disturbance probability is 0.6.
Table 4 fee calculation table
| β 1 | β 2 | C 1 | C 2 | C 3 | |
| Expense (Yuan) | 0.12 | 0.2 | 1.25 | 203.4 | 23.53 |
The B station distribution result (hereinafter, referred to as the B station regulation front-rear train distribution plan) is calculated to determine whether to consider the rear technical station traffic demand, as shown in tables 6 and 8. Similarly, the distribution plan of the trains before and after the C station adjustment can be obtained, and as shown in tables 10 and 12, the format of the starting train formation content is train number/direction/number of trains.
After the technical stations B, C cooperatively operate, the total station stop time of the vehicles at the two technical stations is saved by 21.9h, the number of trains sent out at the positive point is increased by two rows, namely 10012 trains sent out at the station B and 20020 trains sent out at the station C, and the total number of sent out vehicles in the stage time is increased by 61. The results of the coordinated operation between the technical stations and the flow distribution of the single technical station are shown in table 13, and compared with the mode that the technical stations operate independently, the coordinated operation mode obviously improves the operation efficiency in the stations and the overall benefit of the regional road network.
The flow distribution scheme of the technical station B, C before and after further contrast adjustment can find that:
(1) In table 6, when the freight train distribution is performed based on a single technical station, 10012 times of trains in the B station cannot reach the full-axle departure condition in the consolidation and aggregation mode, and 10012 times of train under-axle stop increases the stop time of the trains in the B station. In table 8, after the two technical stations cooperate, 10012 trains in the station B satisfy the determination condition, and 10012 trains and their neighboring trains departing from the same direction are distributed and adjusted by the relaxed condition fixed point aggregation mode, so that 10012 can be dispatched in a phased time, and the required traffic flow is provided for the distribution operation in the station C. Compared with the independent flow distribution of the technical station, the stop time of the vehicles at the station B after cooperative operation is saved by 37.308h. In the result of the independent distribution of the station B, 10022 is similarly out of the train due to the failure to meet the departure condition, but since the train cannot provide the flow for the station C, the flow is not adjusted, and if the coordinated distribution of the multiple technical stations is considered, the flow can be further optimized.
(2) In table 10, when the technical stations distribute traffic independently, the train at station C goes to the direction D for 20024 times of underbalance shutdown, which increases the stop time of the train at station C. In table 12, after the two technical stations cooperate, 10012 trains in station B provide the same-direction traffic flow for station C, and 20024 trains in station C are sent out under the full-axle departure condition. Compared with the independent flow distribution of the technical station, the stop time of the vehicles at the C station after the cooperative operation is increased by 15.408h, but the stop time of the vehicles at the C station is increased by one more train after the cooperative operation than the stop time of the vehicles at the C station after the cooperative operation, and the sample size is too small, so that the stop and parking reduction of the vehicles at the station in the cooperative operation mode is not obvious, and the improvement can be realized after the case size is properly enlarged.
Through the analysis, the technical inter-station cooperative flow distribution is carried out based on the fixed-point aggregation mode under the mixed condition, so that the intra-station operation efficiency and the whole transportation benefit of the freight train can be improved, the condition of line loss of the operation diagram can be effectively reduced, and the transfer reliability of the truck is improved.
TABLE 5 operating scheme for de-editing before B adjustment in technical station
Table 6 technical station B flow distribution scheme before adjustment
TABLE 7 technical station B adjusted train decombined plan
TABLE 8 post-adjustment distribution scheme for technical station B
TABLE 9 technical station C train decombination scheme before adjustment
TABLE 10 technical station C distribution scheme before adjustment
TABLE 11 technical station C adjusted train decombined scheme
Table 12 flow distribution scheme after adjustment of technical station C
TABLE 13 comparative analysis of distribution results
TABLE 14 increase of departure train information after cooperative work
| Number of departure | Grouping content | Number of departure | Grouping content |
| 10012 | 10023/D/9,10011/C/7,10013/C/9 | 20024 | 20023/D/4,20023/E/33,10012/D/8 |
Taking the process of adjusting the front distribution flow of the technical station B as an example, the result of comparing the convergence curves of the improved grey wolf algorithm and the traditional grey wolf algorithm is shown in FIG. 8, the total residence time of the vehicle in the station for searching the optimal distribution flow scheme by the two algorithms is different, and the convergence speed of the algorithms is also different. As can be seen from fig. 8: the optimal residence time of the grey wolf algorithm is 2182.88h h, while the optimal residence time of the improved grey wolf algorithm is 2176.13h, and the improved grey wolf algorithm is obviously superior to the grey wolf algorithm in the solution precision. In addition, the average iteration number of the gray wolf algorithm is 93 generations, the average iteration number of the improved gray wolf algorithm is 51 generations, and the convergence rate of the improved gray wolf algorithm is obviously improved. Compared with the traditional grey wolf algorithm, the improved grey wolf algorithm has obvious advantages in the aspect of solving the comprehensive performance of the distribution scheme.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A coordinated flow distribution method for goods trains between technical stations based on a fixed-point aggregation mode under a mixed condition is characterized by comprising the following steps: the cooperative flow distribution method comprises the following steps:
selecting the minimum station-staying time of vehicles at least two adjacent technical stations as a target function, and establishing a goods train cooperative flow distribution model between the technical stations by taking arrival train disassembly operation constraint, departure train marshalling operation constraint, arrival train flow connection constraint and cooperative flow distribution adjustment constraint as constraint conditions;
optimizing the order of the deplylation of the trains by an improved Husky algorithm, continuously updating the individual positions of the technical stations by an optimizing mode to find a more optimal order of the deplylation, and further obtaining the optimal order of the deplylation of the freight trains and a flow distribution scheme;
the step of continuously updating the individual positions of the technical station in an optimizing manner to find a more optimal solution sequence comprises the following steps:
a1, inputting algorithm related parameters, enabling a wolf colony algebra gen =1, initializing alpha, beta and delta wolf individual positions to be null, enabling the grey wolf individual position information integral number to be formed by two parts of a disassembly sequence of the front n positions and a marshalling sequence of the rear m positions to adjust the adjacent train disassembly sequence, respectively generating an initial population CF of a departure train marshalling sequence and an initial population DD of an arrival train disassembly sequence, and combining the two to generate an initial grey wolf population with a popsize;
a2, performing static flow distribution on a solution-editing sequence corresponding to each wolf location by adopting a generalized static flow distribution method, calculating the fitness of wolf individuals in an initial population according to a flow distribution process objective function and constraint conditions to obtain the optimal value of each individual and the optimal value of a wolf population, and determining alpha, beta and delta wolfs of a first generation according to the fitness;
a3, carrying out cross operation on position codes of alpha, beta and delta of the three wolfs head to realize the exchange of high-quality information of the wolf population;
a4, approaching the omega wolf to three head wolfs, updating the position of the grey wolf population, recalculating the fitness value of the updated wolf population, sequencing the wolf population from small to large, and determining new alpha, beta, delta and omega wolfs;
a5, judging whether gen is less than or equal to gen _ max, if not, enabling gen = gen +1, and executing the step A2 again, otherwise, outputting an optimal value;
the operation of interleaving the position codes of the three wolf heads alpha, beta and delta comprises the following steps:
by passing
The position information of 3 wolfs is crossed, the searching range of the wolfs is enlarged, the information exchange between the good wolfs is realized, wherein, the wolfs are combined in the large wolf area>
Is a new position after the communication of the 3 head wolfs information, and is combined with the voice mail server>
Represents the positions of two wolfs, r [ d, e ], of any Geng generation of alpha, beta and delta wolfs]For a randomly generated distance of a cross operation>
Represents a pair->
Run length of r [ d, e ]]In which d denotes->
e then represents->
The intersection is mainly related to the positions of d and e, namely the intersection operation is carried out on the d-th item to the e-th item in the disassembly sequence;
the approaching of the omega wolf to three head wolfs and the updating of the gray wolf population position comprise the following steps:
by the formula
With probability r m Selecting three wolfs and approaching the selected wolfs;
to the selected head wolf
The operation of the disturbance of (2),
is indicated to be at>
Randomly selecting two positions u and v, interchanging the two positions, and if the generated random number is less than r c Taking the head wolf position after the disturbance operation as an object for the omega wolf to approach, otherwise, directly taking the selected head wolf position as the object for the omega wolf to approach;
simulating the hunting, enveloping and attacking process of the hunting objects under the leading of alpha, beta and delta wolfs by the convergence factor a according to the
Determining the moving distance a [ d, e ] of wolf group individual]，a[d,e] max The sum of the length of the individual positions of the wolf, namely the number of the train to be sent, is expressed as a [ d, e ]]Approaching to the selected position.
2. The coordinated distribution method for goods trains among technical stations based on the fixed-point aggregation mode under the mixed condition according to claim 1, characterized in that: the arriving train disassembly job constraints include: when the ith arrival train disassembly serial number is 1, S i θ If =1, the actual start time of the train disassembly operation is the earliest start time; when the ith train disassembly serial number is not 1, two situations exist, wherein the first situation is the actual ending time of the current sequence train disassembly operation
Later than the earliest starting time of the disassembly work of the train>
When the train is disassembled, the actual start time of the disassembly operation isIs->
Second situation the actual end time ≥ based on the current sequential vehicle disassembly operation>
Is earlier than the earliest starting time of the disassembly work of the train>
Then the actual starting time of the disassembly operation of the train is->
The constraint condition that the actual starting moment of the train disassembly operation meets the constraint condition is as follows:
wherein
To arrive at the train i the previous train to disintegrate.
3. The coordinated distribution method for goods trains among technical stations based on the fixed-point aggregation mode under the mixed condition according to claim 1, characterized in that: the starting train grouping operation constraint comprises: when the starting train group number of the jth train is the last train, that is
The actual end time of the train marshalling operation is the latest end time; when the jth departure train consist No. is not m, there are two situations, the first of which is when the actual start time of a subsequent train consist operation >>
Before the latest finishing time of the train marshalling operation>
When the train marshalling operation is finished, the actual finishing time of the train marshalling operation is>
Second situation when the actual start time of a subsequent train consist operation is->
Later than the latest finishing time ^ of the train marshalling operation>
When the train marshalling operation actually ends, the actual time is ^ greater than or equal to>
Namely, the actual finishing time of the starting train marshalling operation meets the constraint conditions:
wherein
A train is marshalled for the next trip of the starting train j.
4. The coordinated distribution method for goods trains among technical stations based on the fixed-point aggregation mode under the mixed condition according to claim 1, characterized in that: the traffic flow connection constraint of the arriving train comprises the following steps: the number of the k-direction vehicles provided by the arriving train i for the departing train is required to be less than or equal to the total number of the k-direction vehicles in the arriving train i; when the departure train j goes to a traffic flow which specifies that k is not numbered according to the formation, the departure train j does not absorb the traffic flow in the direction, namely the going direction of the traffic flow distributed by the departure train must be within the specified going direction of the formation plan; the method comprises the following steps that a traffic flow connection relation exists between an arriving train i and a departing train j, and the necessary condition is that the start time of the formation of the departing train j is necessarily behind the disassembly end time of the arriving train i; and (5) starting the train and fully constraining the train.
5. The coordinated distribution method for goods trains among technical stations based on the fixed-point aggregation mode under the mixed condition according to claim 1, characterized in that: the coordinated flow adjustment constraints include:
under the fixed point aggregation mode of the relaxation condition, whether the number of the trains which are not sent out by the original default axle meets the minimum train aggregation number V of the fixed point aggregation mode of the relaxation condition is judged m ' in Simultaneously less than the maximum marshalling number specified for the train starting from the first technical station
If so, grouping and dispatching according to the regulations;
if the train flow is not met, the train flows of the first train and the second train in the same direction are distributed and adjusted, when the train flow of the second train does not meet the minimum number of the formed trains, but the sum of the train flows of the front and rear two trains in the same direction meets the condition of sending out the two trains with the minimum number of the formed trains, the first train set marshals part of the train flows to the second train set on the basis of meeting the condition of ending the set-point mode aggregation of the relaxation condition, so that the front and rear two trains can both meet the minimum number of the formed trains, and the just-in-time train sending is ensured.
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