CN114462764A - Dispatching method of multilayer multi-port hoister - Google Patents
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Abstract
A dispatching method of a multilayer multi-port hoister comprises the following steps: acquiring a calling call task; if only one group of calling tasks exists, directly executing the calling tasks; if the number of the calling tasks is more than 1 and not more than M, determining the execution sequence of each calling task by adopting a full-array algorithm with the shortest total length of the receiving distance as a target, and executing a plurality of groups of calling tasks according to the determined execution sequence, wherein M is equal to 5 or 6; if the number of the calling tasks is larger than M, the shortest total length of the receiving distance is taken as an optimization target, the execution sequence of the calling tasks is optimized by adopting a self-adaptive genetic algorithm, and a plurality of groups of calling tasks are executed according to the optimized execution sequence. The invention can realize the high-efficiency distribution of the elevator calling tasks of the elevator and improve the operation efficiency of the multi-layer and multi-port elevator.
Description
Technical Field
The invention relates to the technical field of logistics, in particular to a scheduling method of a hoister.
Background
With the optimization and upgrading of industrial production and logistics distribution, the efficiency of the logistics elevator becomes a key concern in the industry and logistics industry. The operating efficiency of the hoister is always a pain point of the whole automatic transportation system, the efficiency shortage of intelligent logistics transportation can be effectively compensated by improving the operating efficiency of the hoister, and the whole climbing of the transportation speed of the automatic logistics is realized.
The optimization control of the multilayer multi-port and multi-task hoister is essentially a scheduling problem, namely a problem of optimal solution of a multi-task running path. Common algorithms include genetic algorithm, simulated annealing algorithm, hill climbing algorithm, particle swarm algorithm and the like in the optimal solution method. The simulated annealing algorithm has strong local searching capability and short running time, but lacks global searching capability and is easily influenced by parameters. The hill climbing algorithm is simple in principle and high in efficiency, but the optimal solution cannot be obtained for multi-constraint large-scale problems. The particle swarm algorithm is suitable for solving the mathematical problem, is simple and convenient to calculate, and has the problem of falling into local optimization. The genetic algorithm can better process the constraint, is not limited by local optimum, has strong global search capability, can infinitely approach to a global optimum solution, but has relatively complex algorithm and weak convergence capability.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a dispatching method of a multi-layer and multi-port hoister, which can realize the efficient distribution of the calling tasks of the hoister and improve the operating efficiency of the multi-layer and multi-port hoister.
The embodiment of the invention provides a dispatching method of a multilayer multi-port hoister, which comprises the following steps:
acquiring a calling call task;
if only one group of calling tasks exists, directly executing the calling tasks; if the number of the calling tasks is more than 1 and not more than M, determining the execution sequence of each calling task by adopting a full-permutation algorithm with the shortest total length of the receiving distance as a target, and executing a plurality of groups of calling tasks according to the determined execution sequence, wherein M is equal to 5 or 6; if the number of the calling tasks is larger than M, the shortest total length of the receiving distance is taken as an optimization target, the execution sequence of the calling tasks is optimized by adopting a self-adaptive genetic algorithm, and a plurality of groups of calling tasks are executed according to the optimized execution sequence.
The invention has at least the following advantages:
1. the embodiment of the invention distributes the dispatching algorithm in real time according to the real-time dispatching calling number of tasks, adopts the full-array algorithm when the calling number of tasks is less, adopts the genetic algorithm when the calling number of tasks is more, and adopts the combined algorithm mode to effectively realize the optimal design of control time and efficiency of the dispatching system of the multilayer multi-port multi-task hoister, and solve the problems that the genetic algorithm consumes too long time and affects the overall working efficiency of the hoister when the task number is low;
2. the embodiment of the invention aims at the problems of poor repeated convergence characteristics, serious premature phenomenon, overlarge maximum error and the like of the genetic algorithm in actual control, improves the execution mechanism of the genetic algorithm, fully optimizes the screening of genetic operators, effectively improves the repeated convergence characteristics of the genetic algorithm, reduces the maximum error, reduces the premature probability and meets the actual control requirement.
Drawings
Fig. 1 shows a schematic view of a multi-deck multi-port elevator.
Fig. 2 is a flow chart illustrating a scheduling method of a multi-deck multi-port elevator according to an embodiment of the present invention.
FIG. 3 shows a schematic diagram of the principle of the full-array algorithm.
FIG. 4 shows a schematic diagram of an improved genetic algorithm according to an embodiment of the present invention.
Detailed Description
Fig. 1 shows a schematic view of a multi-deck multi-port elevator. In the example of fig. 1, the elevator is a multi-layer double-port elevator, the double-side design structure of the system is the same, fig. 1 only shows the single-side structure thereof, and the system comprises three parts, namely a transportation end, a conveying end and an operation platform, wherein the transportation end is the inner part of the elevator car, the conveying end is the transition conveying part, and the part is provided with a plurality of conveying sections on site.
The elevator operates in a one-cargo one-elevator mode, and the lift car only transports one piece of standard cargo once. The user inputs freight transportation floor information through the operation panel, and places the goods in the transfer chain, accomplishes the goods and transports floor data and types, and the lifting machine transports the goods to appointed floor according to goods route information.
Assuming that the position of the elevator before operation is a, the coordinates of the starting point and the end point of the n operating tasks are (b)n，cn) All the running paths X of the n tasksLComprises the following steps:
XL＝|a-b1|+|b1-c1|+|c1-b2|+…+|bn-cn|
because the elevator adopts a goods-elevator mode, each task needs to complete a whole set of transportation process of receiving goods, delivering goods and delivering goods, and the operation rhythm of the horizontal conveying line is far greater than the operation rhythm of the elevator, the acceleration and deceleration time and the goods receiving waiting time of the elevator are not considered, and the optimal solution problem of the elevator is considered only from the operation distance.
According to the running model of the hoister, no matter how the scheduling tasks are distributed, the running distance in the task system of the hoister is fixed, and the distance influenced by the scheduling is only the distance from the current goods outlet to the next task goods inlet, so that the best sorting is only needed to be carried out on the total length of the goods distance. Total length of receiving distance Xi:
Xi＝|a-b1|+|c1-b2|+…+|cn-1-bn|。
Referring to fig. 2, a method for scheduling a multi-tier and multi-port elevator according to an embodiment of the present invention includes the following steps:
acquiring a calling call task;
if only one group of calling tasks exists, directly executing the calling tasks; if the number of the calling tasks is more than 1 and not more than M, determining the execution sequence of each calling task by adopting a full-permutation algorithm with the shortest total length of the receiving distance as a target, and executing a plurality of groups of calling tasks according to the determined execution sequence, wherein M is equal to 5 or 6; if the number of the calling tasks is larger than M, the shortest total length of the receiving distance is taken as an optimization target, the execution sequence of the calling tasks is optimized by adopting a self-adaptive genetic algorithm, and a plurality of groups of calling tasks are executed according to the optimized execution sequence.
In a specific implementation manner, the scheduling method of the embodiment establishes a call task preprocessing queue and a task execution queue, and dynamically processes the call task according to the actual operation condition of the elevator after the goods call is successful. When the elevator is in an idle state, after any call command calls in, the call is directly executed without sequencing operation; if the elevator is in a running state, the newly received call request is temporarily stored in a task preprocessing queue, and after the current task of the elevator is finished, the call task is sequenced. The call task ordering is divided into three cases according to the number of tasks:
1. only one group of calling tasks is directly executed;
2. and (4) executing a full-ranking algorithm when the number of the call tasks is more than 1 and not more than 6, and performing 720 maximum ranking calculations with absolute optimality.
3. The number of the calling tasks is more than 6, the calling tasks are sorted by adopting an improved self-adaptive genetic algorithm, and the calculation number is mainly determined by the evolution algebra and the number of the tasks and is relatively optimal.
The full-ranking algorithm generates all possible running paths of all call tasks and then calculates the path distance of all the possible running paths. The basic principle of the full-permutation algorithm is shown in fig. 3, taking three numbers of 0, 1 and 2 as an example, each number represents one element (i.e. a group call task), and the permutation of two elements has 2 possibilities, namely 1, 0/0 and 1; the full queue of three elements has 6 queues, and the origin of the 6 queues is conceivable to insert 2 elements into two known queues, 1, 0/0, 1, in turn, so that as shown in fig. 3, the 6 queues will be: 2,1,0/1,2,0/1,0,2/2,0,1/0,2,1/0,1,2. By analogy, when the fourth element appears, the element is inserted into the 6 groups of queues in sequence, and each group of queues generates 4 groups of new queues, so that 24 groups of new queues can be generated. By analogy, full queue ordering of any bit element can be accomplished. And after the full-row groups of all the calling tasks are completed according to the logic, substituting the path distance, and taking out the optimal solution by using a bubbling algorithm.
The embodiment adopts a sorting mode of a combined algorithm, so that the calculation and execution efficiency of the system can be improved and the optimal solution of the sorting can be ensured at the same time when the number of tasks is low. When the number of tasks is too large, if the full-permutation algorithm is still adopted, huge calculation time is consumed, so that the processing algorithm is reasonably distributed by taking 6 tasks as demarcation points according to calculation items and according to the number of real-time call calling tasks.
In this embodiment, the task of obtaining a call includes:
and putting the received call requests into a task queue as call tasks, wherein the call requests comprise a cargo inlet floor, a cargo outlet floor, a cargo inlet and a cargo outlet.
Please refer to fig. 4. In this embodiment, optimizing the execution sequence of the call tasks by using the adaptive genetic algorithm includes the following steps:
s1, generating an initial population N by using the execution sequence of the group call tasks as a genetic individual in the populationA；
S2, initializing population NAOptimizing to obtain an optimized initial population Nc；
S3, optimizing the initial population NcAnd iteratively carrying out self-adaptive genetic operation until a preset iteration termination condition is met, and outputting an optimal solution when the iteration termination condition is met as an execution sequence of the optimized call tasks.
In this embodiment, the step S1 specifically includes:
generating a chromosome: pi＝(bi,ci) Bi is the goods-in floor of the ith calling task, ci is the goods-out floor of the ith calling task, i is more than or equal to 1 and less than or equal to n, and n is the total number of the calling tasks;
generating genes as the genetic individuals according to the call task ranking: pj’＝{P1,P2…Pn}；
Generating an initial population NA：NA＝{P1’,P2’...Pm' }; and m is the population scale.
In this embodiment, the step S2 specifically includes:
for initial population NAPerforming single genetic operation to obtain new population NBN with the population size of mAAnd N with population size mBMixing and preferentially outputting a new initial population N with the population size of mC。
The preferred output population size is mNew initial population NCThe method comprises the following steps:
calculating the average fitness of the mixed population (the scale of the mixed population is 2m), and allowing the genes with the fitness higher than the average fitness to enter the next generation population NCGenes below the mean fitness are discarded; if the population scale is met in advance (namely m genes are selected), the selection is stopped; and if the total number of the genes to be selected remaining in the mixed population is equal to the total number of the remaining requirements of the new population (namely the number of the genes to be selected remaining is m-the number of the genes which are already selected), stopping selection, and putting all the remaining genes into the new population.
The above-mentioned pair of initial population NAPerforming a single genetic manipulation to generate a population NBThe method specifically comprises the following steps:
fitness F was calculated for each gene:
F＝1/Xi，Xithe total length of the receiving distance is defined as the length, namely the fitness F is equal to the reciprocal of the total length of the receiving distance; i is more than or equal to 1 and less than or equal to n, and n is the total number of the calling tasks;
calculating the hit probability F for each Genep：
selecting next evolved gene by traversing roulette rule, taking the selected gene as parent, obtaining new gene by cross operation, and performing mutation operation on the selected gene to obtain new gene, generating population NB。
The selection of the next evolved gene by the ergodic roulette selection comprises: performing m rounds of selection, randomly generating a random number in the interval of [0, 1] in each round of selection, sequentially comparing each random number with the selection probability of m genes in the population until a gene with the selection probability higher than the random number is found, stopping comparison and selecting the gene to enter the next generation genetic population; in the next round of selection, the selection probability of each gene in the population is compared with the random number in sequence from the next gene of the selected gene in the previous round, and if the last gene in the population is compared and the gene with the selection probability larger than the random number is not found, the first gene in the population is returned to be compared. In the prior art, in each round of selection, the first gene in the population is compared with the random number, and if the gene with the selection probability higher than the random number is found, the comparison is stopped, so that the local optimal solution is easily generated. In the next round of selection, the embodiment compares the next gene of the selected gene in the previous round with the random number, so that the generation of the local optimal solution can be avoided.
In this embodiment, the step S3 specifically includes:
s31, calculating the fitness F of each gene as a genetic individual in the population:
F＝1/Xi，Xithe total length of the receiving distance is defined as the length, namely the fitness F is equal to the reciprocal of the total length of the receiving distance;
s32, calculating the selection probability F of each geneP：
s33, selecting the next evolved gene by traversing double-gene roulette, taking the selected gene as a parent, obtaining a new gene by self-adaptive cross operation, and performing self-adaptive mutation operation on the selected gene to obtain a new gene to generate a new population;
the selection of the next evolved gene by the ergodic double-gene roulette selection comprises the following steps: performing m rounds of selection, randomly generating two random numbers in the interval of [0, 1] in each round of selection, sequentially comparing each random number with the selection probability of m genes in the population until a gene with the selection probability higher than that of the random number is found, stopping comparison and reserving the gene, comparing two genes obtained according to the two random numbers, and selecting the gene with the higher selection probability to enter the next generation genetic population; in the next round of selection, the selection probability of each gene in the population is compared with the random number in sequence from the next gene of the selected gene in the previous round, and if the last gene in the population is compared and the gene with the selection probability larger than the random number is not found, the first gene in the population is returned to be compared.
In operator selection, after a group of individuals is selected nearby, the basic genetic algorithm restarts next individual selection, and only the individuals with relatively high fitness in front of the initial population are easily selected. Therefore, assuming that the size of the initial population is m, the individuals screened by the S-th selection operator are the nth individuals (n < m) of the initial population, the S1-th operator selects the n +1 th individuals of the initial population until all the initial population is screened, and then the screening is traversed again. In the selection calculation of the embodiment, the preference of the selection operator is not directly output only by touching one set of operators meeting the constraint condition, but the first set of excellent operators is maintained, one set of excellent operators is continuously screened, and the two sets of operators are preferentially output, so that the calculation efficiency can be fully utilized.
In this embodiment, the adaptive crossover probability P for adaptive crossover calculationcThe calculation formula of (2) is as follows:
adaptive mutation probability P for adaptive mutation computationmThe calculation formula of (2) is as follows:
wherein: pCTo cross probability, PmF' is the value with higher fitness in the two groups of genes to be crossed as the mutation probability,avgis the mean value of population fitness, fmaxIs the maximum value of population fitness, f is the fitness of the mutated gene, P1、2、k1、k2Are all control parameters, control parameter P1、2、k1、k2All are preset manually.
Adaptive crossover probability P of existing adaptive genetic algorithmscAnd adaptive mutation probability PmThe low-fitness individual is more preferably changed, and the high-fitness individual is hardly changed, so that the convergence of the algorithm to a local value may be increased, and the adaptive cross probability P of the embodimentcAnd adaptive mutation probability PmBy utilizing an exponential calculation formula, the cross probability and the variation probability change stability are improved, and the large-amplitude change condition of the calculation factor is avoided;
s34, adding the optimal solution of the previous generation into the population, screening the optimal solution of the population after bubble sorting, discarding the last group of genes if the optimal solution is still the optimal solution of the previous generation, bringing the optimal solution of the previous generation into the genetic population, and transmitting the genetic population to the next generation; if the optimal solution is not the previous generation optimal solution, randomly taking out a group of genes to compare with the previous generation optimal solution, and preferentially bringing the genes into the population; step S34 corresponds to the optimal fitness genetic factor addition in fig. 4;
and S35, judging whether the newly formed population meets a preset iteration termination condition, if not, returning to the step S31, and if so, outputting the gene with the maximum fitness in the population as an optimal solution. In this embodiment, the preset iteration termination condition is that the genetic algebra of the population reaches a preset termination genetic algebra.
In summary, the main differences between the improved adaptive genetic algorithm used in this embodiment and the general genetic algorithm are as follows:
(1) optimizing an initialization group;
(2) optimizing and selecting;
(3) adaptive parameters
Replacing the fixed cross and mutation probabilities with adaptive genetic parameters;
(4) essence of English
In order to prevent the optimal solution from being crossed or mutated and damaged in the evolution process, the optimal solution with the fitness is directly used as a final operator to participate in the sequencing of the shortest path and is inherited to the next generation.
The embodiment of the invention adopts the combined algorithm, so that the execution time of the system with low task number can be effectively reduced, and the absolute optimization of the execution queue with low task number is ensured; when the number of the calling tasks is large, the genetic algorithm is adopted, the execution tasks can be relatively accurately output in limited time, and the execution efficiency of the whole system is ensured.
Through verification, compared with a single algorithm, the combined algorithm can effectively reduce the calculation time of the system, improve the overall execution efficiency of the system and bring the execution efficiency of the system into play to the optimum. The improved genetic algorithm can obtain better repeated convergence, the maximum error is greatly reduced, the abnormal time consumption times are obviously reduced, and the method can adapt to the actual control requirements.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A dispatching method of a multilayer multi-port elevator is characterized by comprising the following steps:
acquiring a calling call task;
if only one group of calling tasks exists, directly executing the calling tasks; if the number of the calling tasks is more than 1 and not more than M, determining the execution sequence of each calling task by adopting a full-array algorithm with the shortest total length of the receiving distance as a target, and executing a plurality of groups of calling tasks according to the determined execution sequence, wherein M is equal to 5 or 6; if the number of the calling tasks is larger than M, the shortest total length of the receiving distance is taken as an optimization target, the execution sequence of the calling tasks is optimized by adopting a self-adaptive genetic algorithm, and a plurality of groups of calling tasks are executed according to the optimized execution sequence.
2. The method for dispatching the multi-layer and multi-port elevator as claimed in claim 1, wherein the optimization of the execution sequence of the call tasks by the adaptive genetic algorithm comprises the following steps:
s1, generating an initial population N by using the execution sequence of the group call tasks as a genetic individual in the populationA；
S2, for the initial population NAOptimizing to obtain an optimized initial population Nc；
S3, optimizing the initial population NcAnd (4) performing self-adaptive genetic operation iteratively until a preset iteration termination condition is met, and outputting an optimal solution when the iteration termination condition is met as an execution sequence of the optimized call tasks.
3. The method as claimed in claim 2, wherein the adaptive genetic operations include selection, adaptive crossover and adaptive mutation.
4. The method for dispatching a multi-level and multi-port elevator as claimed in claim 2, wherein said step S1 comprises:
generating a chromosome: pi＝(bi,ci) Bi is the goods-in floor of the ith calling task, ci is the goods-out floor of the ith calling task, i is more than or equal to 1 and less than or equal to n, and n is the total number of the calling tasks;
generating genes as the genetic individuals according to the call task ranking: pj’＝{P1,P2…Pn}；
Generating an initial population NA：NA＝{P1’,P2’...Pm' }; and m is the population scale.
5. The method for dispatching a multi-level and multi-port elevator as claimed in claim 2, wherein said step S2 comprises:
for initial population NAPerforming single genetic operation to obtain new population NBN with the population size of mAAnd N with population size mBMixing and preferentially outputting a new initial population N with the population size of mC。
6. The method as claimed in claim 5, wherein said preferentially outputting a new initial population N having a population size mCThe method comprises the following steps:
calculating the average fitness of the mixed population, and allowing the genes with the fitness higher than the average fitness to enter the next generation population NCGenes below the mean fitness are discarded; if the population scale is met in advance, the selection is stopped; and stopping selecting if the total number of the genes to be selected remaining in the mixed population is equal to the total number of the remaining requirements of the new population, and bringing all the remaining individuals into the new population.
7. The method for dispatching a multi-level and multi-port elevator as claimed in claim 2, wherein said step S3 comprises:
s31, calculating the fitness F of each gene serving as the genetic individual in the population:
F＝1/Xi，Xii is more than or equal to 1 and less than or equal to n, and n is the total number of the calling tasks;
s32, calculating the selection probability F of each geneP：
s33, selecting the next evolved gene by traversing double-gene roulette, taking the selected gene as a parent, obtaining a new gene by self-adaptive cross operation, and performing self-adaptive mutation operation on the selected gene to obtain a new gene to generate a new population;
s34, adding the optimal solution of the previous generation into the population, sorting and screening the optimal solution of the population, discarding the last group of genes if the optimal solution is still the optimal solution of the previous generation, bringing the optimal solution of the previous generation into the genetic population, and transmitting the genetic population to the next generation; if the optimal solution is not the previous generation optimal solution, randomly taking out a group of genes to compare with the previous generation optimal solution, and preferentially bringing the genes into the population;
and S35, judging whether the newly formed population meets a preset iteration termination condition, if not, returning to the step S31, and if so, outputting the gene with the maximum fitness in the population as an optimal solution.
8. The method as claimed in claim 7, wherein the selecting the next evolved gene by the ergodic double-gene roulette selection comprises: performing m rounds of selection, randomly generating two random numbers in the interval of [0, 1] in each round of selection, sequentially comparing each random number with the selection probability of m genes in the population until a gene with the selection probability higher than that of the random number is found, stopping comparison and reserving the gene, comparing two genes obtained according to the two random numbers, and selecting the gene with the higher selection probability to enter the next generation genetic population; in the next round of selection, the selection probability of each gene in the population is compared with the random number in sequence from the next gene of the selected gene in the previous round, and if the last gene in the population is compared and the gene with the selection probability larger than the random number is not found, the first gene in the population is returned to be compared.
9. The method for dispatching a multi-layer and multi-port elevator as recited in claim 1, wherein the task of obtaining a call comprises:
and putting the received call requests into a task queue as call tasks, wherein the call requests comprise a goods entrance floor, a goods exit floor, a goods entrance and a goods exit.
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| CN117196269A (en) * | 2023-11-07 | 2023-12-08 | 深圳市欣视景科技股份有限公司 | Intelligent transfer control method based on artificial intelligence and computer program medium |
| CN117196269B (en) * | 2023-11-07 | 2024-02-20 | 深圳市欣视景科技股份有限公司 | Intelligent transfer control method based on artificial intelligence and computer program medium |
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