Container truck reservation scheduling method based on class arrival and departure time window

Abstract

The invention discloses a container truck appointment scheduling method based on a train arrival and departure time window, and the method comprises the steps: constructing a single path set S based on a current station, setting a preset complete time window of a path as T, setting an actual complete time window of the path as T ', obtaining a complete time window difference value D on the path, and obtaining a complete time window difference value D of the path; obtaining a preset time window threshold value Y of the path and a corresponding original band domain, constructing gene chains based on respective time sequences, judging Yi by Y, and adjusting a scheduling plan so as to realize integral scheduling; by monitoring the whole running route of the train and calculating and adjusting the matching time of the train and the container truck, the time pressure and the space pressure of the current station are reduced, the overall matching, appointment and scheduling difficulty is reduced, when non-punctual running occurs, the scheduling plan is revised by adjusting the container truck appointment time in advance, and the scheduling efficiency is improved. The scheduling cost is reduced, and the operation efficiency of the station is also improved.

Description

Technical Field

[0001] The present invention relates to the technical field of train scheduling, and in particular to a method for scheduling container truck reservations based on a train arrival and departure time window. Background Art

[0002] Block trains are a common mode of bulk cargo transportation by land. After the block train arrives, the bulk cargo needs to be transferred and diverted by container trucks. At the same time, since the block trains that stop at a station are not unique, and the transferred container trucks cannot wait for a long time in the station and the operating space in the station is limited, effective scheduling is needed to enable the container trucks and block trains to be effectively scheduled and coordinated, so as to ensure that the stay time of the goods and container trucks at the station is within the turnover range.

[0003] The existing dispatching scheme is to make time reservations through multiple parties, and then the vehicle waits outside the station until the train arrives, enters the station at about the same time as the train, and finally transfers the goods. However, this method is very likely to waste resources and affect the operation order due to the unpunctuality of one party. Since the current station is not the only station where the train stops, the time points are dense and oppressive, which greatly increases the pressure on the current station reservation and dispatching.

[0004] Therefore, it is necessary to design a truck reservation scheduling method based on the arrival and departure time window of trains. Summary of the invention

[0005] The object of the present invention is to provide a method for truck reservation and scheduling based on the arrival and departure time windows of trains, so as to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A method for truck reservation and scheduling based on the arrival and departure time window of a train includes the following steps:

[0008] S100, construct a single path set S based on the current station, that is, a set of several sub-segments of the interval from the origin station to the current station, each sub-segment is composed of two adjacent stations, which are s1, s2, ..., s i ;

[0009] S200: The preset complete time window for constructing the path is T, and the preset time used for the sub-segments from the starting station to the current station is set to t1, t2, ..., t i , that is, T = t1 + t2 + ... + t i ;

[0010] S300, assuming that the actual complete time window of the path is T', and the actual time used for each sub-segment is t'1, t'2, ..., t' i, that is, T′=t′1+t′2+…+t′ i ;

[0011] S400, obtaining a complete time window difference |D| on the path by comparing a preset complete time window T with an actual complete time window T′ of the path;

[0012] S500, performing dual-value recovery on the time window difference |D|, and then performing dual-value calculation based on the time point, thereby obtaining a preset time window threshold |Y| of the path and a corresponding original band domain;

[0013] S600, constructing gene points through all trains of the station, constructing independent chains through all paths of the station, and then matching the gene points with the chains to form gene chains with their own time series;

[0014] S700. Based on S100 to S500, it can be seen that each gene point has an independent time window threshold during operation, which is |Y1|, |Y2|, ..., |Y i |, through |Y| and the close band and distant band formed by it and |Y i | Make judgments and adjust the scheduling plan;

[0015] S800, the number of chains accommodated by the current site at the same time is allocated based on the time domain of the gene point expansion, so as to achieve overall scheduling.

[0016] According to the above technical solution, the sub-sections of S100 are divided based on the stops of the trains, that is, on the same route, different trains stop at different adjacent stops, and the sub-sections divided therein are different.

[0017] According to the above technical solution, the time t used by the preset sub-section in S200 is i It is the departure time interval between two adjacent stations, which includes the time period from leaving the previous station to arriving at the current station, and the time period from arriving at the current station to leaving the current station.

[0018] According to the above technical solution, the actual sub-section time t′ of S300 is i The change in is the time period from leaving the previous station to arriving at the current station, which is affected by the current road conditions.

[0019] According to the above technical solution, it can be known from the calculations of S200 and S300 in S400 that the complete time window difference |D| is composed of sub-differences of several sub-segments, that is, |D|=|d1+d2+…+d i |, while d i =(t i -t′ i), and obtain the time variation amplitude value of a single sub-section |d i |, right |d i |Perform data statistics and construct |d based on the distribution model i | to extend the distribution, and at the same time compare the influencing factors and the corresponding |d i |Perform matching association to form a sub-segment database.

[0020] According to the above technical solution, the calculation of the time window difference |D| and the time window threshold |Y| in S500 is as follows:

[0021] Through dual-value recovery, two values ​​D and -D can be obtained from |D|. At the same time, calculation is performed through a predetermined time point to obtain a Y of |2D|, so that the value range of |Y| can be determined to be (0~|2D|). The original band is formed based on the value range of |Y| and the preset time point. Since the value range of |Y| is known, the band is divided again. The original band (0~|D|) on both sides of the time point is set as the close band, and the original band (|D|~|2D|) on both sides of the time point is set as the distant band.

[0022] According to the above technical solution, the specific operation of S600 is:

[0023] Each train is set as a single gene point, so each gene point has a preset complete time window T n , the preset time used by the sub-section t n , the actual complete time window T′ n , the actual time t′ of the sub-section n , full time window difference |D n |with time window threshold|Y n |, n = 1, 2, ..., i;

[0024] There are several paths that the current station passes through, and each path will run several trains based on time sequence, that is, any path can arrange the gene points based on time sequence to form a gene chain based on time series, and each train corresponds to a gene point one by one.

[0025] According to the above technical solution, the specific determination operation of S700 is:

[0026] The gene point has its own preset time window threshold |Y|. When the gene point is re-run, steps S100 to S500 are repeated to generate a new time window threshold |Y|. n |, and the corresponding new band domain;

[0027] Compare |Y| with the original band domain generated at the expected time point and |Y n|Compare the overlap with the new band generated at the expected time point, that is, judge the position of the extreme value of the new band in the original band. When it is located in the close band, it is the timely operation of the gene point, and when it is located in the distant band, it is the untimely operation of the gene point;

[0028] For gene points that do not run on time, the arrival time needs to be recalculated, that is, the arrival time is preset by the median of the new band, and then the calculation steps S100 to S500 are repeated, and they are marked as floating gene points. At the same time, the corresponding original gene points are hidden to form a pseudo-on-time operation state.

[0029] According to the above technical solution, the scheduling method of S800 is:

[0030] S801. Based on the on-time operation and pseudo-on-time operation of the gene points, the preset arrival time points are coordinated and a time distribution model is formed;

[0031] S802, assigning an original band domain to each gene point, and extending each band domain based on a time distribution model;

[0032] S803, integrating and combining based on the number of simultaneous accommodating stations of the current station and all the paths passing through the current station to form a combination set;

[0033] S804, separating the overlapping gene points based on the extension result formed by the time distribution model of the band obtained in S802, and forming a corresponding number of clusters based on the number of sites that can be accommodated at the same time;

[0034] S805: The trains in the cluster need to be combined, extracted and matched from the combination set, so as to achieve effective deployment of the train stops;

[0035] S806: Based on the results of the train stop deployment, the transfer container trucks are dispatched as a whole. The algorithm used is the Kalman filter algorithm, which calculates both to obtain the time value T of the train. b The time value T of the collection card j ;

[0036] S807, by the time value T of the train b The time value T of the collection card j For comparison, T b / T j =N, when N<0.85, the train is late; when 0.85≤N≤1.15, the train and the truck are in time; when 1.15≤N, the truck is late;

[0037] S808. Effectively dispatch container trucks based on the timing matching between container trucks and trains.

[0038] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0039] By monitoring the entire running route of the train, the matching time between the train and the container truck is calculated and adjusted, thereby reducing the time and space pressure of the current station, making the overall matching scheduling difficulty lowered; and when non-punctual operation occurs, the scheduling plan is adjusted to reduce the operating pressure of the current station, thereby reducing the scheduling cost and improving the operation efficiency of the station. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, the present invention provides a technical solution: a method for booking and scheduling container trucks based on the arrival and departure time window of a train, comprising the following steps:

[0043] S100, construct a single path set S based on the current station, that is, a set of several sub-segments of the interval from the origin station to the current station, each sub-segment is composed of two adjacent stations, which are s1, s2, ..., s i ;

[0044] S200: The preset complete time window for constructing the path is T, and the preset time used for the sub-segments from the starting station to the current station is set to t1, t2, ..., t i , that is, T = t1 + t2 + ... + t i ;

[0045] S300, assuming that the actual complete time window of the path is T', and the actual time used for each sub-segment is t'1, t'2, ..., t' i , that is, T′=t′1+t′2+…+t′ i ;

[0046] S400, obtaining a complete time window difference |D| on the path by comparing a preset complete time window T with an actual complete time window T′ of the path;

[0047] S500, performing dual-value recovery on the time window difference |D|, and then performing dual-value calculation based on the time point, thereby obtaining a preset time window threshold |Y| of the path and a corresponding original band domain;

[0048] S600, constructing gene points through all trains of the station, constructing independent chains through all paths of the station, and then matching the gene points with the chains to form gene chains with their own time series;

[0049] S700. Based on S100 to S500, it can be seen that each gene point has an independent time window threshold during operation, which is |Y1|, |Y2|, ..., |Y i |, through |Y| and the close band and distant band formed by it and |Y i | Make judgments and adjust the scheduling plan;

[0050] S800, the number of chains accommodated by the current site at the same time is allocated based on the time domain of the gene point expansion, so as to achieve overall scheduling.

[0051] Specifically, the sub-sections of S100 are divided based on the stops of the trains, that is, on the same route, different trains stop at different adjacent stops, and the sub-sections divided therein are different.

[0052] Specifically, the time t used by the preset sub-section of S200 is i It is the departure time interval between two adjacent stations, which includes the time period from leaving the previous station to arriving at the current station, and the time period from arriving at the current station to leaving the current station.

[0053] Specifically, the actual sub-section time t′ of S300 is i The change in is the time period from leaving the previous station to arriving at the current station, which is affected by the current road conditions.

[0054] Specifically, it can be known from the calculations of S200 and S300 in S400 that the complete time window difference |D| is composed of sub-differences of several sub-segments, that is, |D|=|d1+d2+…+d i |, while d i =(t i -t′ i ), and obtain the time variation amplitude value of a single sub-section |d i |, right |d i |Perform data statistics and construct |d based on the distribution model i | to extend the distribution, and at the same time compare the influencing factors and the corresponding |d i |Perform matching association to form a sub-segment database.

[0055] Specifically, the calculation of the time window difference |D| and the time window threshold |Y| in S500 is as follows:

[0056] Through dual-value recovery, two values ​​D and -D can be obtained from |D|. At the same time, calculation is performed through a predetermined time point to obtain a Y of |2D|, so that the value range of |Y| can be determined to be (0~|2D|). The original band is formed based on the value range of |Y| and the preset time point. Since the value range of |Y| is known, the band is divided again. The original band (0~|D|) on both sides of the time point is set as the close band, and the original band (|D|~|2D|) on both sides of the time point is set as the distant band.

[0057] Specifically, the specific operations of S600 are:

[0058] Each train is set as a single gene point, so each gene point has a preset complete time window T n , the preset time used by the sub-section t n , the actual complete time window T′ n , the actual time t′ of the sub-section n , full time window difference |D n |with time window threshold|Y n |, n = 1, 2, ..., i;

[0059] There are several paths that the current station passes through, and each path will run several trains based on time sequence, that is, any path can arrange the gene points based on time sequence to form a gene chain based on time series, and each train corresponds to a gene point one by one.

[0060] Specifically, the specific determination operation of S700 is:

[0061] The gene point has its own preset time window threshold |Y|. When the gene point is re-run, steps S100 to S500 are repeated to generate a new time window threshold |Y|. n |, and the corresponding new band domain;

[0062] Compare |Y| with the original band domain generated at the expected time point and |Y n |Compare the overlap with the new band generated at the expected time point, that is, judge the position of the extreme value of the new band in the original band. When it is located in the close band, it is the timely operation of the gene point, and when it is located in the distant band, it is the untimely operation of the gene point;

[0063] For gene points that do not run on time, the arrival time needs to be recalculated, that is, the arrival time is preset by the median of the new band, and then the calculation steps S100 to S500 are repeated, and they are marked as floating gene points. At the same time, the corresponding original gene points are hidden to form a pseudo-on-time operation state.

[0064] Specifically, the scheduling method of S800 is:

[0065] S801. Based on the on-time operation and pseudo-on-time operation of the gene points, the preset arrival time points are coordinated and a time distribution model is formed;

[0066] S802, assigning an original band domain to each gene point, and extending each band domain based on a time distribution model;

[0067] S803, integrating and combining based on the number of simultaneous accommodating stations of the current station and all the paths passing through the current station to form a combination set;

[0068] S804, separating the overlapping gene points based on the extension result formed by the time distribution model of the band obtained in S802, and forming a corresponding number of clusters based on the number of sites that can be accommodated at the same time;

[0069] S805: The trains in the cluster need to be combined, extracted and matched from the combination set, so as to achieve effective deployment of the train stops;

[0070] S806: Based on the results of the train stop deployment, the transfer container trucks are dispatched as a whole. The algorithm used is the Kalman filter algorithm, which calculates both to obtain the time value T of the train. b The time value T of the collection card j ;

[0071] S807, by the time value T of the train b The time value T of the collection card j For comparison, T b / T j =N, when N<0.85, the train is late; when 0.85≤N≤1.15, the train and the truck are in time; when 1.15≤N, the truck is late;

[0072] S808. Effectively dispatch container trucks based on the timing matching between container trucks and trains.

[0073] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0074] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for booking and scheduling container trucks based on the arrival and departure time window of a train, characterized in that: The steps include: S100, construct a single path set S based on the current station, that is, a set of several sub-segments of the interval from the origin station to the current station, each sub-segment is composed of two adjacent stations, which are s1, s2, ..., s i ; S200, the preset complete time window for constructing the path is T, and the preset time used for the sub-segments from the starting station to the current station is set to t1, t2, ..., t i , that is, T = t1 + t2 + ... + t i ; S300: Assume that the actual complete time window of the path is T′, and the actual time used for each sub-segment is t′1, t′2, ..., t′ i , that is, T′=t′1+t′2+...+t′ i ; S400, obtaining a complete time window difference |D| on the path by comparing a preset complete time window T with an actual complete time window T′ of the path; S500, performing dual-value recovery on the time window difference |D|, and then performing dual-value calculation based on the time point, thereby obtaining a preset time window threshold |Y| of the path and a corresponding original band domain; S600, constructing gene points through all trains of the station, constructing independent chains through all paths of the station, and then matching the gene points with the chains to form gene chains with their own time series; S700. Based on S100 to S500, it can be seen that each gene point has an independent time window threshold during operation, which is |Y1|, |Y2|, ..., |Y i |, through |Y| and the close band and distant band formed by it and |Y i | Make judgments and adjust the scheduling plan; S800. The number of chains accommodated at the current station is allocated in a coordinated manner based on the time domain of the gene point expansion, thereby realizing the coordinated scheduling of container trucks and trains.

2. According to claim 1, a method for truck reservation and scheduling based on the arrival and departure time window of a train is characterized in that: The sub-sections of S100 are divided based on the stops of the trains, that is, on the same route, different trains stop at different adjacent stops, and the sub-sections divided therein are different.

3. The method for truck reservation and scheduling based on the arrival and departure time window of a train according to claim 1 is characterized in that: The time t used for the preset sub-section in S200 i It is the departure time interval between two adjacent stations, which includes the time period from leaving the previous station to arriving at the current station, and the time period from arriving at the current station to leaving the current station.

4. The method for truck reservation and scheduling based on the arrival and departure time window of a train according to claim 1 is characterized in that: The actual sub-section time t′ of S300 i The change in is the time period from leaving the previous station to arriving at the current station, which is affected by the current road conditions.

5. The method for truck reservation and scheduling based on the arrival and departure time window of a train according to claim 1 is characterized in that: It can be known from the calculations of S200 and S300 in S400 that the complete time window difference |D| is composed of sub-differences of several sub-sections, that is, |D|=|d1+d2+...+d i |, while d i =(t i -t′ i ), and obtain the time variation amplitude value of a single sub-section |d i |, right |d i |Perform data statistics and construct |d based on the distribution model i | to extend the distribution, and at the same time compare the influencing factors and the corresponding |d i |Perform matching association to form a sub-segment database.

6. The method for truck reservation and scheduling based on the arrival and departure time window of a train according to claim 1 is characterized in that: The calculation of the time window difference |D| and the time window threshold |Y| in S500 is as follows: Through dual-value recovery, two values ​​D and -D can be obtained from |D|. At the same time, calculation is performed through a predetermined time point to obtain a Y of |2D|, so that the value range of |Y| can be determined to be (0~|2D|). The original band is formed based on the value range of |Y| and the preset time point. Since the value range of |Y| is known, the band is divided again. The original band (0~|D|) on both sides of the time point is set as the close band, and the original band (|D|~|2D|) on both sides of the time point is set as the distant band.

7. The method for truck reservation and scheduling based on the arrival and departure time window of a train according to claim 1 is characterized in that: The specific operations of S600 are: Each train is set as a single gene point, so each gene point has a preset complete time window T n , the preset time used by the sub-section t n , the actual complete time window T′ n , the actual time t′ of the sub-section n , full time window difference |D n |with time window threshold|Y n |, n = 1, 2, ..., i; There are several paths that the current station passes through, and each path will run several trains based on time sequence, that is, any path can arrange the gene points based on time sequence to form a gene chain based on time series, and each train corresponds to a gene point one by one.

8. The method for truck reservation and scheduling based on the arrival and departure time window of a train according to claim 7 is characterized in that: The specific determination operation of S700 is: The gene point has its own preset time window threshold |Y|. When the gene point is re-run, steps S100 to S500 are repeated to generate a new time window threshold |Y|. n |, and the corresponding new band domain; Compare |Y| with the original band domain generated at the expected time point and |Y n |Compare the overlap with the new band generated at the expected time point, that is, judge the position of the extreme value of the new band in the original band. When it is located in the close band, it is the timely operation of the gene point, and when it is located in the distant band, it is the untimely operation of the gene point; For gene points that do not run on time, the arrival time needs to be recalculated, that is, the arrival time is preset by the median of the new band, and then the calculation steps S100 to S500 are repeated, and they are marked as floating gene points. At the same time, the corresponding original gene points are hidden to form a pseudo-on-time operation state.

9. A method for truck reservation and scheduling based on the arrival and departure time window of a train according to claim 8, characterized in that: The scheduling method of S800 is: S801. Based on the on-time operation and pseudo-on-time operation of the gene points, the preset arrival time points are coordinated and a time distribution model is formed; S802, assigning an original band domain to each gene point, and extending each band domain based on a time distribution model; S803, integrating and combining based on the number of simultaneous accommodating stations of the current station and all the paths passing through the current station to form a combination set; S804, separating the overlapping gene points based on the extension result formed by the time distribution model of the band obtained in S802, and forming a corresponding number of clusters based on the number of sites that can be accommodated at the same time; S805: The trains in the cluster need to be combined, extracted and matched from the combination set, so as to achieve effective deployment of the train stops; S806: Based on the results of the train stop deployment, the transfer container trucks are dispatched as a whole. The algorithm used is the Kalman filter algorithm, which calculates both to obtain the time value T of the train. b The time value T of the collection card i ; S807, by the time value T of the train b The time value T of the collection card j For comparison, T b / T j =N, when N<0.85, the train is late; when 0.85≤N≤1.15, the train and the truck are in time; when 1.15≤N, the truck is late; S808. Effectively dispatch container trucks based on the timing matching between container trucks and trains.

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