CN116307646A - One-rail double-vehicle scheduling method based on two-stage dynamic partitioning algorithm - Google Patents

One-rail double-vehicle scheduling method based on two-stage dynamic partitioning algorithm Download PDF

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CN116307646A
CN116307646A CN202310580393.7A CN202310580393A CN116307646A CN 116307646 A CN116307646 A CN 116307646A CN 202310580393 A CN202310580393 A CN 202310580393A CN 116307646 A CN116307646 A CN 116307646A
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王筱圃
袁丁
张弢
张庆
宋玉雪
岳亚莉
钟智敏
刘伟
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Hkust Intelligent Internet Of Things Technology Co ltd
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Abstract

The invention relates to the technical field of rail guided vehicle dispatching, and discloses a one-rail double-vehicle dispatching method based on a two-stage dynamic partitioning algorithm, which comprises the following steps: partitioning the mobile task, wherein the optimization target is to minimize the execution time difference of the mobile task set between the two partitions, and determining the execution sequence of the mobile task of the two partitions; obtaining an optimal moving task sequence of the cross-region moving task through a second-stage scheduling algorithm based on an improved simulated annealing algorithm; the self-adaptive two-stage real-time scheduling method can improve the optimizing speed of the algorithm and reduce the response time of the algorithm.

Description

One-rail double-vehicle scheduling method based on two-stage dynamic partitioning algorithm
Technical Field
The invention relates to the technical field of rail guided vehicle dispatching, in particular to a one-rail double-vehicle dispatching method based on a two-stage dynamic partitioning algorithm.
Background
Rail guided vehicles (Rail Guided Vehicle, RGV), also known as rail shuttle vehicles.
The existing scheduling schemes of RGV mainly include partition scheduling, regular scheduling and regular real-time scheduling, which are described in detail below.
Partition type scheduling: the layout requirements are high, and the method is suitable for symmetrical layout, namely RGV stations are arranged on two sides, but the application effect under asymmetrical layout is poor; the utilization of RGVs is affected by the amount of tasks after partitioning, resulting in large differences in RGVs duty cycle.
And (3) regular scheduling: custom development is carried out according to different layout requirements; the scheduling is not flexible enough, and an unreasonable scheduling phenomenon exists, so that RGV movement is wasted; the service scenes to be considered are more, and errors can occur during development due to incomplete service scene consideration.
Conventional real-time scheduling: the overall scheduling efficiency is poor, and the method is not suitable for a short-time large-scale operation mode.
The invention aims to solve the problem of one-rail double-vehicle scheduling of large-scale operation tasks in a short time under a multi-layout scene, namely, how to scientifically schedule two RGVs in real time under the layout scene of symmetry, asymmetry and the like in which RGVs have a large number of moving tasks, and improves the RGV scheduling efficiency so as to reduce the total time for completing all the moving tasks.
Disclosure of Invention
In order to solve the technical problems, the invention provides a one-rail double-vehicle scheduling method based on a two-stage dynamic partitioning algorithm.
In order to solve the technical problems, the invention adopts the following technical scheme:
A one-rail double-vehicle scheduling method based on a two-stage dynamic partitioning algorithm is used for optimizing the total time of moving tasks of two RGVs on the same track and outputting a corresponding optimal moving task sequence; the sequence of the mobile tasks is the execution sequence of the mobile tasks; the one-rail double-vehicle scheduling method comprises the following steps:
s1, partitioning a mobile task, wherein an optimization target is to minimize the execution time difference of a mobile task set of two partitions, and the execution sequence of the mobile task of the two partitions is determined; the starting point and the end point of each mobile task are called RGV point positions, and specifically include:
s11: according to a certain RGV point position
Figure SMS_1
Dividing the track to form a left partition and a right partition>
Figure SMS_2
Representing a set of all RGV points; the moving task whose starting point and end point are both located in the left partition is marked as a moving task set +.>
Figure SMS_3
The method comprises the steps of carrying out a first treatment on the surface of the The mobile task whose starting point and end point are both located in the right partition is marked as the mobile task set +.>
Figure SMS_4
；
S12: by first-stage scheduling algorithm pair based on ant colony-genetic-particle swarm algorithm
Figure SMS_6
and />
Figure SMS_8
The mobile tasks in the left partition are sequenced and the mobile task sequences of the left partition are respectively output>
Figure SMS_10
And the movement task sequence of the right partition +.>
Figure SMS_7
，/>
Figure SMS_9
and />
Figure SMS_11
Respectively make- >
Figure SMS_12
and />
Figure SMS_5
The total moving distance of the middle moving task is shortest;
s13: calculating current RGV point positions respectively
Figure SMS_13
Corresponding->
Figure SMS_14
and />
Figure SMS_15
Execution time of->
Figure SMS_16
and />
Figure SMS_17
Calculating an execution time difference
Figure SMS_18
；
S14: repeating steps S11 to S13 until completion
Figure SMS_19
Selecting all RGV points; selecting the smallest execution time difference +.>
Figure SMS_20
The corresponding RGV-bit is taken as the optimal partition-bit +.>
Figure SMS_21
And the smallest execution time difference +.>
Figure SMS_22
Corresponding->
Figure SMS_23
and />
Figure SMS_24
Respectively used as the optimal moving task sequence of the left partition and the optimal moving task sequence of the right partition;
s2, obtaining an optimal moving task sequence of the cross-region moving task through a second-stage scheduling algorithm based on an improved simulated annealing algorithm; wherein the transregional moving task is that the starting point and the end point are respectively positioned at the optimal partition point positions
Figure SMS_25
A movement task on both sides; when the second stage scheduling algorithm is executed, only one RGV can execute the mobile task at the same time, and the RGV which does not execute the mobile task is avoided from being out of the mobile area of the RGV which executes the mobile task.
Specifically, in step S12, the moving task sequence of the left partition is outputted by the first-stage scheduling algorithm based on the ant colony-genetic-particle swarm algorithm
Figure SMS_26
And the movement task sequence of the right partition +.>
Figure SMS_27
When the method comprises the following steps:
S131: randomly selecting the left partition and the right partition respectively
Figure SMS_28
A sequence of mobile tasks, calculating left partition +.>
Figure SMS_29
The respective fitness value, right partition +.>
Figure SMS_30
The respective fitness values of the respective mobile task sequences;
s132: respectively selecting from left partition and right partition
Figure SMS_31
A moving task sequence B with the shortest total moving distance,
Figure SMS_32
calculating the fitness value corresponding to each moving task sequence, and taking the fitness value of each moving task sequence B as the initial pheromone concentration of any two adjacent moving tasks in the moving task sequence B during ant colony search;
s133: taking the minimum value of the adaptation degree value of the moving task sequence of the left partition in the step S131 as the individual extremum of the left partition
Figure SMS_34
The corresponding movement task sequence is +.>
Figure SMS_36
The method comprises the steps of carrying out a first treatment on the surface of the Taking the minimum value of the right partition moving task sequence fitness value in the step S131 as the individual extremum +.>
Figure SMS_41
The corresponding movement task sequence is +.>
Figure SMS_35
The method comprises the steps of carrying out a first treatment on the surface of the Calculating the fitness values of all the mobile task sequences of the left partition and the right partition, and taking the minimum fitness value as the global extremum +.>
Figure SMS_38
And global extremum of right partition->
Figure SMS_40
Global extremum->
Figure SMS_42
The corresponding sequence of movement tasks is marked +.>
Figure SMS_33
Global extremum->
Figure SMS_37
The corresponding sequence of movement tasks is marked +. >
Figure SMS_39
；
S134: initializing the iteration times t=0;
s135: in the left partition
Figure SMS_52
Randomly selecting +.>
Figure SMS_43
A plurality of movement tasks are allocated to ∈>
Figure SMS_47
Ants; for->
Figure SMS_46
Ant only, will be->
Figure SMS_50
The mobile task allocated by ant is placed in the corresponding task sequence solution set to calculate +.>
Figure SMS_53
Only ants are moved by the current task->
Figure SMS_56
To next movement task->
Figure SMS_51
State transition probability>
Figure SMS_55
And according to->
Figure SMS_44
Acquire next movement task->
Figure SMS_49
The movement task is further->
Figure SMS_54
Is placed at->
Figure SMS_58
Only the task sequence corresponding to ants is concentrated; when the mobile tasks to be ordered are all placed in the task sequence solution set, the +.>
Figure SMS_57
Mobile task sequence explored by ants only ∈>
Figure SMS_59
The method comprises the steps of carrying out a first treatment on the surface of the Computing the movement task sequence->
Figure SMS_45
Is>
Figure SMS_48
；
In the right partition
Figure SMS_71
Randomly selecting +.>
Figure SMS_61
A plurality of movement tasks are allocated to ∈>
Figure SMS_67
Ants; for->
Figure SMS_73
Ant only, will be->
Figure SMS_76
The mobile task allocated by ant is placed in the corresponding task sequence solution set to calculate +.>
Figure SMS_74
Only ants are moved by the current task->
Figure SMS_75
To next movement task->
Figure SMS_69
State transition probability>
Figure SMS_72
And according to->
Figure SMS_60
Acquire next movement task->
Figure SMS_64
The movement task is further->
Figure SMS_63
Is placed at->
Figure SMS_65
Only the task sequence corresponding to ants is concentrated; when the mobile tasks to be ordered are all placed in the task sequence solution set, the +. >
Figure SMS_68
Mobile task sequence explored by ants only ∈>
Figure SMS_70
The method comprises the steps of carrying out a first treatment on the surface of the Computing the movement task sequence->
Figure SMS_62
Is>
Figure SMS_66
；
S136, for left partition: moving task sequences
Figure SMS_87
And->
Figure SMS_79
Performing cross operation to obtain a mobile task sequence
Figure SMS_84
，/>
Figure SMS_80
And->
Figure SMS_82
Performing the cross operation again to obtain a moving task sequence +.>
Figure SMS_85
And then (2) to->
Figure SMS_88
Performing mutation operation to obtain a mobile task sequence->
Figure SMS_86
Calculating the execution movement task sequence->
Figure SMS_91
Is>
Figure SMS_77
The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
Figure SMS_81
Compared with->
Figure SMS_89
Smaller, accept the movement task sequence +.>
Figure SMS_92
The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
Figure SMS_90
Compared with->
Figure SMS_93
Not get smaller, then->
Figure SMS_78
The moving task sequence corresponding to ants only is still
Figure SMS_83
；
For the right partition: moving task sequences
Figure SMS_102
And->
Figure SMS_94
Performing cross operation to obtain a moving task sequence +.>
Figure SMS_99
，/>
Figure SMS_104
And->
Figure SMS_108
Performing the cross operation again to obtain a moving task sequence +.>
Figure SMS_107
And then (2) to->
Figure SMS_110
Performing mutation operation to obtain a mobile task sequence->
Figure SMS_103
Calculating the execution movement task sequence->
Figure SMS_109
Is>
Figure SMS_95
The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
Figure SMS_101
Compared with->
Figure SMS_97
Smaller, accept the movement task sequence +.>
Figure SMS_100
The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
Figure SMS_105
Compared with->
Figure SMS_106
Not get smaller, then->
Figure SMS_96
The moving task sequence corresponding to ants only is still
Figure SMS_98
；
S137: after the moving task sequences of all ants in the left partition and the moving task sequences of all ants in the right partition are obtained, individual extremum values of ant colony in the left partition and the right partition are obtained
Figure SMS_112
、/>
Figure SMS_115
Mobile task sequence corresponding to individual extremum
Figure SMS_116
、/>
Figure SMS_113
Global extremum->
Figure SMS_114
、/>
Figure SMS_117
A mobile task sequence corresponding to the global extremum +.>
Figure SMS_118
、
Figure SMS_111
Updating;
s138: iterative equation by ant colony pheromone
Figure SMS_120
Updating the information concentration between the mobile tasks; />
Figure SMS_124
and />
Figure SMS_126
Representing the movement task +.for the t-th and t+1-th iterations, respectively>
Figure SMS_121
To move task->
Figure SMS_122
Pheromone concentration,/->
Figure SMS_125
Representing pheromone volatilization factors; />
Figure SMS_127
For the t-th iteration, move task +.>
Figure SMS_119
To move task->
Figure SMS_123
New pheromone concentration; the number of iterations t=t+1;
s139: repeating steps S135 to S138 until the number of repetitions reaches the set maximum number of iterations
Figure SMS_128
；
S1310: taking the moving task sequence corresponding to the individual extremum of the left partition as the optimal moving task sequence of the left partition; and taking the moving task sequence corresponding to the individual extremum of the right partition as the optimal moving task sequence of the right partition.
Specifically, state transition probabilities
Figure SMS_130
，/>
Figure SMS_136
Indicate->
Figure SMS_138
Ant only from the movement task->
Figure SMS_132
Accessible movementsA collection of dynamic tasks; />
Figure SMS_134
Representing the slave movement task->
Figure SMS_137
To move task->
Figure SMS_140
Is>
Figure SMS_129
Visibility of (i.e.)>
Figure SMS_139
，/>
Figure SMS_141
Representing movement task->
Figure SMS_142
Starting point to move task->
Figure SMS_131
Is the distance of the end point of (2); />
Figure SMS_133
Representing the relative importance of the track; />
Figure SMS_135
Indicating the relative importance of visibility.
Specifically, in step S13, a sequence of movement tasks to be operated
Figure SMS_155
And a specific movement task sequence->
Figure SMS_145
When the cross operation is performed, the task sequence is moved>
Figure SMS_150
、/>
Figure SMS_146
Each comprising M mobile tasks; randomly generating two non-equal positive integers +.>
Figure SMS_148
、
Figure SMS_151
，/>
Figure SMS_153
Get the movement task sequence->
Figure SMS_157
The upper index is +.>
Figure SMS_160
To->
Figure SMS_143
Between mobile task segments->
Figure SMS_152
Move task sequence->
Figure SMS_156
Go up and->
Figure SMS_159
The movement task segment at the same position is removed and +.>
Figure SMS_158
Splicing the rest mobile task fragments to obtain a mobile task fragment +.>
Figure SMS_161
Finally, move task segment->
Figure SMS_144
Splice to Mobile task segment->
Figure SMS_147
Terminal, and thus get the sequence of tasks with movement +.>
Figure SMS_149
Corresponding new movement task sequence->
Figure SMS_154
。
Specifically, in step S13, a sequence of movement tasks to be operated
Figure SMS_164
When the mutation operation is performed, the task sequence is moved
Figure SMS_167
、/>
Figure SMS_168
All comprising M movement tasks, randomly generating two unequal positive integers +.>
Figure SMS_162
、/>
Figure SMS_166
，/>
Figure SMS_170
Will->
Figure SMS_172
Index +.>
Figure SMS_163
and />
Figure SMS_165
Transposition is performed on the two mobile tasks of (2) to obtain AND +.>
Figure SMS_169
Corresponding new movement task sequence->
Figure SMS_171
。
Specifically, in step S13, the left partition moves the fitness value of the task sequence
Figure SMS_173
The method comprises the following steps:
Figure SMS_174
；
wherein
Figure SMS_175
Moving the total number of tasks for the left partition; />
Figure SMS_179
Is->
Figure SMS_180
Start of individual movement tasks, +.>
Figure SMS_177
Is->
Figure SMS_178
End point of each movement task- >
Figure SMS_181
Is->
Figure SMS_182
and />
Figure SMS_176
Is a distance of (2);
fitness value of right partition movement task sequence
Figure SMS_183
The method comprises the following steps:
Figure SMS_184
；
wherein
Figure SMS_185
The total number of tasks is moved for the right partition.
Specifically, the second stage scheduling algorithm based on the improved simulated annealing algorithm specifically comprises the following steps:
S21，initializing parameters of a second-stage scheduling algorithm: setting an initial temperature
Figure SMS_186
Stop temperature->
Figure SMS_187
Temperature decay Rate->
Figure SMS_188
Probability of mutation->
Figure SMS_189
The method comprises the steps of carrying out a first treatment on the surface of the Setting the number of internal cycles per unit temperature +.>
Figure SMS_190
The method comprises the steps of carrying out a first treatment on the surface of the Setting the current temperature +.>
Figure SMS_191
；
S22: randomly distributing to-be-sequenced cross-region mobile tasks to two
Figure SMS_192
Obtaining an initial solution; solving, namely a moving task sequence of a cross-region moving task;
s23: judging the current temperature
Figure SMS_193
Whether or not is greater than->
Figure SMS_194
The method comprises the steps of carrying out a first treatment on the surface of the If yes, go to step S24, if no, go to step S210;
s24: constructing four ways of searching the neighborhood solution, and searching the neighborhood solution for the initial solution according to the ways of executing the four ways of searching the neighborhood solution with equal probability according to the internal circulation times LL at the preset unit temperature; then according to the setting
Figure SMS_195
The rule judges to reserve new solutions or old solutions, and a plurality of solutions obtained through internal circulation at the current temperature are obtained;
s25: obtaining a plurality of solutions through internal circulation at the current temperature, calculating respective fitness values, taking two solutions with the minimum fitness values for performing cross operation, and calculating the fitness values of new solutions obtained after the cross operation; judging whether the fitness value of the new solution is smaller than the minimum fitness value of each solution at the current temperature, if so, taking the new solution as the optimal solution at the current temperature; if not, taking the solution with the minimum fitness value at the current temperature as the optimal solution at the current temperature;
S26: performing cross operation on the optimal solution at the current temperature and the optimal solution at the previous temperature, calculating the fitness value of the new solution after the cross, judging whether the fitness value of the new solution is smaller than the fitness value of the optimal solution at the current temperature, and if so, taking the new solution as the optimal solution at the current temperature; if not, keeping the optimal solution at the current temperature unchanged;
s27: taking the optimal solution at the current temperature, and according to the set variation probability
Figure SMS_196
Executing the mutation operation, calculating the fitness value of the new solution after executing the mutation operation, judging whether the fitness value of the new solution is smaller than the fitness value of the optimal solution at the current temperature, and if so, taking the new solution as the optimal solution at the current temperature; if not, keeping the optimal solution at the current temperature unchanged;
s28: judging whether the fitness value of the optimal solution at the current temperature is smaller than that of the optimal solution at the previous temperature, if so, keeping the optimal solution at the current temperature as a final optimal solution; if not, taking the optimal solution at the previous temperature as the optimal solution finally obtained at the current temperature;
s29: performing a cooling process, i.e.
Figure SMS_197
Jumping to the step S24, and taking the optimal solution finally obtained at the current temperature as the initial solution in the step S24;
S210: for the current temperature
Figure SMS_198
Decoding the finally obtained optimal solution to obtain the optimal movement of the transregional movement taskA task sequence.
Specifically, the metapolis rule is set as:
Figure SMS_210
a sequence of mobile tasks representing an old solution, +.>
Figure SMS_199
A sequence of mobile tasks representing a new solution, +.>
Figure SMS_206
A sequence of mobile tasks representing the current solution, +.>
Figure SMS_202
Fitness value representing old solution, +.>
Figure SMS_203
Fitness value representing the new solution, +.>
Figure SMS_208
An fitness value representing a current solution; according to the calculation mode of fitness value function, find +.>
Figure SMS_211
and />
Figure SMS_209
Difference>
Figure SMS_213
I.e. +.>
Figure SMS_201
The method comprises the steps of carrying out a first treatment on the surface of the If->
Figure SMS_205
Then accept the new solution, i.e. the current solution +.>
Figure SMS_214
，/>
Figure SMS_217
The method comprises the steps of carrying out a first treatment on the surface of the If condition A is satisfied, a new solution is accepted, i.e.)>
Figure SMS_215
，/>
Figure SMS_218
The method comprises the steps of carrying out a first treatment on the surface of the If the condition A is not satisfied, the old solution is retained, i.e. +.>
Figure SMS_204
，
Figure SMS_207
The method comprises the steps of carrying out a first treatment on the surface of the Wherein condition a is: />
Figure SMS_212
And->
Figure SMS_216
，/>
Figure SMS_200
Representing random numbers uniformly distributed within the interval (0, 1).
Specifically, when the cross operation is performed in the second stage scheduling algorithm: mobile task sequence to be operated
Figure SMS_235
And a specific movement task sequence->
Figure SMS_238
When the cross operation is performed, the task sequence is moved>
Figure SMS_240
、/>
Figure SMS_219
All comprising M movement tasks, three non-equal positive integers are randomly generated +.>
Figure SMS_225
、/>
Figure SMS_228
、/>
Figure SMS_231
，/>
Figure SMS_221
Get the movement task sequence->
Figure SMS_224
Go up the index->
Figure SMS_227
To the point of
Figure SMS_233
Is->
Figure SMS_229
Index number->
Figure SMS_232
To->
Figure SMS_237
Is- >
Figure SMS_242
The method comprises the steps of carrying out a first treatment on the surface of the Move task sequence->
Figure SMS_236
Top and Mobile task segment->
Figure SMS_239
、/>
Figure SMS_241
The same-position mobile task segment is removed and +.>
Figure SMS_243
The rest segments are spliced to obtain a mobile task segment +.>
Figure SMS_220
Finally, move task segment->
Figure SMS_226
、/>
Figure SMS_230
Splice to Mobile task segment->
Figure SMS_234
Terminal, get the sequence of movement tasks to be operated +.>
Figure SMS_222
Corresponding new movement task sequence->
Figure SMS_223
。
Specifically, in the second stage scheduling algorithm, the fitness value of the mobile task sequence
Figure SMS_244
：
Figure SMS_245
；
wherein ,
Figure SMS_262
for the total number of cross-zone movement tasks, +.>
Figure SMS_249
Is->
Figure SMS_260
Start of individual movement tasks, +.>
Figure SMS_259
Is->
Figure SMS_263
End point of each movement task->
Figure SMS_264
Is->
Figure SMS_265
and />
Figure SMS_254
Distance of->
Figure SMS_257
Is->
Figure SMS_246
A number of RGV assigned by each mobile task; />
Figure SMS_251
Indicate->
Figure SMS_252
The RGV point position set which is required to pass through by the mobile task and the current position of another RGV have intersection or not, if yes, 1 is taken, and if not, 0 is taken;
Figure SMS_255
indicate->
Figure SMS_258
The union of the RGV point position set required to pass by the mobile task and the RGV current position for executing the i mobile task to the i mobile task starting point is provided with or not with the other RGV current position, wherein the intersection is 1, and the non-intersection is 0; />
Figure SMS_261
Indicate->
Figure SMS_247
Whether the direction of each mobile task is matched with the relative position of the RGV for executing the ith mobile task or not, taking 1 in a matching way, and taking 0 in a non-matching way; / >
Figure SMS_250
Representing execution of +.>
Figure SMS_253
Real-time location of RGVs of the individual mobile tasks; />
Figure SMS_256
Indicating that no->
Figure SMS_248
Real-time location of RGV for each mobile task.
First, the
Figure SMS_266
Whether the direction of the ith movement task matches the relative position of the RGV performing the ith movement task means that if the direction of the ith movement task is left, the RGV performing the ith movement task is located on the left side, and otherwise, it is called matching. The relative position of the RGVs may be represented by the numbering of the RGVs.
Compared with the prior art, the invention has the beneficial technical effects that:
the one-rail double-vehicle scheduling method based on the two-stage dynamic partitioning algorithm is innovative in algorithm design, and can improve the optimizing speed and reduce the response time. Specifically, the method is suitable for different layout scenes, can realize global search of solutions, can output a globally optimal scheduling scheme, and avoids invalid movement of RGVs; the two-stage scheduling algorithm is designed for solving, so that the scheduling logic is simple, the calculated amount is small, the output scheduling scheme is fast, the algorithm solving complexity is reduced, the algorithm response rate is improved, and the method is suitable for a short-time large-scale operation mode; dynamic partitioning operation can reduce RGV road leaving distance and invalid moving distance; RGV cross-zone operation is reduced, and RGV parking waiting time can be reduced.
Drawings
FIG. 1 is a flow chart of a scheduling method of the present invention.
Detailed Description
A preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.
The one-rail double-vehicle scheduling method comprises two stages. The movement tasks of the RGV include partition movement tasks and cross-zone movement tasks; the partition moving task refers to a moving task with a starting point and an end point both positioned in the same partition; a cross-zone movement task refers to a movement task in which the start point and the end point are both located in different partitions.
The first stage: and dynamically partitioning according to the starting point and the ending point of each mobile task in the mobile task set, wherein the optimization target is to minimize the time difference of executing the mobile tasks by two RGVs, and outputting the optimal mobile task sequence of the partitioned mobile tasks. The method specifically comprises the following steps:
the starting point and the end point of each mobile task are called RGV point positions; after dividing the track according to a certain RGV point position, naturally forming a left partition and a right partition, and marking the moving tasks with the starting point and the ending point both positioned in the left partition as a left partition moving task set
Figure SMS_267
The moving task with the starting point and the end point both in the right partition is marked as a right partition moving task set +.>
Figure SMS_268
. The first stage scheduling algorithm is used for respectively performing the corresponding steps >
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Mobile task in->
Figure SMS_270
The movement tasks of the RGVs are ordered such that the total movement distance of the RGVs is minimized. Then calculates two RGVs to execute +.>
Figure SMS_271
Middle movement task and +.>
Figure SMS_272
The time difference of the moving task.
After partitioning by different RGV points, repeating the above steps to obtain time difference corresponding to each RGV point, and taking one RGV point with the smallest time difference as the optimal partition point
Figure SMS_273
And the moving task sequence of the left partition at this time is +.>
Figure SMS_274
As the best movement task sequence for the left partition, the movement task sequence for the right partition is +.>
Figure SMS_275
As the best sequence of movement tasks for the right partition.
And a second stage: after the screening of the first stage, the rest mobile tasks all need to cross the set optimal partition point positions
Figure SMS_276
Namely, the tasks are all cross-region moving tasks; thus, when an RGV is performing a movement task at this stage, two RGVs cannot be performed simultaneously, i.e., when one RGV is after performing the allocated movement task, it is required to avoid the movement task beyond the road segment where the other RGV is performing the movement task (e.g., beyond the start point or beyond the end point of the other RGV) so as to ensure that no collision occurs. Specifically, the optimal allocation scheme of the cross-region mobile task is searched through a second-stage scheduling algorithm, so that an optimal mobile task sequence of the cross-region mobile task is obtained, and the optimization target is that the total completion time of all the cross-region mobile tasks is the shortest.
The first stage scheduling algorithm performs two-layer coding on the mobile task sequence, including:
first layer coding:
Figure SMS_277
a starting point for executing a certain movement task;
second layer coding:
Figure SMS_278
the end point of a certain movement task is performed.
In the first stage scheduling algorithm, the adaptability value of the left partition moving task sequence
Figure SMS_279
：
Figure SMS_280
；
Fitness value of right partition movement task sequence
Figure SMS_281
The method comprises the following steps:
Figure SMS_282
。
wherein
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For the total number of left partition move tasks, +.>
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Moving the total number of tasks for the right partition; />
Figure SMS_290
Is->
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Start of individual movement tasks, +.>
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Is->
Figure SMS_289
End point of each movement task->
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Is->
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and />
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Is a distance of (3).
The second stage scheduling algorithm performs four-layer coding on the mobile task sequence, including:
first layer coding:
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a starting point for executing a certain movement task;
second layer coding:
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executing the end point of a certain mobile task;
third layer coding:
Figure SMS_294
numbering;
fourth layer coding:
Figure SMS_295
a direction of movement.
In the second stage scheduling algorithm, the fitness value of the mobile task sequence
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：
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；
wherein ,
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for the total number of cross-zone movement tasks, +.>
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Is->
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Start of individual movement tasks, +.>
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Is->
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End point of each movement task->
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Is->
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and />
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Distance of->
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Is->
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A number of RGV assigned by each mobile task; />
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Indicate->
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The RGV point position set which is required to pass through by the mobile task and the current position of another RGV have intersection or not, if yes, 1 is taken, and if not, 0 is taken;
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Indicate->
Figure SMS_307
The union of the RGV point position set required to pass by the mobile task and the RGV current position for executing the i mobile task to the i mobile task starting point is provided with or not with the other RGV current position, wherein the intersection is 1, and the non-intersection is 0; />
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Indicate->
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Whether the direction of each mobile task is matched with the number of the RGV for executing the ith mobile task or not, taking 1 in a matching way, and taking 0 in a non-matching way; />
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Representing execution of +.>
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Real-time location of RGVs of the individual mobile tasks; />
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Indicating that no->
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Real-time location of RGV for each mobile task.
First, the
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Whether the direction of the ith movement task matches the relative position of the RGV performing the ith movement task or not, that is, if the direction of the ith movement task is left, the RGV performing the ith movement task is locatedOn the left, then match is called, otherwise mismatch. The relative position of the RGVs may be represented by the numbering of the RGVs.
Specifically, the mobile task sequence of the left partition is output through a first-stage scheduling algorithm
Figure SMS_319
And the movement task sequence of the right partition +.>
Figure SMS_320
When the method comprises the following steps:
s131: randomly selecting the left partition and the right partition respectively
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A sequence of mobile tasks, calculating left partition +.>
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The respective fitness value, right partition +. >
Figure SMS_323
The respective fitness values of the respective mobile task sequences; in the present embodiment
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。
S132: respectively selecting from left partition and right partition
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Calculating the fitness value corresponding to each moving task sequence by the moving task sequence with the shortest total moving distance, and taking the fitness value of each moving task sequence B as the initial pheromone concentration of any two adjacent moving tasks in the moving task sequence B during ant colony search; in this embodiment +.>
Figure SMS_326
。
S133: taking the minimum value of the adaptation degree value of the moving task sequence of the left partition in the step S131 as the individual extremum of the left partition
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The corresponding movement task sequence is +.>
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The method comprises the steps of carrying out a first treatment on the surface of the Taking the minimum value of the right partition moving task sequence fitness value in the step S131 as the individual extremum +.>
Figure SMS_333
The corresponding movement task sequence is +.>
Figure SMS_328
The method comprises the steps of carrying out a first treatment on the surface of the Calculating the fitness values of all the mobile task sequences of the left partition and the right partition, and taking the minimum fitness value as the global extremum +.>
Figure SMS_332
And global extremum of right partition->
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Global extremum->
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The corresponding sequence of movement tasks is marked +.>
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Global extremum->
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The corresponding sequence of movement tasks is marked +.>
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。
S134: the number of iterations t=0 is initialized.
S135: in the left partition
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Randomly selecting +.>
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A plurality of movement tasks are allocated to ∈ >
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Ants; for->
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Ant only, will be->
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The mobile task allocated by ant is placed in the corresponding task sequence solution set to calculate +.>
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Only ants are moved by the current task->
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To next movement task->
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State transition probability>
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And according to->
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Acquire next movement task->
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The movement task is further->
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Is placed at->
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Only the task sequence corresponding to ants is concentrated; when the mobile tasks to be ordered are all placed in the task sequence solution set, the +.>
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Mobile task sequence explored by ants only ∈>
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The method comprises the steps of carrying out a first treatment on the surface of the Computing the movement task sequence->
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Is>
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。
In the right partition
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Randomly selecting +.>
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A plurality of movement tasks are allocated to ∈>
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Ants; for->
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Ant only, will be->
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The mobile task allocated by ant is placed in the corresponding task sequence solution set to calculate +.>
Figure SMS_367
Only ants are moved by the current task->
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To next movement task->
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State transition probability>
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And according to->
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Acquire next movement task->
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The movement task is further->
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Is placed at->
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Only the task sequence corresponding to ants is concentrated; when the mobile tasks to be ordered are all placed in the task sequence solution set, the +.>
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Mobile task sequence explored by ants only ∈ >
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The method comprises the steps of carrying out a first treatment on the surface of the Computing the movement task sequence->
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Is>
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。
S136, for left partition: moving task sequences
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And->
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Performing cross operation to obtain a mobile task sequence
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，/>
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And->
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Performing the cross operation again to obtain a moving task sequence +.>
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And then (2) to->
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Performing mutation operation to obtain a mobile task sequence->
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Calculating the execution movement task sequence->
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Is>
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The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
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Compared with->
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Smaller, accept the movement task sequence +.>
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The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
Figure SMS_379
Compared with->
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Not get smaller, then->
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The moving task sequence corresponding to ants only is still
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。
For the right partition: moving task sequences
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And->
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Performing cross operation to obtain a moving task sequence +.>
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，/>
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And->
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Performing the cross operation again to obtain a moving task sequence +.>
Figure SMS_400
And then (2) to->
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Performing mutation operation to obtain a mobile task sequence->
Figure SMS_397
Calculating the execution movement task sequence->
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Is>
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The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
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Compared with->
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Smaller, accept the movement task sequence +.>
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The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
Figure SMS_398
Compared with->
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Not get smaller, then->
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The moving task sequence corresponding to ants only is still
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。
S137: after the moving task sequences of all ants in the left partition and the moving task sequences of all ants in the right partition are obtained, individual extremum values of ant colony in the left partition and the right partition are obtained
Figure SMS_406
、/>
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Individual pole Value-corresponding mobile task sequence
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、/>
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Global extremum->
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、/>
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A mobile task sequence corresponding to the global extremum +.>
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、
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And updating.
S138: iterative equation by ant colony pheromone
Figure SMS_413
Updating the information concentration of the track; />
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and />
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Representing the movement task +.for the t-th and t+1-th iterations, respectively>
Figure SMS_415
To move task->
Figure SMS_416
Pheromone concentration,/->
Figure SMS_419
Representing pheromone volatilization factors; />
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For the t-th iteration, move task +.>
Figure SMS_414
To move task->
Figure SMS_418
New pheromone concentration; the number of iterations t=t+1.
S139: repeating steps S135 to S138 until the number of repetitions reaches the set maximum number of iterations
Figure SMS_422
。
S1310: taking the moving task sequence corresponding to the individual extremum of the left partition as the optimal moving task sequence of the left partition; and taking the moving task sequence corresponding to the individual extremum of the right partition as the optimal moving task sequence of the right partition.
Wherein the state transition probability
Figure SMS_424
，/>
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Indicate->
Figure SMS_434
Ant only from the movement task->
Figure SMS_425
A set of mobile tasks that can be accessed; />
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Representing the slave movement task->
Figure SMS_432
To move task->
Figure SMS_435
Is>
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Visibility of (i.e.)>
Figure SMS_427
，/>
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Representing movement task->
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Starting point to move task->
Figure SMS_426
Is the distance of the end point of (2); />
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Representing the relative importance of the track; />
Figure SMS_431
Indicating the relative importance of visibility.
In the first stage scheduling algorithm, a sequence of mobile tasks to be operated
Figure SMS_448
And a specific movement task sequence->
Figure SMS_439
When the cross operation is performed, the task sequence is moved>
Figure SMS_444
、/>
Figure SMS_452
Each comprising M mobile tasks; randomly generating two non-equal positive integers +.>
Figure SMS_454
、/>
Figure SMS_453
，/>
Figure SMS_455
Get the movement task sequence->
Figure SMS_447
The upper index is +.>
Figure SMS_451
To->
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Between mobile task segments->
Figure SMS_442
Move task sequence->
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Go up and->
Figure SMS_443
The movement task segment at the same position is removed and +.>
Figure SMS_446
Splicing the rest mobile task fragments to obtain a mobile task fragment +.>
Figure SMS_450
Finally, move task segment->
Figure SMS_437
Splice to Mobile task segment->
Figure SMS_441
Terminal, and thus get the sequence of tasks with movement +.>
Figure SMS_445
Corresponding new movement task sequence->
Figure SMS_449
。
In the first stage scheduling algorithm, a sequence of mobile tasks to be operated
Figure SMS_456
When mutation operation is performed, the task sequence is moved>
Figure SMS_461
、/>
Figure SMS_462
Each comprising M mobile tasks; randomly growTwo unequal positive integers +.>
Figure SMS_458
、/>
Figure SMS_460
，/>
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Will->
Figure SMS_466
Index +.>
Figure SMS_457
and />
Figure SMS_459
Transposition is performed on the two mobile tasks of (2) to obtain AND +.>
Figure SMS_464
Corresponding new movement task sequence->
Figure SMS_465
。
The second stage scheduling algorithm based on the improved simulated annealing algorithm specifically comprises the following steps:
s21, initializing parameters of a second-stage scheduling algorithm: setting an initial temperature
Figure SMS_467
Stop temperature->
Figure SMS_468
Temperature decay Rate->
Figure SMS_469
Probability of mutation->
Figure SMS_470
The method comprises the steps of carrying out a first treatment on the surface of the Setting the number of internal cycles per unit temperature +. >
Figure SMS_471
The method comprises the steps of carrying out a first treatment on the surface of the Setting the current temperature +.>
Figure SMS_472
；
S22: randomly distributing to-be-sequenced cross-region mobile tasks to two
Figure SMS_473
Obtaining an initial solution; solving, namely a moving task sequence of a cross-region moving task;
s23: judging the current temperature
Figure SMS_474
Whether or not is greater than->
Figure SMS_475
The method comprises the steps of carrying out a first treatment on the surface of the If yes, go to step S24, if no, go to step S210;
s24: constructing four ways of searching the neighborhood solution, and searching the neighborhood solution for the initial solution according to the ways of executing the four ways of searching the neighborhood solution with equal probability according to the internal circulation times LL at the preset unit temperature; then according to the setting
Figure SMS_476
The rule judges to reserve new solutions or old solutions, and a plurality of solutions obtained through internal circulation at the current temperature are obtained;
s25: obtaining a plurality of solutions through internal circulation at the current temperature, calculating respective fitness values, taking two solutions with the minimum fitness values for performing cross operation, and calculating the fitness values of new solutions obtained after the cross operation; judging whether the fitness value of the new solution is smaller than the minimum fitness value of each solution at the current temperature, if so, taking the new solution as the optimal solution at the current temperature; if not, taking the solution with the minimum fitness value at the current temperature as the optimal solution at the current temperature;
s26: performing cross operation on the optimal solution at the current temperature and the optimal solution at the previous temperature, calculating the fitness value of the new solution after the cross, judging whether the fitness value of the new solution is smaller than the fitness value of the optimal solution at the current temperature, and if so, taking the new solution as the optimal solution at the current temperature; if not, keeping the optimal solution at the current temperature unchanged;
S27: taking outThe optimal solution at the current temperature is according to the set variation probability
Figure SMS_477
Executing the mutation operation, calculating the fitness value of the new solution after executing the mutation operation, judging whether the fitness value of the new solution is smaller than the fitness value of the optimal solution at the current temperature, and if so, taking the new solution as the optimal solution at the current temperature; if not, keeping the optimal solution at the current temperature unchanged;
s28: judging whether the fitness value of the optimal solution at the current temperature is smaller than that of the optimal solution at the previous temperature, if so, keeping the optimal solution at the current temperature as a final optimal solution; if not, taking the optimal solution at the previous temperature as the optimal solution finally obtained at the current temperature;
s29: performing a cooling process, i.e.
Figure SMS_478
Jumping to the step S24, and taking the optimal solution finally obtained at the current temperature as the initial solution in the step S24;
s210: for the current temperature
Figure SMS_479
And decoding the finally obtained optimal solution to obtain an optimal movement task sequence of the transregional movement task.
The set Metropolis rule is as follows:
Figure SMS_490
a sequence of mobile tasks representing an old solution, +.>
Figure SMS_483
A sequence of mobile tasks representing a new solution, +.>
Figure SMS_486
A sequence of mobile tasks representing the current solution, +.>
Figure SMS_492
Fitness value representing old solution, +. >
Figure SMS_496
Fitness value representing the new solution, +.>
Figure SMS_497
An fitness value representing a current solution; according to the calculation mode of fitness value function, find +.>
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and />
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Difference>
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I.e. +.>
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The method comprises the steps of carrying out a first treatment on the surface of the If->
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Then accept the new solution, i.e. the current solution +.>
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，/>
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The method comprises the steps of carrying out a first treatment on the surface of the If condition A is satisfied, a new solution is accepted, i.e.)>
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，/>
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The method comprises the steps of carrying out a first treatment on the surface of the If the condition A is not satisfied, the old solution is retained, i.e. +.>
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，
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The method comprises the steps of carrying out a first treatment on the surface of the Wherein condition a is: />
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And->
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，/>
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Representing random numbers uniformly distributed within the interval (0, 1).
When the second stage scheduling algorithm performs the cross operation: mobile task sequence to be operated
Figure SMS_518
And a specific movement task sequence->
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When the cross operation is performed, the task sequence is moved>
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、/>
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All comprising M movement tasks, three non-equal positive integers are randomly generated +.>
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、/>
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、/>
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，/>
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Get the movement task sequence->
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Go up the index->
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To->
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Is->
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Index number->
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To->
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Is->
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The method comprises the steps of carrying out a first treatment on the surface of the Move task sequence->
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Top and Mobile task segment->
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、
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The same-position mobile task segment is removed and +.>
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The rest segments are spliced to obtain a mobile task segment +.>
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Finally, move task segment->
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、/>
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Splice to Mobile task segment->
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Terminal, get the sequence of movement tasks to be operated +.>
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Corresponding new movement task sequence->
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。
When the mutation operation is carried out in the second stage scheduling algorithm: with a sequence of mobile tasks
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For example, two non-equal positive integers +.>
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、/>
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Moving task sequence->
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Comprising M mobile tasks->
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，/>
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Index as
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、/>
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Is transposed by the mobile task of (2) and +.>
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The coding of the third layer of (2) is modified, i.e./i>
Figure SMS_532
The RGV number of (2) is replaced by the number of another RGV, thereby obtaining a new movement task sequence +.>
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。
The invention adopts a two-stage scheduling algorithm to carry out staged processing on the NP-hard problem with higher complexity (the problem that all NP problems can be reduced in the polynomial time complexity), is suitable for dynamic partition and improves the effect. The two stages design a coding mode, a fitness function and an adaptive algorithm to solve. The method is suitable for dynamic partitioning of various layout scenes, and does not need to be customized and developed for different layout scenes. The utilization rate of the two RGVs is more balanced, and the occurrence of an over-busy or over-idle state of the RGVs is avoided.
The flow of the two-stage scheduling algorithm of the invention is shown in figure 1.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a single embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to specific embodiments, and that the embodiments may be combined appropriately to form other embodiments that will be understood by those skilled in the art.

Claims (10)

1. A one-rail double-vehicle scheduling method based on a two-stage dynamic partitioning algorithm is used for optimizing the total time of moving tasks of two RGVs on the same track and outputting a corresponding optimal moving task sequence; the sequence of the mobile tasks is the execution sequence of the mobile tasks; the one-rail double-vehicle scheduling method comprises the following steps:
s1, partitioning a mobile task, wherein an optimization target is to minimize the execution time difference of a mobile task set of two partitions, and the execution sequence of the mobile task of the two partitions is determined; the starting point and the end point of each mobile task are called RGV point positions, and specifically include:
s11: according to a certain RGV point position
Figure QLYQS_1
Dividing the track to form a left partition and a right partition>
Figure QLYQS_2
Representing a set of all RGV points; the moving task whose starting point and end point are both located in the left partition is marked as a moving task set +. >
Figure QLYQS_3
The method comprises the steps of carrying out a first treatment on the surface of the The mobile task whose starting point and end point are both located in the right partition is marked as the mobile task set +.>
Figure QLYQS_4
；
S12: by first-stage scheduling algorithm pair based on ant colony-genetic-particle swarm algorithm
Figure QLYQS_6
and />
Figure QLYQS_9
The mobile tasks in the left partition are sequenced and the mobile task sequences of the left partition are respectively output>
Figure QLYQS_11
And the movement task sequence of the right partition +.>
Figure QLYQS_7
，/>
Figure QLYQS_8
and />
Figure QLYQS_10
Respectively make
Figure QLYQS_12
and />
Figure QLYQS_5
The total moving distance of the middle moving task is shortest;
s13: calculating current RGV point positions respectively
Figure QLYQS_13
Corresponding->
Figure QLYQS_14
and />
Figure QLYQS_15
Execution time of->
Figure QLYQS_16
and />
Figure QLYQS_17
Calculating an execution time difference
Figure QLYQS_18
；
S14: repeating steps S11 to S13 until completion
Figure QLYQS_19
Selecting all RGV points; selecting the smallest execution time difference
Figure QLYQS_20
The corresponding RGV-bit is taken as the optimal partition-bit +.>
Figure QLYQS_21
And the smallest execution time difference +.>
Figure QLYQS_22
Corresponding->
Figure QLYQS_23
and />
Figure QLYQS_24
Respectively used as the optimal moving task sequence of the left partition and the optimal moving task sequence of the right partition;
s2, obtaining an optimal moving task sequence of the cross-region moving task through a second-stage scheduling algorithm based on an improved simulated annealing algorithm; wherein the cross-zone movementThe starting point and the end point of the task are respectively positioned at the optimal partition point positions
Figure QLYQS_25
A movement task on both sides; when the second stage scheduling algorithm is executed, only one RGV can execute the mobile task at the same time, and the RGV which does not execute the mobile task is avoided from being out of the mobile area of the RGV which executes the mobile task.
2. The two-stage dynamic partitioning algorithm based one-rail two-vehicle scheduling method as set forth in claim 1, wherein: in step S12, the moving task sequence of the left partition is output by the first stage scheduling algorithm based on the ant colony-genetic-particle swarm algorithm
Figure QLYQS_26
And the movement task sequence of the right partition +.>
Figure QLYQS_27
When the method comprises the following steps:
s131: randomly selecting the left partition and the right partition respectively
Figure QLYQS_28
A sequence of mobile tasks, calculating left partition +.>
Figure QLYQS_29
The respective fitness value, right partition +.>
Figure QLYQS_30
The respective fitness values of the respective mobile task sequences;
s132: respectively selecting from left partition and right partition
Figure QLYQS_31
A moving task sequence B with the shortest total moving distance,
Figure QLYQS_32
calculate eachThe fitness value corresponding to the moving task sequence takes the fitness value of each moving task sequence B as the initial pheromone concentration of any two adjacent moving tasks in the moving task sequence B during ant colony search;
s133: taking the minimum value of the adaptation degree value of the moving task sequence of the left partition in the step S131 as the individual extremum of the left partition
Figure QLYQS_34
The corresponding movement task sequence is +.>
Figure QLYQS_36
The method comprises the steps of carrying out a first treatment on the surface of the Taking the minimum value of the right partition moving task sequence fitness value in the step S131 as the individual extremum +. >
Figure QLYQS_38
The corresponding movement task sequence is +.>
Figure QLYQS_35
The method comprises the steps of carrying out a first treatment on the surface of the Calculating the fitness values of all the mobile task sequences of the left partition and the right partition, and taking the minimum fitness value as the global extremum +.>
Figure QLYQS_37
And global extremum of right partition->
Figure QLYQS_39
Global extremum->
Figure QLYQS_40
The corresponding sequence of movement tasks is marked +.>
Figure QLYQS_33
Global extremum
Figure QLYQS_41
The corresponding sequence of movement tasks is marked +.>
Figure QLYQS_42
；
S134: initializing the iteration times t=0;
s135: in the left partition
Figure QLYQS_54
Randomly selecting +.>
Figure QLYQS_44
A plurality of movement tasks are allocated to ∈>
Figure QLYQS_51
Ants; for->
Figure QLYQS_49
Ant only, will be->
Figure QLYQS_52
The mobile task allocated by ant is placed in the corresponding task sequence solution set to calculate +.>
Figure QLYQS_56
Only ants are moved by the current task->
Figure QLYQS_58
To next movement task->
Figure QLYQS_53
State transition probability>
Figure QLYQS_59
And according to->
Figure QLYQS_46
Acquire next movement task->
Figure QLYQS_48
The movement task is further->
Figure QLYQS_45
Is placed at->
Figure QLYQS_50
Only the task sequence corresponding to ants is concentrated; when the mobile tasks to be ordered are all placed in the task sequence solution set, the +.>
Figure QLYQS_55
Mobile task sequence explored by ants only ∈>
Figure QLYQS_57
The method comprises the steps of carrying out a first treatment on the surface of the Computing the movement task sequence->
Figure QLYQS_43
Is>
Figure QLYQS_47
；
In the right partition
Figure QLYQS_67
Randomly selecting +.>
Figure QLYQS_62
A plurality of movement tasks are allocated to ∈>
Figure QLYQS_65
Ants; for- >
Figure QLYQS_66
Ant only, will be->
Figure QLYQS_68
The mobile task allocated by ant is placed in the corresponding task sequence solution set to calculate +.>
Figure QLYQS_69
Only ants are moved by the current task->
Figure QLYQS_72
To next movement task->
Figure QLYQS_73
State transition probability>
Figure QLYQS_76
And according to->
Figure QLYQS_60
Acquire next movement task->
Figure QLYQS_75
The movement task is further->
Figure QLYQS_63
Is placed at->
Figure QLYQS_70
Only the task sequence corresponding to ants is concentrated; when the mobile tasks to be ordered are all placed in the task sequence solution set, the +.>
Figure QLYQS_71
Mobile task sequence explored by ants only ∈>
Figure QLYQS_74
The method comprises the steps of carrying out a first treatment on the surface of the Computing the movement task sequence->
Figure QLYQS_61
Is>
Figure QLYQS_64
；
S136, for left partition: moving task sequences
Figure QLYQS_86
And->
Figure QLYQS_79
Performing cross operation to obtain a moving task sequence +.>
Figure QLYQS_81
，/>
Figure QLYQS_88
And->
Figure QLYQS_92
Performing the cross operation again to obtain a moving task sequence +.>
Figure QLYQS_90
And then (2) to->
Figure QLYQS_93
Performing mutation operation to obtain a mobile task sequence->
Figure QLYQS_87
Calculating the execution movement task sequence->
Figure QLYQS_91
Is>
Figure QLYQS_77
The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
Figure QLYQS_85
Compared with->
Figure QLYQS_80
Smaller, accept the movement task sequence +.>
Figure QLYQS_82
The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
Figure QLYQS_84
Compared with->
Figure QLYQS_89
Not get smaller, then->
Figure QLYQS_78
The corresponding moving task sequence of ants is still +.>
Figure QLYQS_83
；
For the right partition: moving task sequences
Figure QLYQS_105
And->
Figure QLYQS_96
Performing cross operation to obtain a moving task sequence +.>
Figure QLYQS_102
，/>
Figure QLYQS_104
And (3) with
Figure QLYQS_109
Performing the cross operation again to obtain a moving task sequence +.>
Figure QLYQS_108
And then (2) to- >
Figure QLYQS_110
Performing mutation operation to obtain a mobile task sequence->
Figure QLYQS_103
Calculating the execution movement task sequence->
Figure QLYQS_107
Is>
Figure QLYQS_94
The method comprises the steps of carrying out a first treatment on the surface of the If the fitness value/>
Figure QLYQS_99
Compared with->
Figure QLYQS_97
Smaller, accept the movement task sequence +.>
Figure QLYQS_98
The method comprises the steps of carrying out a first treatment on the surface of the If fitness value->
Figure QLYQS_101
Compared with->
Figure QLYQS_106
Not get smaller, then->
Figure QLYQS_95
The corresponding moving task sequence of ants is still +.>
Figure QLYQS_100
；
S137: after the moving task sequences of all ants in the left partition and the moving task sequences of all ants in the right partition are obtained, individual extremum values of ant colony in the left partition and the right partition are obtained
Figure QLYQS_112
、/>
Figure QLYQS_115
A movement task sequence corresponding to the extremum of the individual +.>
Figure QLYQS_117
、
Figure QLYQS_113
Global extremum->
Figure QLYQS_114
、/>
Figure QLYQS_116
A mobile task sequence corresponding to the global extremum +.>
Figure QLYQS_118
、/>
Figure QLYQS_111
Updating;
s138: iterative equation by ant colony pheromone
Figure QLYQS_119
Updating the information concentration between the mobile tasks; />
Figure QLYQS_124
and />
Figure QLYQS_126
Representing the movement task +.for the t-th and t+1-th iterations, respectively>
Figure QLYQS_121
To move task->
Figure QLYQS_123
Pheromone concentration,/->
Figure QLYQS_125
Representing pheromone volatilization factors; />
Figure QLYQS_127
For the t-th iteration, move task +.>
Figure QLYQS_120
To move task->
Figure QLYQS_122
New pheromone concentration; the number of iterations t=t+1;
s139: repeating steps S135 to S138, until the repetition number reaches the set maximum iteration number
Figure QLYQS_128
；
S1310: taking the moving task sequence corresponding to the individual extremum of the left partition as the optimal moving task sequence of the left partition; and taking the moving task sequence corresponding to the individual extremum of the right partition as the optimal moving task sequence of the right partition.
3. The two-stage dynamic partitioning algorithm based one-rail two-vehicle scheduling method as set forth in claim 2, wherein: probability of state transition
Figure QLYQS_131
，/>
Figure QLYQS_135
Indicate->
Figure QLYQS_140
Ant only from the movement task->
Figure QLYQS_130
A set of mobile tasks that can be accessed; />
Figure QLYQS_133
Representing the slave movement task->
Figure QLYQS_136
To move task->
Figure QLYQS_139
Is>
Figure QLYQS_129
Visibility of (i.e.)>
Figure QLYQS_134
，/>
Figure QLYQS_137
Representing movement task->
Figure QLYQS_141
Starting point to move task->
Figure QLYQS_132
Is the distance of the end point of (2); />
Figure QLYQS_138
Representing the relative importance of the track; />
Figure QLYQS_142
Indicating the relative importance of visibility.
4. The two-stage dynamic partitioning algorithm based one-rail two-vehicle scheduling method as set forth in claim 2, wherein: in step S13, a sequence of mobile tasks to be operated
Figure QLYQS_151
And a specific movement task sequence->
Figure QLYQS_144
When the cross operation is performed, the task sequence is moved>
Figure QLYQS_148
、/>
Figure QLYQS_158
Each comprising M mobile tasks; randomly generating two non-equal positive integers +.>
Figure QLYQS_160
、/>
Figure QLYQS_159
，/>
Figure QLYQS_161
Get the movement task sequence->
Figure QLYQS_152
The upper index is +.>
Figure QLYQS_156
To->
Figure QLYQS_143
Between mobile task segments->
Figure QLYQS_150
Move task sequence->
Figure QLYQS_146
Go up and->
Figure QLYQS_147
The movement task segment at the same position is removed and +.>
Figure QLYQS_153
The rest mobile task segments are spliced to obtain the mobile task segments
Figure QLYQS_155
Finally, move task segment->
Figure QLYQS_145
Splice to Mobile task segment->
Figure QLYQS_149
Terminal, and thus get the sequence of tasks with movement +. >
Figure QLYQS_154
Corresponding new movement task sequence->
Figure QLYQS_157
。
5. The two-stage dynamic partitioning algorithm based one-rail two-vehicle scheduling method as set forth in claim 2, wherein: in step S13, a sequence of mobile tasks to be operated
Figure QLYQS_162
When mutation operation is performed, the task sequence is moved>
Figure QLYQS_165
、/>
Figure QLYQS_168
All comprising M movement tasks, randomly generating two unequal positive integers +.>
Figure QLYQS_164
、/>
Figure QLYQS_167
，/>
Figure QLYQS_170
Will->
Figure QLYQS_172
Index +.>
Figure QLYQS_163
and />
Figure QLYQS_166
Transposition is performed on the two mobile tasks of (2) to obtain AND +.>
Figure QLYQS_169
Corresponding new movement task sequence->
Figure QLYQS_171
。
6. According to claim 2The one-rail double-vehicle scheduling method based on the two-stage dynamic partitioning algorithm is characterized by comprising the following steps of: in step S13, the adaptability value of the left partition movement task sequence
Figure QLYQS_173
The method comprises the following steps:
Figure QLYQS_174
；
wherein
Figure QLYQS_175
Moving the total number of tasks for the left partition; />
Figure QLYQS_178
Is->
Figure QLYQS_180
Start of individual movement tasks, +.>
Figure QLYQS_177
Is->
Figure QLYQS_179
End point of each movement task->
Figure QLYQS_181
Is->
Figure QLYQS_182
and />
Figure QLYQS_176
Is a distance of (2);
fitness value of right partition movement task sequence
Figure QLYQS_183
The method comprises the following steps:
Figure QLYQS_184
；
wherein
Figure QLYQS_185
The total number of tasks is moved for the right partition.
7. The two-stage dynamic partitioning algorithm-based one-rail two-vehicle scheduling method as set forth in claim 1, wherein the second stage scheduling algorithm based on the improved simulated annealing algorithm specifically includes the steps of:
S21, initializing parameters of a second-stage scheduling algorithm: setting an initial temperature
Figure QLYQS_186
Stop temperature->
Figure QLYQS_187
Temperature decay Rate->
Figure QLYQS_188
Probability of mutation->
Figure QLYQS_189
The method comprises the steps of carrying out a first treatment on the surface of the Setting the number of internal cycles per unit temperature +.>
Figure QLYQS_190
The method comprises the steps of carrying out a first treatment on the surface of the Setting the current temperature +.>
Figure QLYQS_191
；
S22: randomly distributing to-be-sequenced cross-region mobile tasks to two
Figure QLYQS_192
Obtaining an initial solution; solving, namely a moving task sequence of a cross-region moving task;
s23: judging the current temperature
Figure QLYQS_193
Whether or not is greater than->
Figure QLYQS_194
The method comprises the steps of carrying out a first treatment on the surface of the If yes, go to step S24, if no, go to step S210;
s24: constructing four ways of searching the neighborhood solution, and searching the neighborhood solution for the initial solution according to the ways of executing the four ways of searching the neighborhood solution with equal probability according to the internal circulation times LL at the preset unit temperature; then according to the setting
Figure QLYQS_195
The rule judges to reserve new solutions or old solutions, and a plurality of solutions obtained through internal circulation at the current temperature are obtained;
s25: obtaining a plurality of solutions through internal circulation at the current temperature, calculating respective fitness values, taking two solutions with the minimum fitness values for performing cross operation, and calculating the fitness values of new solutions obtained after the cross operation; judging whether the fitness value of the new solution is smaller than the minimum fitness value of each solution at the current temperature, if so, taking the new solution as the optimal solution at the current temperature; if not, taking the solution with the minimum fitness value at the current temperature as the optimal solution at the current temperature;
S26: performing cross operation on the optimal solution at the current temperature and the optimal solution at the previous temperature, calculating the fitness value of the new solution after the cross, judging whether the fitness value of the new solution is smaller than the fitness value of the optimal solution at the current temperature, and if so, taking the new solution as the optimal solution at the current temperature; if not, keeping the optimal solution at the current temperature unchanged;
s27: taking the optimal solution at the current temperature, and according to the set variation probability
Figure QLYQS_196
Executing the mutation operation, calculating the fitness value of the new solution after executing the mutation operation, judging whether the fitness value of the new solution is smaller than the fitness value of the optimal solution at the current temperature, and if so, taking the new solution as the optimal solution at the current temperature; if not, keeping the optimal solution at the current temperature unchanged;
s28: judging whether the fitness value of the optimal solution at the current temperature is smaller than that of the optimal solution at the previous temperature, if so, keeping the optimal solution at the current temperature as a final optimal solution; if not, taking the optimal solution at the previous temperature as the optimal solution finally obtained at the current temperature;
s29: performing a cooling process, i.e.
Figure QLYQS_197
Jumping to the step S24, and taking the optimal solution finally obtained at the current temperature as the initial solution in the step S24;
S210: for the current temperature
Figure QLYQS_198
And decoding the finally obtained optimal solution to obtain an optimal movement task sequence of the transregional movement task.
8. The two-stage dynamic partitioning algorithm based one-rail two-vehicle scheduling method as set forth in claim 7, wherein the set metapolis rule is:
Figure QLYQS_210
a sequence of mobile tasks representing an old solution, +.>
Figure QLYQS_201
A sequence of mobile tasks representing a new solution, +.>
Figure QLYQS_205
A sequence of mobile tasks representing the current solution, +.>
Figure QLYQS_215
Fitness value representing old solution, +.>
Figure QLYQS_216
Fitness value representing the new solution, +.>
Figure QLYQS_214
Indicating the suitability of the current solutionA fitness value; according to the calculation mode of fitness value function, find +.>
Figure QLYQS_218
and />
Figure QLYQS_206
Difference>
Figure QLYQS_209
I.e.
Figure QLYQS_199
The method comprises the steps of carrying out a first treatment on the surface of the If->
Figure QLYQS_204
Then accept the new solution, i.e. the current solution +.>
Figure QLYQS_202
，/>
Figure QLYQS_203
The method comprises the steps of carrying out a first treatment on the surface of the If condition A is satisfied, a new solution is accepted, i.e.)>
Figure QLYQS_208
，/>
Figure QLYQS_211
The method comprises the steps of carrying out a first treatment on the surface of the If the condition A is not satisfied, the old solution is retained, i.e. +.>
Figure QLYQS_207
，/>
Figure QLYQS_212
The method comprises the steps of carrying out a first treatment on the surface of the Wherein condition a is: />
Figure QLYQS_213
And->
Figure QLYQS_217
，/>
Figure QLYQS_200
Representing random numbers uniformly distributed within the interval (0, 1).
9. The two-stage dynamic partitioning algorithm-based one-rail two-vehicle scheduling method as set forth in claim 1, wherein when the cross operation is performed in the second stage scheduling algorithm: mobile task sequence to be operated
Figure QLYQS_222
And a specific sequence of mobile tasks
Figure QLYQS_226
When the cross operation is performed, the task sequence is moved >
Figure QLYQS_230
、/>
Figure QLYQS_220
All comprising M movement tasks, three non-equal positive integers are randomly generated +.>
Figure QLYQS_223
、/>
Figure QLYQS_229
、/>
Figure QLYQS_232
，/>
Figure QLYQS_221
Get the movement task sequence->
Figure QLYQS_225
Go up the index->
Figure QLYQS_228
To->
Figure QLYQS_234
Mobile task segment of (a)
Figure QLYQS_235
Index number->
Figure QLYQS_238
To->
Figure QLYQS_240
Is->
Figure QLYQS_242
The method comprises the steps of carrying out a first treatment on the surface of the Move task sequence->
Figure QLYQS_236
Top and Mobile task segment->
Figure QLYQS_239
、/>
Figure QLYQS_241
The same-position mobile task segment is removed and +.>
Figure QLYQS_243
The rest segments are spliced to obtain a mobile task segment +.>
Figure QLYQS_219
Finally, move task segment->
Figure QLYQS_224
、/>
Figure QLYQS_227
Splice to Mobile task segment->
Figure QLYQS_231
Terminal, get the sequence of movement tasks to be operated +.>
Figure QLYQS_233
Corresponding new movement task sequence->
Figure QLYQS_237
。
10. The two-stage dynamic partitioning algorithm based one-rail two-vehicle scheduling method as set forth in claim 7, wherein in the second stage scheduling algorithm, the fitness value of the moving task sequence is
Figure QLYQS_244
：
Figure QLYQS_245
；
wherein ,
Figure QLYQS_258
for the total number of cross-zone movement tasks, +.>
Figure QLYQS_246
Is->
Figure QLYQS_253
Start of individual movement tasks, +.>
Figure QLYQS_249
Is->
Figure QLYQS_252
End point of each movement task->
Figure QLYQS_254
Is->
Figure QLYQS_261
and />
Figure QLYQS_257
Distance of->
Figure QLYQS_259
Is->
Figure QLYQS_248
A number of RGV assigned by each mobile task; />
Figure QLYQS_251
Indicate->
Figure QLYQS_262
The RGV point position set which is required to pass through by the mobile task and the current position of another RGV have intersection or not, if yes, 1 is taken, and if not, 0 is taken; />
Figure QLYQS_264
Indicate->
Figure QLYQS_263
The union of the RGV point position set required to pass by the mobile task and the RGV current position for executing the i mobile task to the i mobile task starting point is provided with or not with the other RGV current position, wherein the intersection is 1, and the non-intersection is 0;
Figure QLYQS_265
Indicate->
Figure QLYQS_250
Whether the direction of each mobile task is matched with the relative position of the RGV for executing the ith mobile task or not, taking 1 in a matching way, and taking 0 in a non-matching way; />
Figure QLYQS_255
Representing execution of +.>
Figure QLYQS_256
Real-time location of RGVs of the individual mobile tasks; />
Figure QLYQS_260
Indicating that no->
Figure QLYQS_247
Real-time location of RGV for each mobile task.
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Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170270466A1 (en) * 2016-03-18 2017-09-21 Jusda International Logistics (TAIWAN) CO.,LTD Method and system using with automated guided vehicle
US20190196456A1 (en) * 2017-12-27 2019-06-27 Tokyo Electron Limited Data communication method for reducing working time of automated material handling system
CN110471287A (en) * 2019-08-27 2019-11-19 合肥工业大学 A kind of real-time scheduling method and system of more rail Shuttling trolleys
CN110837950A (en) * 2019-10-22 2020-02-25 山东女子学院 Dynamic scheduling method of RGV (traffic volume vector)
CN111459108A (en) * 2020-04-08 2020-07-28 北京理工大学 Task allocation and conflict-free path planning method for pull-type multi-AGV system
CN112633582A (en) * 2020-12-29 2021-04-09 中机中联工程有限公司 Multi-station multi-RGV intelligent flexible dynamic scheduling system
CN112629537A (en) * 2020-12-11 2021-04-09 华晟（青岛）智能装备科技有限公司 Method and system for dynamically selecting conveying route
CN112950060A (en) * 2021-03-26 2021-06-11 北京京东乾石科技有限公司 Scheduling method and device of annular RGV, electronic equipment and storage medium
CN114706395A (en) * 2022-04-02 2022-07-05 安歌科技(集团)股份有限公司 Four-way shuttle RGV cluster scheduling system
CN114936783A (en) * 2022-06-02 2022-08-23 暨南大学 RGV (vehicle target volume) trolley scheduling method and system based on MMDDPG (multimedia messaging service data distribution group) algorithm
WO2022174483A1 (en) * 2021-02-22 2022-08-25 广东鑫光智能系统有限公司 Serial scheduling algorithm-based rgv scheduling method
CN115032952A (en) * 2022-05-13 2022-09-09 景德镇陶瓷大学 Marine weight-related part processing production workshop integrated scheduling differential evolution control method, device and system based on differentiation transfer strategy
US20220391813A1 (en) * 2021-06-02 2022-12-08 Lineage Logistics, LLC Swapping task assignments to determine task selection
CN115963797A (en) * 2023-01-31 2023-04-14 三一机器人装备(西安)有限公司 Scheduling method, system, RGV, electronic device and computer medium

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170270466A1 (en) * 2016-03-18 2017-09-21 Jusda International Logistics (TAIWAN) CO.,LTD Method and system using with automated guided vehicle
US20190196456A1 (en) * 2017-12-27 2019-06-27 Tokyo Electron Limited Data communication method for reducing working time of automated material handling system
CN110471287A (en) * 2019-08-27 2019-11-19 合肥工业大学 A kind of real-time scheduling method and system of more rail Shuttling trolleys
CN110837950A (en) * 2019-10-22 2020-02-25 山东女子学院 Dynamic scheduling method of RGV (traffic volume vector)
CN111459108A (en) * 2020-04-08 2020-07-28 北京理工大学 Task allocation and conflict-free path planning method for pull-type multi-AGV system
CN112629537A (en) * 2020-12-11 2021-04-09 华晟（青岛）智能装备科技有限公司 Method and system for dynamically selecting conveying route
CN112633582A (en) * 2020-12-29 2021-04-09 中机中联工程有限公司 Multi-station multi-RGV intelligent flexible dynamic scheduling system
WO2022174483A1 (en) * 2021-02-22 2022-08-25 广东鑫光智能系统有限公司 Serial scheduling algorithm-based rgv scheduling method
CN112950060A (en) * 2021-03-26 2021-06-11 北京京东乾石科技有限公司 Scheduling method and device of annular RGV, electronic equipment and storage medium
US20220391813A1 (en) * 2021-06-02 2022-12-08 Lineage Logistics, LLC Swapping task assignments to determine task selection
CN114706395A (en) * 2022-04-02 2022-07-05 安歌科技(集团)股份有限公司 Four-way shuttle RGV cluster scheduling system
CN115032952A (en) * 2022-05-13 2022-09-09 景德镇陶瓷大学 Marine weight-related part processing production workshop integrated scheduling differential evolution control method, device and system based on differentiation transfer strategy
CN114936783A (en) * 2022-06-02 2022-08-23 暨南大学 RGV (vehicle target volume) trolley scheduling method and system based on MMDDPG (multimedia messaging service data distribution group) algorithm
CN115963797A (en) * 2023-01-31 2023-04-14 三一机器人装备(西安)有限公司 Scheduling method, system, RGV, electronic device and computer medium

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
LUO SHUYU: "RGV optimal schedualing scheme in multi-process scenario", 《ADVANCES IN MATERIALS》, pages 1 - 10 *
ZISHI WANG 等: "An ant colony optimizaiton-simulated anealing algorithm for solving a multiload AGVs workshop schedualing problem with limited buffer capacity", 《PROCESSES》, pages 1 - 20 *
陈华: "基于分区法的2-RGV调度问题的模型和算法", 《工业工程与管理》, pages 70 - 76 *

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