CN113326987A - Railway central station track type container crane collaborative optimization scheduling method - Google Patents

Railway central station track type container crane collaborative optimization scheduling method Download PDF

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CN113326987A
CN113326987A CN202110616487.6A CN202110616487A CN113326987A CN 113326987 A CN113326987 A CN 113326987A CN 202110616487 A CN202110616487 A CN 202110616487A CN 113326987 A CN113326987 A CN 113326987A
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周勇
张�杰
付正康
李文锋
李卫东
曹小华
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Wuhan University of Technology WUT
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Abstract

The invention relates to a collaborative optimization scheduling method for a rail type container crane of a railway central station, which comprises the following steps: acquiring position information of a container in a railway central station and operation parameters of a track crane; constructing a track crane dispatching model according to the position information and the operation parameters; and performing initial coding on the unloading operation task sequence of the track crane by adopting an integer sequence coding mode, searching and solving the track crane scheduling model, outputting an optimal task sequence, and performing task allocation on each track crane according to the optimal task sequence. The invention utilizes the searching solution after modeling to improve the optimizing capability, effectively improve the operation balance rate and the operation efficiency of the railway station, reduce the long-distance movement of the track crane, reduce the energy consumption and improve the dynamic configuration scheduling capability of the track crane of the central station.

Description

Railway central station track type container crane collaborative optimization scheduling method
Technical Field
The invention relates to the technical field of cooperative optimization scheduling of loading and unloading equipment of a railway central station, in particular to a cooperative optimization scheduling method of a railway central station track type container crane.
Background
A railway container central station (hereinafter, simply referred to as a "central station") is an important node of an integrated transportation system, and the improvement of the operation efficiency thereof is receiving attention from managers. At present, the scheduling optimization aiming at the multi-track crane limits the operation area of the track crane to avoid the occurrence of interference, but the mode is not beneficial to improving the utilization rate and flexibility of the track crane, and flexible scheduling is carried out, namely the operation range of the track crane is not limited, and an efficient task allocation strategy and an interference avoidance method are all of great importance. In conclusion, how to provide an efficient and timely method for dispatching a track crane is an urgent problem to be solved.
Disclosure of Invention
In view of the above, a need exists for a method for collaborative optimal scheduling of a rail container crane at a railway center station, which is used for solving the problem in the prior art that task allocation of a rail crane is not efficient and timely.
The invention provides a collaborative optimization scheduling method for a rail type container crane of a railway central station, which comprises the following steps:
acquiring position information and operation parameters of a container in a railway central station;
constructing a track crane dispatching model according to the position information and the operation parameters;
and performing initial coding on the unloading operation task sequence of the track crane by adopting an integer sequence coding mode, searching and solving the track crane scheduling model, outputting an optimal task sequence, and performing task allocation on each track crane according to the optimal task sequence.
Further, the track crane scheduling model comprises an objective function and constraint conditions, wherein the objective function is used for representing the minimum job completion time, and the constraint conditions comprise:
the first constraint condition is used for constraining any task of the track crane to have only one corresponding task immediately before;
the second constraint condition is used for constraining any task of the track crane to have only one corresponding tightened task;
the third constraint condition is used for constraining the relation between the ending time and the starting time of the box unloading operation task corresponding to the track crane;
the fourth constraint condition is used for constraining the time connection of two continuous unloading operation tasks of the track crane;
and the fifth constraint condition is used for constraining the total moving distance of the cart corresponding to the track crane to complete all the unloading operation tasks.
Further, the objective function is represented by the following formula:
Figure BDA0003096596370000021
wherein, G represents a track crane set, and G belongs to G ═ 1, 2 and 3; ig represents a set of unpacking job tasks, and Ig belongs to {1, 2, …, Ig }; fgiShowing the end time of the ith unloading task of the g-th track crane; min f represents the minimum job completion time.
Further, the first constraint, the second constraint, the third constraint, the fourth constraint and the fifth constraint are expressed by the following formulas:
Figure BDA0003096596370000022
Figure BDA0003096596370000023
Figure BDA0003096596370000024
Figure BDA0003096596370000025
Figure BDA0003096596370000026
wherein the content of the first and second substances,
Figure BDA0003096596370000027
representing a variable of 0 to 1, if the ith unloading task and the jth unloading task are operated by the gth rail crane,
Figure BDA0003096596370000028
if not, then,
Figure BDA0003096596370000029
Sgishowing the starting time of the ith unloading task of the g-th track crane; dgiThe horizontal moving distance of the g-th rail crane during the ith unloading task is shown;
Figure BDA0003096596370000031
the vertical movement distance of the g-th rail crane during the ith unloading task is shown; v1Representing the moving speed of the g-th track crane; v2Representing the moving speed of the trolley of the g-th track crane;
Figure BDA0003096596370000032
the empty driving distance of the g-th track crane is represented when the i-th box unloading task and the j-th box unloading task are continuously executed; dgAnd the total moving distance of the g-th rail crane for completing all box unloading operation tasks is shown.
Further, the initial coding of the unloading task sequence of the track crane by using an integer sequence coding mode comprises:
expressing the box unloading task of the track crane into a genetic algorithm gene form by adopting an integer sequence coding mode;
and determining the coding length of the box unloading operation task of the track crane according to the number of the operation boxes contained in the box unloading operation task.
Further, the searching and solving the track crane scheduling model and outputting the optimal task sequence includes:
initializing sparrow population algorithm parameters, randomly generating an initial task sequence of a box unloading operation task of the rail crane in a search space, and forming an initial population according to the initial task sequence;
performing task allocation according to the initial population, and determining a sequence and a fitness value corresponding to each sparrow;
according to the updating formulas of discoverers, joiners and early-warning persons in the sparrow population, the sequences corresponding to all sparrows are updated for multiple times, and the fitness value and the corresponding sequence of each sparrow after the multiple updating are determined;
judging whether preset conditions are met or not according to the fitness value of each sparrow, if so, returning to the step of updating the sequences corresponding to all sparrows for multiple times according to the updating formulas of discoverers, joiners and early-warning persons in the sparrow population;
and if not, the corresponding particles are early sparrows, differential processing is carried out on the early sparrows, the sequences corresponding to the early sparrows are updated and replaced until iteration termination conditions are met, and the final optimal task sequence is output.
Further, the updating the sequences corresponding to all sparrows for multiple times according to the updating formulas of the discoverers, the joiners and the early-warning persons in the sparrow population comprises:
selecting Pnum sparrows as discoverers and other sparrows as addicts, updating sequences corresponding to all sparrows according to the sequences corresponding to the discoverers, and carrying out legalization treatment, wherein Pnum is a preset integer, the legalization treatment is ascending sequencing according to the size of each value of the sequences, assigning sequencing serial numbers of each value of an original sequence to original positions, and for the sparrows with the same value in the original sequence, the sparrows are arranged in the original sequence first;
updating the sequences corresponding to all sparrows again according to the updated sequences of the joiners, and carrying out legalization processing;
and selecting Dnum sparrows as early warning persons, updating sequences corresponding to all the sparrows again according to the sequences corresponding to the early warning persons, and carrying out legalization processing, wherein Dnum is a preset integer.
Further, the preset conditions include: the number of sparrows with similar fitness values in the population is larger than the preset number.
Further, if the current state does not satisfy the iteration termination condition, the corresponding particle is a premature sparrow, the premature sparrow is subjected to differential processing, a sequence corresponding to the premature sparrow is updated and replaced, and the outputting of the final optimal task sequence includes:
sequentially carrying out variation processing, cross processing and legalization processing on the sequences corresponding to the early-maturing sparrows, and selecting individuals with fitness values reaching preset values as returned cross individuals by adopting a greedy strategy;
performing task allocation again, and determining the fitness value and the corresponding sequence of each sparrow after updating;
and if the iteration times meet the termination condition, outputting the optimal task sequence, otherwise returning to the step of updating the sequences corresponding to all the sparrows for multiple times according to the updating formulas of the discoverers, the joiners and the early-warning persons in the sparrow population.
Further, the task allocation for each track crane according to the optimal task sequence includes:
distributing the unloading operation tasks according to the optimal task sequence;
distributing the box unloading operation tasks to corresponding track cranes according to the relative relation between the initial positions of the box unloading operation tasks and the positions of the earliest available time of the track cranes, and performing interference judgment;
if the interference exists, the sequence of the box unloading operation tasks is reset, and if the interference does not exist, the next box unloading operation task is distributed until all the box unloading operation tasks are distributed;
wherein the interference determination comprises: and after distributing the unloading operation task to the corresponding track crane, judging whether the relative relation between the distance between the track crane and the adjacent track crane at the corresponding moment and the safety distance between the two preset tracks meets a preset interference condition, if so, judging that no interference exists, and if not, judging that the interference exists.
Compared with the prior art, the invention has the beneficial effects that: firstly, effectively obtaining modeling parameters; then, according to the modeling parameters, effectively constructing a track crane scheduling model; and finally, searching and solving are carried out by utilizing the track crane scheduling model, effective optimization is carried out, an optimal task sequence is determined, and task allocation is carried out on the track crane according to the optimal task sequence. In conclusion, the invention utilizes the searching and solving after modeling to improve the optimizing capability, effectively improve the operation balance rate and the operation efficiency of the railway station, reduce the long-distance movement of the track crane, reduce the energy consumption and improve the dynamic configuration and dispatching capability of the track crane of the central station.
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FIG. 1 is a schematic flow chart of an embodiment of a coordinated optimization scheduling method for a rail container crane at a railway center station according to the present invention;
FIG. 2 is a first flowchart illustrating an embodiment of step S3 in FIG. 1 according to the present invention;
FIG. 3 is a flowchart illustrating an embodiment of step S33 in FIG. 2 according to the present invention;
FIG. 4 is a flowchart illustrating an embodiment of step S35 in FIG. 2 according to the present invention;
FIG. 5 is a second flowchart illustrating the step S3 in FIG. 2 according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an embodiment of a specific application scenario of the track crane according to the present invention;
FIG. 7 is a diagram illustrating a comparison between an initial task sequence and an optimal task sequence according to an embodiment of the present invention;
FIG. 8 is a comparison diagram of an embodiment of the search time provided by the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
The embodiment of the invention provides a collaborative optimization scheduling method for a railway center station rail-mounted container crane, and when being seen in combination with fig. 1, fig. 1 is a schematic flow chart of an embodiment of the collaborative optimization scheduling method for the railway center station rail-mounted container crane provided by the invention, and the method comprises steps S1 to S3, wherein:
in step S1, acquiring location information of the container at the railway central station and operation parameters of the track crane, wherein the operation parameters include operation parameters of the track crane;
in step S2, a gantry crane scheduling model is constructed according to the position information and the operation parameters;
in step S3, an integer sequence coding method is adopted to initially code the task sequence of the box unloading operation of the track crane, search and solve the track crane scheduling model, output an optimal task sequence, and perform task allocation on each track crane according to the optimal task sequence.
In the embodiment of the invention, firstly, modeling parameters are effectively obtained; then, according to the modeling parameters, effectively constructing a track crane scheduling model; and finally, searching and solving are carried out by utilizing the track crane scheduling model, effective optimization is carried out, an optimal task sequence is determined, and task allocation is carried out on the track crane according to the optimal task sequence.
It should be noted that the invention sets the track crane as the dispatching object, combines the position of the container in the railway central station and the multi-aspect factors of the track crane running parameters, and takes the minimum time consumed for completing the loading and unloading of all the containers on a train as the dispatching object of the track crane to construct the track crane dispatching model. The gantry scheduling is a non-linear mixed integer programming problem and includes multiple TSP problems, which are therefore NP-hard problems.
In a preferred embodiment, the track crane scheduling model includes an objective function and constraints, the objective function is used for representing minimum job completion time, and the constraints include:
the first constraint condition is used for constraining any task of the track crane to have only one corresponding task immediately before;
the second constraint condition is used for constraining any task of the track crane to have only one corresponding tightened task;
the third constraint condition is used for constraining the relation between the ending time and the starting time of the box unloading operation task corresponding to the track crane;
the fourth constraint condition is used for constraining the time connection of two continuous unloading operation tasks of the track crane;
and the fifth constraint condition is used for constraining the total moving distance of the cart corresponding to the track crane to complete all the unloading operation tasks.
As a specific embodiment, the embodiment of the invention establishes an effective objective function and constraint conditions to carry out modeling of a corresponding scene.
As a preferred embodiment, the objective function is represented by the following formula:
Figure BDA0003096596370000071
wherein, G represents a track crane set, and G belongs to G ═ 1, 2 and 3; ig represents a set of unpacking job tasks, and Ig belongs to {1, 2, …, Ig }; fgiShowing the end time of the ith unloading task of the g-th track crane; min f represents the minimum job completion time.
As a specific embodiment, the embodiment of the present invention sets an objective function, which is an objective function of an inner layer model, that is, the operation completion time is the minimum.
As a preferred embodiment, the first constraint, the second constraint, the third constraint, the fourth constraint and the fifth constraint are expressed by the following formulas:
Figure BDA0003096596370000072
Figure BDA0003096596370000073
Figure BDA0003096596370000074
Figure BDA0003096596370000075
Figure BDA0003096596370000076
wherein the content of the first and second substances,
Figure BDA0003096596370000077
representing a variable of 0 to 1, if the ith unloading task and the jth unloading task are operated by the gth rail crane,
Figure BDA0003096596370000081
if not, then,
Figure BDA0003096596370000082
Sgishowing the starting time of the ith unloading task of the g-th track crane; dgiThe horizontal moving distance of the g-th rail crane during the ith unloading task is shown;
Figure BDA0003096596370000083
the vertical movement distance of the g-th rail crane during the ith unloading task is shown; v1Representing the moving speed of the g-th track crane; v2Representing the moving speed of the trolley of the g-th track crane;
Figure BDA0003096596370000084
the empty driving distance of the g-th track crane is represented when the i-th box unloading task and the j-th box unloading task are continuously executed; dgAnd the total moving distance of the g-th rail crane for completing all box unloading operation tasks is shown.
As a specific embodiment, the embodiment of the present invention sets up multiple constraint conditions to ensure effective constraints of multiple conditions, where a first constraint condition ensures that any task of a track crane only has one immediately preceding task, a second constraint condition ensures that any task of the track crane only has one immediately following task, and a third constraint condition is a relationship between an end time and a start time of a track crane operation task; the fourth constraint condition is time connection constraint of two continuous tasks of the track crane operation; and the fifth constraint condition rail crane cart completes the total moving distance of all the tasks borne by the rail crane cart.
As a preferred embodiment, referring to fig. 2, fig. 2 is a first flowchart of an embodiment of step S3 in fig. 1 provided by the present invention, and step S3 includes steps S31 to S35, where:
in step S31, initializing sparrow population algorithm parameters, randomly generating an initial task sequence of the box unloading task of the gantry crane in a search space, and forming an initial population according to the initial task sequence;
in step S32, performing task allocation according to the initial population, and determining a sequence and a fitness value corresponding to each sparrow;
in step S33, according to the update formulas of the discoverer, the joiner, and the early-warning person in the sparrow population, the sequences corresponding to all sparrows are updated many times, and the fitness value and the sequence corresponding to each sparrow after updating many times are determined;
in step S34, if the fitness value of each sparrow meets the preset condition, returning to the step of updating the sequences corresponding to all sparrows multiple times according to the update formulas of the discoverer, the joiner and the early-warning person in the sparrow population (i.e., step S33);
in step S35, if not, the corresponding particle is a premature sparrow, the premature sparrow is differentially processed, the sequence corresponding to the premature sparrow is updated and replaced until an iteration termination condition is satisfied, and the final optimal task sequence is output.
As a specific embodiment, the embodiment of the invention combines a sparrow search algorithm and a differential evolution algorithm, selects the sparrow search algorithm to generate an optimized population, applies a premature similar theory to promote the population to keep sustainable evolution, applies a differential evolution operator to premature, can effectively expand a routing space, avoids premature trapping in a local optimal solution to a certain extent, and provides data support for solving the problems of dynamic configuration and scheduling of a gantry crane by the sparrow search algorithm.
As a preferred embodiment, referring to fig. 3, fig. 3 is a schematic flowchart of an embodiment of step S33 in fig. 2 provided by the present invention, and step S33 includes steps S331 to S333, where:
in step S331, Pnum sparrows are selected as discoverers, other sparrows are added, and sequences corresponding to all sparrows are updated according to the sequences corresponding to the discoverers, and a legalization process is performed, where Pnum is a preset integer, where the legalization process is an ascending sort according to the size of each value of the sequence, and a sort number of each value of an original sequence is assigned to an original position, and for a sparrow with the same value in the original sequence, the former is ranked first in the original sequence;
in step S332, according to the updated sequence of the subscriber, updating the sequences corresponding to all sparrows again, and performing a legalization process;
in step S333, Dnum sparrows are selected as forewarning devices, and sequences corresponding to all sparrows are updated again according to the sequences corresponding to the forewarning devices, and then legal processing is performed, where Dnum is a preset integer
As a specific embodiment, the embodiment of the present invention utilizes the update formulas of the finder, the joiner, and the early-warning person to update the sequences corresponding to all sparrows for multiple times, which is convenient for local optimization.
As a preferred embodiment, the preset conditions include: the number of sparrows with similar fitness values in the population is larger than the preset number. As a specific embodiment, the embodiment of the present invention sets a prematurity determination condition, which facilitates finding out a corresponding prematurity.
In a specific embodiment of the present invention, the sparrow search process is as follows:
the method comprises the steps that firstly, an integer sequence coding mode is adopted to represent the track crane task as a gene form similar to a genetic algorithm, and the coding length represents the number of operation boxes contained in the track crane box unloading operation task;
secondly, setting the size M of the sparrow population, the number Pnum of discoverers, the number Dnum of early-warning persons, the maximum iteration number Genmax, a scaling factor F and a cross factor CR;
step three, randomly generating a track crane task sequence, namely an initial population, in a search space;
fourthly, task allocation is carried out, the fitness value Fit of each sparrow is calculated, and the current best fitness value Fb and the sequence popest thereof, as well as the current worst fitness value Fw and the sequence Popworst thereof are selected and recorded;
fifthly, Pnum sparrows are selected as discoverers, other sparrows are selected as participants, the sequence of the sparrows is updated according to the sequence update formula of the discoverers, and the sequence is legalized:
Figure BDA0003096596370000101
wherein the content of the first and second substances,
Figure BDA0003096596370000102
the task sequence number of the ith sparrow in the jth dimension in the tth generation is shown; alpha epsilon (0, 1)]Is a random number; r2(R2∈[0，1]) And ST (ST ∈ [0.5, 1 ]]) Respectively representing an early warning value and a safety value; q is a random number following a normal distribution;
sixthly, taking the remaining M-Pnum sparrows as the participants, updating the sequences of the sparrows according to the following formula after the modified sequences of the participants are updated, and carrying out legalization treatment on the sequences:
Figure BDA0003096596370000103
wherein, Xworst，jRepresenting the j-th dimension task sequence number of the current global worst sequence; xP，jRepresenting the j-th dimension task sequence number of the current global optimal sequence; r3Is a value randomly assigned a value of 1 or-1; n is the total number of tasks, i.e. the length of the sequence;
seventhly, selecting Dnum sparrows as early-warning persons, updating sequences of the sparrows according to the sequences of the early-warning persons by using the following formula, and carrying out legalization treatment on the sequences:
Figure BDA0003096596370000111
wherein, beta is used as step length control parameter and is a random number which follows normal distribution with the mean value of 0 and the variance of 1; k ∈ [ -1, 1]Is a random number; f. ofiIs the fitness value of the current sparrow individual; f. ofgAnd fwCurrent global and worst fitness values, respectively; ε is the smallest constant;
eighthly, task allocation is carried out, the fitness value Fit of each sparrow is calculated, and the current best fitness value Fb and the sequence popest thereof, as well as the current worst fitness value Fw and the sequence Popworst thereof are selected and recorded;
ninth, early maturity judgment is carried out, and the replaced sparrows which tend to be stagnated are new sparrows which are generated randomly, so that sparrow groups generate variation to a certain degree; the similar theory of precocity is specifically to obtain the quantity of the suspended railways with similar fitness value, when the quantity of the suspended railways is more than TeIf so, go to the fifth step, otherwise go to the step of difference processing (i.e., step S351), where TeAre empirical values.
As a preferred embodiment, referring to fig. 4, fig. 4 is a schematic flowchart of an embodiment of step S35 in fig. 2 provided by the present invention, and step S35 further includes steps S351 to S353, where:
in step S351, after performing variation processing, crossover processing, and legalization processing on the sequence corresponding to the early-maturing sparrow in sequence, selecting an individual having a fitness value reaching a preset value as a returned crossover individual by using a greedy strategy;
in step S352, task allocation is performed again, and the fitness value and the corresponding sequence of each sparrow after updating are determined;
in step S353, if the iteration number satisfies the termination condition, outputting the optimal task sequence, otherwise returning to the step of updating the sequences corresponding to all sparrows for multiple times according to the update formulas of the discoverer, the joiner and the early-warning person in the sparrow population.
As a specific embodiment, the differential evolution operator is applied to the early-maturing sparrows, the path-finding space can be effectively expanded, and premature trapping in a local optimal solution is avoided to a certain extent.
In a specific embodiment of the present invention, a specific flow of the difference processing is as follows (the ninth step is followed):
tenth, the similar sequences are mutated according to the following formula:
xi＝xi+F×(xj-xk)
wherein x isi，xj，xkRandomly selecting 3 different track cranes in a sparrow group, wherein r is not equal to i, j and k; f is a scaling factor;
the crossover treatment was performed according to the following formula:
Figure BDA0003096596370000121
wherein CR ∈ [0,1 ]]CR is the crossover probability, PCIs [0,1 ]]A random number in between;
then carrying out legalization treatment, then carrying out selection operation, adopting greedy strategy for selection operation, selecting the individuals with higher fitness value as returned crossed individuals, namely, only when the generated offspring sparrows are superior to the father sparrows, namely, the fitness value f (x)i(t+1))≤f(xi(t)) is retained, otherwise the parent individuals are retained to the next generation:
step ten, performing task allocation, calculating the fitness value Fit of each sparrow, selecting and recording the current best fitness value Fb and the sequence popest thereof, and the current worst fitness value Fw and the sequence Popworst thereof;
and step ten, judging whether the iteration times gen meet termination conditions, if so, outputting an optimal task sequence, and ending the algorithm, otherwise, turning to the step five.
As a preferred embodiment, referring to fig. 5, fig. 5 is a second flowchart of the embodiment of step S3 in fig. 2 provided by the present invention, and step S3 further includes steps S36 to S38, where:
in step S36, allocating the box unloading job task according to the optimal task sequence;
in step S37, the box unloading task is assigned to the corresponding track crane according to the relative relationship between the initial position of the box unloading task and the position of the earliest available time of the plurality of track cranes, and interference determination is performed;
in step S38, if there is interference, the sequence of the box unloading job tasks is reset, and if there is no interference, the next box unloading job task is allocated until all the box unloading job tasks are allocated.
Wherein the interference determination comprises: and after distributing the unloading operation task to the corresponding track crane, judging whether the relative relation between the distance between the track crane and the adjacent track crane at the corresponding moment and the safety distance between the two preset tracks meets a preset interference condition, if so, judging that no interference exists, and if not, judging that the interference exists.
As a specific embodiment, the embodiment of the invention performs task allocation according to the optimal task sequence, ensures that the task is allocated to the corresponding track crane, and does not generate interference.
In a specific embodiment of the present invention, the task allocation process includes the following steps:
step one, when the initial position of the task is before the position of the earliest available time of the track crane 1, the task is allocated to the track crane 1 to carry out interference judgment, and the operation goes to the step five;
secondly, when the initial position of the task is behind the position of the track crane 1 at the earliest available time and before the position of the track crane 2 at the earliest available time, the task is allocated to the track crane with the smallest available time at the initial position of the task to carry out interference judgment, if the interference is caused, the task is distributed to another track crane, and the operation goes to the fifth step;
thirdly, when the initial position of the task is behind the position of the track crane 2 at the earliest available time of the task and before the position of the track crane 3 at the earliest available time, the task is allocated to the track crane with the smallest available time at the initial position of the task to carry out interference judgment, if the interference is caused, the task is distributed to another track crane, and the process goes to the fifth step;
fourthly, when the initial position of the task is behind the position of the track crane 3 at the earliest available time of the task, distributing the task to the track crane 3, and turning to the fifth step;
fifthly, judging interference after distributing a single task, resetting a task sequence if the interference is caused, distributing the next task if the interference is not caused, and finishing the task distribution if all the tasks are distributed;
sixthly, the legalization processing as described above: sorting in ascending order according to the size of each value in the sequence, assigning the sorting serial number of each value in the original sequence to the original position, and for the same value in the original sequence, arranging the value in the original sequence first;
seventhly, the interference judgment specific process comprises the following steps: and after the new task is distributed to the track crane, judging according to the following two formulas, and if the two formulas are not met, interfering:
Figure BDA0003096596370000131
Figure BDA0003096596370000132
wherein the content of the first and second substances,
Figure BDA0003096596370000141
for the position of the id-th rail crane at time t, DsafeThe safe distance between the two track cranes.
In a specific embodiment of the present invention, referring to fig. 6, fig. 6 is a schematic diagram of an embodiment of a specific application scenario of a rail crane provided by the present invention, and an example of a container unloading process in a railway operation area is taken, that is, a container needs to be unloaded from a railway loading and unloading line through the rail crane, and the container is stored in a container position of a yard of a central station through the rail crane, so that the unloading operation of one operation container is completed, the number of rail cranes in the railway central station is set to be 3, only one container can be carried at each time, only one container position of the yard can be carried by one container, two trains have two tracks, and a specific scenario is shown in fig. 1.
Wherein, the loading condition of a train of freight trains is: the train is organized into 30 flat cars, containers are stacked in a single layer, 30TEU is shipped, and meanwhile, the area of a yard corresponding to the train is divided into 30 box areas. The storage condition of the containers on the main storage yard of the railway operation area is shown in the following tables 1 and 2:
TABLE 1
Figure BDA0003096596370000142
TABLE 2
Figure BDA0003096596370000143
The simulation experiment is carried out on an MATLAB platform by adopting a control variable method, the size M of a sparrow population is set to be 100, the number Pnum of discoverers is 6, the number Dnum of early-warners is 24, the maximum iteration frequency Genmax is 1000, a scaling factor F is 0.5, a cross factor CR is 0.6, a safety value ST is 0.8, D is 1000, a scaling factor F is 0.5, a cross factor CR is 0.6, a safety value ST is 0.8, and D issafe4, precocity coefficient TcFig. 7 is a schematic diagram comparing an initial task sequence and an optimal task sequence according to an embodiment of the present invention, and fig. 8 is a schematic diagram comparing a search time according to an embodiment of the present invention, where it can be seen that, compared to other embodiments, the search time is 0.02The algorithm and the method provided by the invention can quickly find an ideal value, can effectively avoid the premature phenomenon, can continuously search for the best quality, and has strong optimization capability.
The invention discloses a collaborative optimization scheduling method for a railway central station rail type container crane, which comprises the following steps of firstly, effectively obtaining modeling parameters; then, according to the modeling parameters, effectively constructing a track crane scheduling model; and finally, searching and solving are carried out by utilizing the track crane scheduling model, effective optimization is carried out, an optimal task sequence is determined, and task allocation is carried out on the track crane according to the optimal task sequence.
According to the technical scheme, the optimization capability is improved by utilizing the searching and solving after modeling, the operation balance rate and the operation efficiency of the railway station are effectively improved, the long-distance movement of the track crane is reduced, the energy consumption is reduced, and the dynamic configuration scheduling capability of the track crane of the central station is improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (10)

1. A railway center station rail type container crane collaborative optimization scheduling method is characterized by comprising the following steps:
acquiring position information of a container in a railway central station and operation parameters of a track crane;
constructing a track crane dispatching model according to the position information and the operation parameters;
and performing initial coding on the unloading operation task sequence of the track crane by adopting an integer sequence coding mode, searching and solving the track crane scheduling model, outputting an optimal task sequence, and performing task allocation on each track crane according to the optimal task sequence.
2. The method as claimed in claim 1, wherein the rail crane scheduling model includes an objective function and constraints, the objective function is used to represent minimum job completion time, and the constraints include:
the first constraint condition is used for constraining any task of the track crane to have only one corresponding task immediately before;
the second constraint condition is used for constraining any task of the track crane to have only one corresponding tightened task;
the third constraint condition is used for constraining the relation between the ending time and the starting time of the box unloading operation task corresponding to the track crane;
the fourth constraint condition is used for constraining the time connection of two continuous unloading operation tasks of the track crane;
and the fifth constraint condition is used for constraining the total moving distance of the cart corresponding to the track crane to complete all the unloading operation tasks.
3. The method of claim 2, wherein the objective function is expressed by the following formula:
Figure FDA0003096596360000011
wherein, G represents a track crane set, and G belongs to G ═ 1, 2 and 3; ig represents a set of unpacking job tasks, and Ig belongs to {1, 2, …, Ig }; fgiShowing the end time of the ith unloading task of the g-th track crane; min f represents the minimum job completion time.
4. The method of claim 2, wherein the first, second, third, fourth and fifth constraints are expressed by the following equations:
Figure FDA0003096596360000021
Figure FDA0003096596360000022
Figure FDA0003096596360000023
Figure FDA0003096596360000024
Figure FDA0003096596360000025
wherein the content of the first and second substances,
Figure FDA0003096596360000026
representing a variable of 0 to 1, if the ith unloading task and the jth unloading task are operated by the gth rail crane,
Figure FDA0003096596360000027
if not, then,
Figure FDA0003096596360000028
Sgishowing the starting time of the ith unloading task of the g-th track crane; dgiThe horizontal moving distance of the g-th rail crane during the ith unloading task is shown;
Figure FDA0003096596360000029
the vertical movement distance of the g-th rail crane during the ith unloading task is shown; v. of1Representing the moving speed of the g-th track crane; v. of2Trolley for g-th rail craneThe moving speed;
Figure FDA00030965963600000210
the empty driving distance of the g-th track crane is represented when the i-th box unloading task and the j-th box unloading task are continuously executed; dgAnd the total moving distance of the g-th rail crane for completing all box unloading operation tasks is shown.
5. The cooperative optimal scheduling method for the rail container crane at the railway center station according to claim 1, wherein the initial coding of the task sequence of the unloading operation of the rail crane by using an integer sequence coding mode comprises:
expressing the box unloading task of the track crane into a genetic algorithm gene form by adopting an integer sequence coding mode;
and determining the coding length of the box unloading operation task of the track crane according to the number of the operation boxes contained in the box unloading operation task.
6. The collaborative optimal scheduling method for the rail-mounted container crane at the railway center station according to claim 1, wherein the searching and solving of the rail crane scheduling model and the outputting of the optimal task sequence comprise:
initializing sparrow population algorithm parameters, randomly generating an initial task sequence of a box unloading operation task of the rail crane in a search space, and forming an initial population according to the initial task sequence;
performing task allocation according to the initial population, and determining a sequence and a fitness value corresponding to each sparrow;
according to the updating formulas of discoverers, joiners and early-warning persons in the sparrow population, the sequences corresponding to all sparrows are updated for multiple times, and the fitness value and the corresponding sequence of each sparrow after the multiple updating are determined;
judging whether preset conditions are met or not according to the fitness value of each sparrow, if so, returning to the step of updating the sequences corresponding to all sparrows for multiple times according to the updating formulas of discoverers, joiners and early-warning persons in the sparrow population;
and if not, the corresponding particles are early sparrows, differential processing is carried out on the early sparrows, the sequences corresponding to the early sparrows are updated and replaced until iteration termination conditions are met, and the final optimal task sequence is output.
7. The collaborative optimal scheduling method for the rail container crane with the railway center station as claimed in claim 6, wherein the updating the sequence corresponding to all sparrows for a plurality of times according to the updating formulas of the discoverer, the joiner and the early-warning device in the sparrow population comprises:
selecting Pnum sparrows as discoverers and other sparrows as addicts, updating sequences corresponding to all sparrows according to the sequences corresponding to the discoverers, and carrying out legalization treatment, wherein Pnum is a preset integer, the legalization treatment is ascending sequencing according to the size of each value of the sequences, assigning sequencing serial numbers of each value of an original sequence to original positions, and for the sparrows with the same value in the original sequence, the sparrows are arranged in the original sequence first;
updating the sequences corresponding to all sparrows again according to the updated sequences of the joiners, and carrying out legalization processing;
and selecting Dnum sparrows as early warning persons, updating sequences corresponding to all the sparrows again according to the sequences corresponding to the early warning persons, and carrying out legalization processing, wherein Dnum is a preset integer.
8. The method for collaborative optimal scheduling of rail-bound container cranes for railway hubs as claimed in claim 7, wherein said preset conditions comprise: the number of sparrows with similar fitness values in the population is larger than the preset number.
9. The method as claimed in claim 8, wherein if the optimal task sequence is not satisfied, the corresponding particles are early sparrows, the early sparrows are differentially processed, the sequence corresponding to the early sparrows is updated and replaced until an iteration termination condition is satisfied, and outputting the final optimal task sequence comprises:
sequentially carrying out variation processing, cross processing and legalization processing on the sequences corresponding to the early-maturing sparrows, and selecting individuals with fitness values reaching preset values as returned cross individuals by adopting a greedy strategy;
performing task allocation again, and determining the fitness value and the corresponding sequence of each sparrow after updating;
and if the iteration times meet the iteration termination condition, outputting the optimal task sequence, otherwise, returning to the step of updating the sequences corresponding to all the sparrows for multiple times according to the updating formulas of the discoverers, the joiners and the early-warning persons in the sparrow population.
10. The collaborative optimal scheduling method for the rail-mounted container crane at the railway center station according to claim 1, wherein the task allocation for each rail crane according to the optimal task sequence comprises:
distributing the unloading operation tasks according to the optimal task sequence;
distributing the box unloading operation tasks to corresponding track cranes according to the relative relation between the initial positions of the box unloading operation tasks and the positions of the earliest available time of the track cranes, and performing interference judgment;
if the interference exists, the sequence of the box unloading operation tasks is reset, and if the interference does not exist, the next box unloading operation task is distributed until all the box unloading operation tasks are distributed;
wherein the interference determination comprises: and after distributing the unloading operation task to the corresponding track crane, judging whether the relative relation between the distance between the track crane and the adjacent track crane at the corresponding moment and the safety distance between the two preset tracks meets a preset interference condition, if so, judging that no interference exists, and if not, judging that the interference exists.
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