CN110580606B - Matching method of railway transportation data - Google Patents
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Abstract
The invention discloses a matching method of railway transportation data, which is used for matching raw material purchase data with railway transportation data to generate train receiving data, and comprises the following steps: acquiring raw material purchasing data; acquiring railway transportation data; applying a greedy random self-adaptive search algorithm to raw material purchase data and railway transportation data, calculating vehicle receiving data by taking carrier information and origin information as constraint conditions, and obtaining a local optimal solution of the vehicle receiving data; and (3) carrying out local neighborhood search on the local optimal solution of the vehicle receiving data by applying a tabu search algorithm, storing the searched local optimal solution by a tabu table, marking the local optimal solution marked as tabu, and obtaining the global optimal solution of the vehicle receiving data by the tabu search algorithm through releasing the local optimal solution by a scofflaw. The invention can realize unmanned automatic vehicle receiving, effectively improve the vehicle receiving accuracy, reduce the vehicle receiving time and improve the working efficiency.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a matching method of railway transportation data.
Background
Most of raw materials of iron and steel production enterprises are transported into factories through railways, 1000 cars are transported into factories in daily life, more than 70% of the car receiving time is between 8 hours in the evening and 5 hours in the morning, the car receiving frequency is high, and night operation is needed. When receiving the train, a train receiving worker needs to check train number information, takes a variety and an invoice position as query conditions, queries train transportation order information provided by a steel mill purchasing center, corresponds to railway train-following tickets, manually matches information such as suppliers, shipping units, departure stations, order numbers and the like to each train number, fills train departure time and station arrival time to form a train entering check batch, generates a train entering checked batch number and sends the train entering checked batch number to facilitate the development of lower working procedures in a train. The checking and recording work of the series is completed by manual checking of the car receiving workers, so that the car receiving time rate and the accuracy rate cannot be ensured. Particularly when the vehicle receiving amount is large, data input errors are easy to cause. Erroneous data can affect the efficiency of work, and more severe errors can also cause work accidents.
Disclosure of Invention
The invention provides a matching method of railway transportation data, which is used for matching raw material purchase data with railway transportation data to generate train receiving data, and comprises the following steps:
acquiring raw material purchase data, wherein the raw material purchase data comprises carrier information and origin station information;
acquiring railway transportation data, wherein the railway transportation data also comprises carrier information and origin station information;
applying greedy random self-adaptive search algorithm to raw material purchase data and railway transportation data, calculating vehicle receiving data and obtaining a local optimal solution of the vehicle receiving data by taking carrier information and origin information as constraint conditions, wherein the vehicle receiving data comprises supplier information, carrier information, origin information, arrival information, train number information, order information and raw material information;
and (3) carrying out local neighborhood search on the local optimal solution of the taxi data by applying a tabu search algorithm, generating a matching scheme based on the taxi number information and the order information in the process of the local neighborhood search, storing the searched local optimal solution by a tabu table, marking the local optimal solution marked as tabu, and obtaining the global optimal solution of the taxi data by the tabu search algorithm through releasing the tabu rule.
According to one embodiment of the present invention, the raw material purchase data further includes: vendor information, raw material information, order information and settlement information, wherein the order information and settlement information are related to a matching rule, and a candidate set C of the pickup data is determined according to the matching rule.
According to one embodiment of the invention, the railway transportation data further comprises: the system comprises train wagon number information, waybill information, departure information and schedule information, wherein arrival information is calculated according to the departure information and the schedule information.
According to one embodiment of the invention, a greedy random adaptive search algorithm includes:
an initial construction step, namely constructing a solution set S and a candidate set C of the vehicle-connected data, initializing the solution set S and the candidate set C, selecting items in the candidate set C by a greedy function, judging whether constraint conditions are met, storing the items meeting the constraint conditions into a limiting candidate list RCL, randomly selecting one item from the RCL, adding the selected item into the solution set S, updating the candidate set C, and cycling the above processes until the rest items in the candidate set C do not meet the constraint conditions, wherein the solution set S obtained in the initial construction step is a local initial solution;
and (3) an optimized searching step, namely, locally searching the local initial solutions in the solution set S by applying a 2-opt algorithm to obtain a local optimal solution.
According to one embodiment of the invention, the constructing step comprises:
s11, initializing a solution set S, enabling the solution set S to be empty,
s12, initializing a candidate set C;
s13, calculating greedy values of all items e, namely e E C, in the candidate set C by using a greedy function;
s14, judging whether the candidate set C is empty, executing step S19, wherein the candidate set C is not empty, namelyExecuting step S4;
s15, respectively taking out the metersThe minimum and maximum values of the greedy values obtained by calculation, i.e. c min ＝ min{c(e)|e∈C},c max ＝max{c(e)|e∈C}；
S16, establishing a limiting candidate list RCL, wherein an item e in the limiting candidate list RCL meets the following constraint conditions: RCL= { e ε C|c (e) ∈c min +α(c max -c min )}；
S17, mechanically selecting an item e from the limiting candidate list RCL, and putting the item into the solution set S;
s18, updating the candidate set C to be the rest item in the limiting candidate list RCL, and returning to the step S13;
and S19, returning to the solution set S, and ending the construction step.
According to one embodiment of the invention, the greedy function has a greedy parameter α, the greedy parameter α has a value in a range from 0 to 1, the greedy function adopts a pure random strategy when α=0, and the greedy greedy function adopts a pure greedy strategy when α=1.
According to one embodiment of the present invention, in the optimizing and searching step, the car number information of the local initial solution in the solution set S is adjusted, and the local initial solution is optimized according to the number of cars to obtain a local optimal solution.
According to one embodiment of the invention, the tabu search algorithm includes:
s21, obtaining a local optimal solution obtained by a greedy random self-adaptive search algorithm, and taking the local optimal solution as a current solution;
s22, initializing a tabu table of a tabu search algorithm, firstly emptying the tabu table, and then storing the local optimum Jie Yi times into the tabu table as a tabu object;
s23, randomly selecting a neighborhood structure for the current solution to generate a neighborhood solution, wherein the neighborhood solution forms a candidate solution set;
s24, for the candidate solution set, judging whether the candidate solution meets the scofflaw or not respectively, if the candidate solution meets the scofflaw, selecting the optimal candidate solution to replace the current solution, replacing the tabu object which enters the tabu table earliest, updating the tabu table, then executing the step S26, and if no candidate solution meets the scofflaw, executing the step S25;
s25, judging the tabu attribute of the candidate solution in the candidate set, selecting the best non-tabu candidate solution from the candidate solution set as the current solution, replacing the tabu object entering the tabu table earliest, updating the tabu table, and then executing the step S26;
s26, adding 1 to the iteration number and judging whether the maximum iteration number is reached, if the maximum iteration number is reached, meeting the termination condition, ending the tabu search algorithm, outputting the current solution as the global optimal solution, and if the maximum iteration number is not reached, returning to the step S23.
According to one embodiment of the invention, a neighborhood structure comprises:
the exchange structure is used for exchanging the train wagon number allocated to the order i with the train wagon number allocated to the order j in the initial solution matching scheme;
the inserting structure is used for inserting the train wagon number distributed by the order i into the position in front of the train wagon number distributed by the order j in the matching scheme of the initial solution;
and the order exchange structure exchanges the positions of the orders i and j.
According to one embodiment of the invention, the tabu length of the tabu search algorithm is set to 0.6N, where N is the number of candidate solutions; subtracting 1 from the forbidden period of the rest of the forbidden objects in the forbidden table after each time of updating the forbidden table, and if the forbidden period of the forbidden objects is 0, removing the forbidden objects from the forbidden table; solutions that are shifted out of the tabu table enter the candidate solution set.
According to the matching method of the railway transportation data, the steel mill purchasing logistics system is in butt joint with the railway system data, the data are matched to generate the car receiving data, the traditional manual car receiving process is replaced, unmanned automatic car receiving can be achieved, the car receiving accuracy can be effectively improved, the car receiving time is shortened, and the working efficiency is improved.
Drawings
Fig. 1 discloses a flow chart of a method of matching railway transportation data according to an embodiment of the present invention.
Fig. 2 discloses a flowchart of initial construction steps in a matching method of railway transportation data according to an embodiment of the present invention.
Fig. 3 discloses a flowchart of a tabu search algorithm in a matching method of railway transportation data according to an embodiment of the present invention.
Detailed Description
Referring to fig. 1, the present invention provides a method for matching railway transportation data, wherein raw material acquisition data and railway transportation data are matched to generate pickup data, and the matching method comprises:
s1, acquiring raw material purchase data, wherein the raw material purchase data comprises carrier information and origin station information. In one embodiment, the raw material procurement data further comprises: vendor information, raw material information, order information and settlement information, wherein the order information and the settlement information are related to a matching rule, and a candidate set C of the pickup data is determined according to the matching rule. Different matching rules are configured according to different settlement information, such as: for settlement basis of "1" (order number selection basis arrival time), the arrival time of the vehicle at the station is estimated as the time of the last but one station before the vehicle arrives at the station, i.e., arrival information, based on the schedule information. Matching the order with the calculated arrival information, if the arrival time of the train arrives at the station to match the month order number in the month, if the train actually arrives at the station to match the month order number for the next month, then transmitting the train number, the material code, the supplier, the carrier, the departure and the order number to a steel mill transportation management system. If a month appears that the number of orders is 2, the program matches the order number of the month with the order validity period. For settlement basis of '2' or '3' or '4' (order number selection basis delivery time), after 'carrier + start + delivery time + train number + material number' information input is completed, the supplier inputs the system program to automatically match the month order number. If a month appears that the number of orders is 2, the program matches the order number of the month with the valid period of the order. And information such as car numbers, material codes, suppliers, carriers, departure, delivery time, order numbers and the like is sent to a steel mill transportation management system.
S2, acquiring railway transportation data, wherein the railway transportation data also comprises carrier information and origin station information. In one embodiment, the rail transportation data further comprises: the system comprises train wagon number information, waybill information, departure information and schedule information, wherein arrival information is calculated according to the departure information and the schedule information.
And S3, applying a greedy random self-adaptive search algorithm to the raw material purchasing data and the railway transportation data, calculating the vehicle receiving data by taking carrier information and origin station information as constraint conditions, and obtaining a local optimal solution of the vehicle receiving data, wherein the vehicle receiving data comprises supplier information, carrier information, origin station information, arrival station information, train number information, order information and raw material information. In one embodiment, a greedy random adaptive search algorithm (Greedy Randomized Adaptive Search Procedures, GRASP) includes: an initial construction step and an optimized search step.
In the initial construction step, a solution set S and a candidate set C of the vehicle-connected data are constructed, the solution set S and the candidate set C are initialized, items in the candidate set C are selected by a greedy function, whether constraint conditions are met or not is judged, the items meeting the constraint conditions are stored in a limiting candidate list RCL, then one item is randomly selected from the RCL and added into the solution set S, the candidate set C is updated, the above process is circulated until the rest items in the candidate set C do not meet the constraint conditions, and the solution set S obtained in the initial construction step is a local initial solution. Fig. 2 discloses a flowchart of initial construction steps in a matching method of railway transportation data according to an embodiment of the present invention. Referring to fig. 2, the specific implementation procedure of the initial construction step is as follows:
s11, initializing a solution set S, enabling the solution set S to be empty,
s12, initializing a candidate set C.
S13, calculating greedy values of all items e, namely e E C, in the candidate set C by using a greedy function;
s14, judging whether the candidate set C is empty, executing step S19, wherein the candidate set C is not empty, namelyAt this time, step S14 is performed.
S15, respectively taking the minimum value and the maximum value of the calculated greedy value, namely c min ＝ min{c(e)|e∈C},c max ＝max{c(e)|e∈C}。
S16, establishing a limiting candidate list RCL, wherein an item e in the limiting candidate list RCL meets the following constraint conditions: RCL= { e ε C|c (e) ∈c min +α(c max -c min )}。
S17, mechanically selecting an item e from the limiting candidate list RCL, and putting the item into the solution set S.
And S18, updating the candidate set C to be the rest item in the limiting candidate list RCL, and returning to the step S13.
And S19, returning to the solution set S, and ending the construction step.
The greedy function is one of key factors affecting the performance and efficiency of the algorithm, a corresponding greedy function is selected according to a specific practical problem before the algorithm is executed, elements in the candidate set are calculated through the greedy function, and the function value is evaluated to consider whether the corresponding element is put in the candidate list. The greedy greedy function has greedy parameter alpha, the value of the greedy parameter alpha is in the range of 0 to 1, when alpha=0, the greedy greedy function adopts a pure random strategy, and when alpha=1, the greedy function adopts a pure greedy strategy. The greedy parameter α may be a fixed value or may be adjusted during iterative computation.
In the optimizing searching step, a 2-opt algorithm is applied to the local initial solution in the solution set S to perform local searching, and a local optimal solution is obtained. In one embodiment, the number of cars information of the local initial solution in the solution set S is adjusted in the optimizing search step, and the local initial solution is optimized based on the number of cars to obtain a local optimal solution. For example, for the obtained initial partial initial solution, two car numbers are exchanged, the solution after the exchange is recalculated, if the matching accuracy rate after the exchange is smaller than that before the exchange and the used car data is less (the car utilization rate is maximized), a new matching scheme is reserved, otherwise, the original scheme is not changed.
S4, performing local neighborhood search on the local optimal solution of the vehicle receiving data by applying a tabu search algorithm, generating a matching scheme based on the vehicle number information and the order information in the process of local neighborhood search, storing the searched local optimal solution by a tabu table, marking the local optimal solution marked as tabu, releasing the local optimal solution through a tabu criterion, and obtaining the global optimal solution of the vehicle receiving data through the tabu search algorithm. The solution obtained by the greedy random self-adaptive search algorithm is obtained in a random mode, the implementation mode is simple, but the quality of the solution generated randomly is not high, the convergence speed is affected, and the condition that the local is optimal and the global is poor exists, so that the solving precision of the algorithm is reduced. The solution obtained by the greedy random adaptive search algorithm is therefore considered to be a locally optimal solution, but not necessarily a globally superior solution. A Taboo Search (TS) algorithm is an extension of local neighborhood Search, and a taboo table is used to store the searched local optimal solution and mark. In the subsequent searching process, the local optimal point is jumped out in turn. While releasing the good state that part has been tabulated by scofflaw. By adopting the mode, the inferior solution is accepted with a certain probability, so that different paths can be ensured to be adopted for searching, and the local optimum is jumped out, so that the global optimization is realized.
Fig. 3 discloses a flowchart of a tabu search algorithm in a matching method of railway transportation data according to an embodiment of the present invention. As shown in fig. 3, the tabu search algorithm includes:
s21, obtaining a local optimal solution obtained by a greedy random self-adaptive search algorithm, and taking the local optimal solution as a current solution.
S22, initializing a tabu table of a tabu search algorithm, firstly emptying the tabu table, and then storing the local optimum Jie Yi times into the tabu table as a tabu object.
S23, randomly selecting a neighborhood structure for the current solution to generate a neighborhood solution, wherein the neighborhood solution forms a candidate solution set. In one embodiment, the optional neighborhood structure includes: switching fabric, insertion fabric, and order switching fabric. In the exchange structure, the train number allocated to the order i and the train number allocated to the order j in the matching scheme of the initial solution are exchanged. In the inserting structure, in the matching scheme of the initial solution, the train wagon number distributed by the order i is inserted before the position of the train wagon number distributed by the order j. In the order exchange structure, the positions of the orders i and j are exchanged. One of the neighborhood structures is randomly selected for the current solution to generate a neighborhood solution.
S24, for the candidate solution set, judging whether the candidate solution meets the scofflaw or not respectively, if the candidate solution meets the scofflaw, selecting the optimal candidate solution to replace the current solution, replacing the tabu object which enters the tabu table earliest, updating the tabu table, then executing the step S26, and if no candidate solution meets the scofflaw, executing the step S25.
S25, judging the tabu attribute of the candidate solution in the candidate set, selecting the best non-tabu candidate solution from the candidate solution set as the current solution, replacing the tabu object entering the tabu table earliest, updating the tabu table, and executing the step S26.
S26, adding 1 to the iteration number and judging whether the maximum iteration number is reached, if the maximum iteration number is reached, meeting the termination condition, ending the tabu search algorithm, outputting the current solution as the global optimal solution, and if the maximum iteration number is not reached, returning to the step S23.
In the tabu search algorithm, the set tabu length is set to 0.6N, where N is the number of candidate solutions. After each update of the tabu table, the tabu period of the remaining tabu objects in the tabu table is subtracted by 1, and if the tabu period of the tabu objects is 0, the tabu objects are moved out of the tabu table. Solutions that are shifted out of the tabu table enter the candidate solution set.
A specific application example is described below:
table 1 is raw material procurement data.
TABLE 1
Table 2 is railway transportation data.
TABLE 2
Table 3 shows the pick-up data, i.e. the result of the calculation.
TABLE 3 Table 3
According to the matching method of the railway transportation data, the steel mill purchasing logistics system is in butt joint with the railway system data, the data are matched to generate the car receiving data, the traditional manual car receiving process is replaced, unmanned automatic car receiving can be achieved, the car receiving accuracy can be effectively improved, the car receiving time is shortened, and the working efficiency is improved.
Claims (7)
1. A method of matching rail transportation data, wherein raw material procurement data is matched with rail transportation data to produce pickup data, the method comprising:
acquiring raw material purchase data, wherein the raw material purchase data comprises carrier information and origin station information;
acquiring railway transportation data, wherein the railway transportation data also comprises carrier information and origin station information;
applying greedy random self-adaptive search algorithm to raw material purchase data and railway transportation data, calculating vehicle receiving data and obtaining a local optimal solution of the vehicle receiving data by taking carrier information and origin information as constraint conditions, wherein the vehicle receiving data comprises supplier information, carrier information, origin information, arrival information, train number, order information and raw material information;
applying a tabu search algorithm to the local optimal solution of the taxi data to perform local neighborhood search, generating a matching scheme based on the taxi number information and the order information in the process of local neighborhood search, storing the searched local optimal solution by a tabu table, marking the local optimal solution marked as tabu, releasing the tabu criterion, obtaining a global optimal solution of the taxi data by the tabu search algorithm,
the tabu search algorithm includes:
s21, obtaining a local optimal solution obtained by a greedy random self-adaptive search algorithm, and taking the local optimal solution as a current solution;
s22, initializing a tabu table of a tabu search algorithm, firstly emptying the tabu table, and then sequentially storing the local optimal solutions into the tabu table as a tabu object;
s23, randomly selecting a neighborhood structure for the current solution to generate a neighborhood solution, wherein the neighborhood solution forms a candidate solution set, and the neighborhood structure comprises: a switch fabric, an insert fabric, and an order switch fabric; in the exchange structure, the train number allocated to the order i and the train number allocated to the order j in the initial solution matching scheme are exchanged; in the inserting structure, in the matching scheme of the initial solution, the train wagon number distributed by the order i is inserted before the position of the train wagon number distributed by the order j; in the order exchange structure, exchanging the positions of the orders i and j; randomly selecting one of the neighborhood structures for the current solution to generate a neighborhood solution;
s24, for the candidate solution set, judging whether the candidate solution meets the scofflaw or not respectively, if the candidate solution meets the scofflaw, selecting the optimal candidate solution to replace the current solution, replacing the tabu object entering the tabu table earliest, updating the tabu table, then executing the step S26, and if no candidate solution meets the scofflaw, executing the step S25;
s25, judging the tabu attribute of the candidate solution in the candidate set, selecting the optimal non-tabu candidate solution from the candidate solution set as the current solution, replacing the tabu object entering the tabu table earliest, updating the tabu table, and then executing the step S26;
s26, adding 1 to the iteration number and judging whether the maximum iteration number is reached, if the maximum iteration number is reached, meeting the termination condition, ending the tabu search algorithm, outputting the current solution as the global optimal solution, if the maximum iteration number is not reached, returning to the step S23,
the neighborhood structure includes:
the exchange structure is used for exchanging the train wagon number allocated to the order i with the train wagon number allocated to the order j in the initial solution matching scheme;
the inserting structure is used for inserting the train wagon number distributed by the order i into the position in front of the train wagon number distributed by the order j in the matching scheme of the initial solution;
an order exchange structure for exchanging the positions of the orders i, j,
the tabu length of the tabu search algorithm is set to 0.6N, wherein N is the number of candidate solutions;
subtracting 1 from the tabu period of the remaining tabu objects in the tabu table after each update of the tabu table, and if the tabu period of the tabu objects is 0, removing the tabu table;
solutions that are shifted out of the tabu table enter the candidate solution set.
2. The method of matching rail transportation data of claim 1, wherein the raw material procurement data further comprises: vendor information, raw material information, order information and settlement information, wherein the order information and settlement information are related to a matching rule, and a candidate set C of the pickup data is determined according to the matching rule.
3. The method of matching rail transportation data of claim 1, wherein the rail transportation data further comprises: the system comprises train wagon number information, waybill information, departure information and schedule information, wherein arrival information is calculated according to the departure information and the schedule information.
4. The method of matching rail transit data of claim 1, wherein the greedy random adaptive search algorithm comprises:
an initial construction step of constructing a solution set S and a candidate set C of the vehicle-connected data, initializing the solution set S and the candidate set C, selecting items in the candidate set C by a greedy function, judging whether constraint conditions are met, storing the items meeting the constraint conditions into a limiting candidate list RCL, randomly selecting one item from the RCL, adding the selected item into the solution set S, updating the candidate set C, and cycling the above processes until the rest items in the candidate set C do not meet the constraint conditions, wherein the solution set S obtained in the initial construction step is a local initial solution;
and (3) an optimized searching step, namely, locally searching the local initial solutions in the solution set S by applying a 2-opt algorithm to obtain a local optimal solution.
5. The method of matching rail transportation data of claim 4, wherein the initial constructing step comprises:
s11, initializing a solution set S, enabling the solution set S to be empty,
s12, initializing a candidate set C;
s13, calculating greedy values of all items e, namely e E C, in the candidate set C by using a greedy function;
s14, judging whether the candidate set C is empty, executing step S19, wherein the candidate set C is not empty, namelyAt this time, step S15 is performed;
s15, respectively taking the minimum value and the maximum value of the calculated greedy value, namely c min ＝min{c(e)|e∈C},c max ＝max{c(e)|e∈C}；
S16, establishing a limiting candidate list RCL, wherein an item e in the limiting candidate list RCL meets the following constraint conditions: RCL= { e ε C|c (e) ∈c min +α(c max -c min ) - α is a greedy parameter;
s17, randomly selecting an item e from the limiting candidate list RCL, and putting the item into the solution set S;
s18, updating the candidate set C to be the rest item in the limiting candidate list RCL, and returning to the step S13;
and S19, returning to the solution set S, and ending the construction step.
6. The method for matching railway transportation data according to claim 5, wherein the greedy function has greedy parameter α, the greedy parameter α has a value ranging from 0 to 1, the greedy function adopts a pure random strategy when α=0, and the greedy function adopts a pure greedy strategy when α=1.
7. The method for matching railway transportation data according to claim 5, wherein in the optimizing and searching step, the car number information of the local initial solution in the solution set S is adjusted, and the local initial solution is optimized based on the number of cars to obtain the local optimal solution.
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| CN111598324A (en) * | 2020-05-11 | 2020-08-28 | 上海东普信息科技有限公司 | Loading vehicle line optimization method, device, equipment and storage medium |
| CN114091717A (en) * | 2021-01-11 | 2022-02-25 | 北京京东振世信息技术有限公司 | Site selection method and device |
| CN113487264B (en) * | 2021-07-20 | 2022-09-02 | 中南大学 | Logistics distribution method and system based on heterogeneous multi-unmanned aerial vehicles |
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