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CN113408988A - Scheduling optimization method of warehousing system, electronic equipment and storage medium - Google Patents

Scheduling optimization method of warehousing system, electronic equipment and storage medium Download PDF

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CN113408988A
CN113408988A CN202110727618.8A CN202110727618A CN113408988A CN 113408988 A CN113408988 A CN 113408988A CN 202110727618 A CN202110727618 A CN 202110727618A CN 113408988 A CN113408988 A CN 113408988A
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徐立云
占翔南
陈晨
张建
刘雪梅
马淑梅
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Abstract

本发明提供一种仓储系统的调度优化方法、电子设备及存储介质；其中，所述仓储系统的调度优化方法，包括；确定用于获取各调度方案对应调度时间的调度时间模型，和确定与所述调度时间模型相关的模型约束；基于所述调度时间模型和所述模型约束，以所述调度时间最小为优化目标，采用混合遗传算法对所述初始调度方案集进行优化，获得优化后的新调度方案集；提取所述新调度方案集中所述调度时间最小的调度方案作为最优调度方案。与现有的调度优化方法相比，本发明实现了多深度仓储系统四向穿梭车作业任务调度优化，显著提高了仓储系统作业效率。

Figure 202110727618

The present invention provides a scheduling optimization method, electronic equipment and storage medium for a storage system; wherein, the scheduling optimization method for a storage system includes: determining a scheduling time model for obtaining the scheduling time corresponding to each scheduling scheme, and determining The model constraints related to the scheduling time model; based on the scheduling time model and the model constraints, with the minimum scheduling time as the optimization goal, the hybrid genetic algorithm is used to optimize the initial scheduling scheme set, and the optimized new Scheduling scheme set; extracting the scheduling scheme with the smallest scheduling time in the new scheduling scheme set as the optimal scheduling scheme. Compared with the existing scheduling optimization method, the invention realizes the task scheduling optimization of the four-way shuttle vehicle in the multi-depth storage system, and significantly improves the operation efficiency of the storage system.

Figure 202110727618

Description

Scheduling optimization method of warehousing system, electronic equipment and storage medium
Technical Field
The invention relates to the field of warehousing system task scheduling, in particular to a scheduling optimization method of a warehousing system, electronic equipment and a computer readable storage medium.
Background
In recent years, with the rapid development of electronic commerce and the continuous innovation of automation technology, the logistics industry in China has been developed greatly. The multi-depth four-way shuttle storage system is a novel intelligent storage system, has the advantages of high space utilization rate, strong robustness, high response speed and the like, and is widely concerned by enterprises. The four-way shuttle serves as a main transportation device of the warehousing system, and the operation efficiency of the four-way shuttle has a direct influence on delivery of orders in and out of a warehouse. The four-way shuttle car runs in a trunk road and a storage roadway of a multi-depth storage area, when the four-way shuttle cars run simultaneously, vehicle conflict can occur, and scheduling tasks are distributed unreasonably and are not optimized enough, so that the running efficiency of the four-way shuttle car is influenced, the system operation efficiency is reduced, and how to reasonably plan a path and schedule tasks is a key problem to be solved urgently.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present invention is to provide a scheduling optimization method for a warehousing system, which is used to solve the problems of long scheduling time and low operation efficiency of the system due to unreasonable and optimized warehousing scheduling in the prior art.
To achieve the above and other related objects, the present invention provides a scheduling optimization method for a warehousing system, for optimizing each scheduling scheme in an initial scheduling scheme set to obtain an optimal scheduling scheme, the method including: determining a scheduling time model, and determining model constraints for the scheduling time model; the scheduling time model is used for obtaining scheduling time of each scheduling scheme; based on the scheduling time model and the model constraint, optimizing the initial scheduling scheme set by adopting a hybrid genetic algorithm with the minimum scheduling time as an optimization target to obtain an optimized new scheduling scheme set; and extracting the scheduling scheme with the minimum scheduling time in the new scheduling scheme set as an optimal scheduling scheme.
In an embodiment of the present invention, an implementation manner of the determining a scheduling time model includes: constructing a road orientation strategy of the warehousing system by adopting a graph theory method; and constructing an association function between the scheduling position information and the scheduling time in the scheduling scheme based on the road orientation strategy.
In an embodiment of the present invention, the implementation manner of constructing the road orientation policy of the warehousing system by using the graph theory method includes: constructing an initial directed graph of road network distribution in a warehousing system by adopting a directed graph expression method, and orienting each edge in the initial directed graph; and converting the initial directed graph into a strongly connected directed graph by adopting a Hopcroft-Tarjan algorithm.
In an embodiment of the present invention, the implementation manner of optimizing the initial scheduling scheme set by using a hybrid genetic algorithm based on the scheduling time model and with the minimum scheduling time as an optimization goal includes: performing encoding, crossing and variation, domain switching and detection updating on each scheduling scheme in the initial scheduling scheme set to obtain a new scheduling scheme set; taking the new scheduling scheme set as the initial scheduling scheme set, and repeatedly executing the processes until the scheduling time of each scheduling scheme in the new scheduling scheme set meets a preset scheduling time convergence condition; the coding is to adopt a double-segment chromosome coding method to construct double-segment chromosome codes corresponding to each scheduling scheme; the crossing and mutation are performed on each two-segment chromosome code based on the model constraint; performing a neighborhood exchange process based on task sequencing on each double-segment chromosome code after the cross mutation by the domain exchange to obtain a new scheduling scheme corresponding to each double-segment chromosome code after the domain exchange; and the detection updating is to detect whether each new scheduling scheme meets a preset optimization judgment condition, if so, the new scheduling scheme meeting the optimization judgment condition is replaced by the corresponding original scheduling scheme, and if not, the corresponding original scheduling scheme is reserved.
In an embodiment of the present invention, the implementation manner that optimizes the initial scheduling scheme set by using a hybrid genetic algorithm with the minimum scheduling time as an optimization target based on the scheduling time model and the model constraint further includes: screening out a better scheduling scheme subset from the initial scheduling scheme set based on the scheduling time model; the total scheduling time of the preferred scheduling scheme subset is less than other scheduling scheme subsets in the initial scheduling scheme set; performing the encoding, the interleaving and mutation, the domain switching, and the detection update for each scheduling scheme in the subset of better scheduling schemes to obtain a new subset of better scheduling schemes; replacing the subset of better scheduling schemes with the subset of new better scheduling schemes to obtain a new set of scheduling schemes.
In an embodiment of the present invention, the implementation manner of screening out a better scheduling scheme subset from the initial scheduling scheme set based on the scheduling time model includes: and acquiring the scheduling time of each scheduling scheme in the initial scheduling scheme set based on the scheduling time model, and screening the optimal scheduling scheme subset in the initial scheduling scheme set by adopting a championship method.
In an embodiment of the present invention, the implementation manner of detecting whether each new scheduling scheme satisfies a preset optimization determination condition includes: and sampling each new scheduling scheme by adopting a simulated annealing method, and detecting whether the sampled scheduling time of each new scheduling scheme meets Metropolis acceptance criteria.
In an embodiment of the present invention, if a single scheduling scheme includes the serial number of each scheduling task and the number of scheduling tasks assigned by each four-way shuttle, the implementation manner of constructing the two-segment chromosome code corresponding to the scheduling scheme by using the two-segment chromosome coding method includes: and setting the serial number of each scheduling task as a first code in the double-segment chromosome codes, and setting the number of the scheduling tasks distributed by each four-way shuttle as a second code in the double-segment chromosome codes.
In an embodiment of the present invention, the implementation manner of performing crossover and mutation on each two-segment chromosome code based on the model constraint includes: for the double-segment chromosome codes corresponding to each scheduling scheme, adopting a two-point crossing mode to cross the task sequence in the first codes, and not crossing the second codes; randomly selecting two variation points in the first code, and exchanging the numerical values of the two variation points; randomly selecting w vehicles (w is any value of 1-4) from the second code, and performing adjacent value variation operation on the task number corresponding to the selected vehicles to obtain new double chromosomes; and judging whether a new scheduling scheme corresponding to the new double-segment chromosome meets the model constraint in the scheduling time model, if so, executing the subsequent steps, and if not, correcting the new double-segment chromosome operator.
In an embodiment of the present invention, the implementation manner of performing the correction on the new two-segment chromosomal engineer includes: detecting whether each gene numerical value in the new double-segment chromosome code has 0, if so, obtaining the total number n of the genes with the numerical value of 0, subtracting n from the numerical value of the gene with the maximum numerical value of the double-segment chromosome code, and adding 1 to the numerical value of the gene with the 0 to obtain the repaired double-segment chromosome code; and obtaining the numerical sum of each gene coded by the repaired double-segment chromosome and the numerical sum of each gene coded by the repaired double-segment chromosome, comparing the two numerical sums, if the former is smaller than the latter, obtaining the difference value of the two numerical sums, and adding the difference value to the numerical value of the minimum value in the repaired double-segment chromosome.
The present invention additionally provides an electronic device comprising: a memory for storing a computer program; and the processor is in communication connection with the memory and executes the scheduling optimization method of the warehousing system when calling the computer program.
The present invention further provides a computer readable storage medium, wherein the computer program, when executed by a processor, implements a scheduling optimization method for a warehousing system as described in any one of the above.
As described above, according to the scheduling optimization method for the warehousing system, the electronic device and the computer-readable storage medium of the invention, the task scheduling scheme is optimized by using the improved hybrid genetic algorithm based on the scheduling time model, and compared with the existing scheduling optimization method, the scheduling optimization method can obtain a better scheduling scheme, shorten the scheduling time required for completing the scheduling task, further improve the operation efficiency of the warehousing system, shorten the time consumption of the optimization process, and find the optimal solution more quickly, thereby further improving the scheduling efficiency of the warehousing system.
Drawings
FIG. 1 is a schematic view of the warehousing system in one embodiment;
FIG. 2 is a flow chart illustrating a method for scheduling optimization of the warehousing system in one embodiment;
FIG. 3 is a flowchart illustrating the step S1 in the scheduling optimization method of the warehousing system according to an embodiment;
FIG. 4 is a schematic flow chart illustrating the road direction strategy in one embodiment;
FIG. 5 is a schematic diagram of the outbound scheduling process (on the left side of the figure) and the inbound scheduling process (on the right side of the figure);
FIG. 6 is a flowchart illustrating step S3 of the scheduling optimization method for the warehousing system in one embodiment;
FIG. 7 is a schematic diagram of the double-segment chromosome encoding method in one embodiment;
FIG. 8 is a schematic diagram of the interleaving process in one embodiment;
FIG. 9 is a schematic diagram of the mutation process in one embodiment;
FIG. 10 is a schematic diagram of the correction process in one embodiment.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Example 1
In order to solve the problems in the prior art, an embodiment of the present invention provides a scheduling optimization method for a warehousing system, which is used for scheduling the warehousing and ex-warehousing of goods in the warehousing system.
As shown in fig. 1, the warehousing system is a multi-depth four-way shuttle warehousing system, and includes a storage area 1, a road network 2, a four-way shuttle 3, a conveying system 4, and a control system (not shown).
Wherein, the storage goods area 1 is provided with a plurality of storage goods positions; in one embodiment, the storage area comprises a multi-depth high-density storage rack;
the road network 2 comprises a trunk road and a storage roadway; the main trunk road is used for connecting each goods storage area, and the goods storage roadway is positioned in the goods storage areas so as to allow the four-way shuttle car to pass through and store and take goods;
the four-way shuttle 3 is used for realizing horizontal transportation of goods and can transversely and longitudinally run along a goods storage roadway and a goods shelf. When the four-way shuttle vehicle is in an idle state, the four-way shuttle vehicle can freely shuttle in a goods storage roadway with goods to reach any goods storage area in the storage system; when the four-way shuttle vehicle is in a cargo state, the four-way shuttle vehicle can only run on the idle storage roadway and the main road.
The conveying system 4 is positioned at the warehousing port and the ex-warehousing port of the warehousing system and is used for realizing the in-and-out transfer of goods;
and the control management system is responsible for equipment monitoring and assignment of operation tasks of the whole warehousing system.
It should be noted that, a plurality of four-way shuttles can be arranged in the warehousing system to carry out warehousing and ex-warehousing operations, so as to improve the flexibility of the warehousing system.
The road network in the storage area comprises roads and intersections among the roads; the road comprises at least a main road for traffic and a roadway for loading/unloading.
In this embodiment, the scheduling optimization method of the warehousing system is shown in fig. 2, and includes the following steps:
s1: constructing a road orientation strategy of the warehousing system by adopting a graph theory method;
the road orientation strategy is an orientation model of the road network in the warehousing system, namely the passing direction of each road in the warehousing system is determined, so that each road is communicated with each other and has the passing direction.
Specifically, this step, as shown in fig. 3, includes the following sub-steps:
s101, constructing an initial directed graph of road network distribution in a warehousing system by adopting a directed graph expression method, and orienting each edge in the initial directed graph;
and converting the distribution of the road network in the warehousing system into an initial directed graph G (V (G), E (G)) by adopting a directed graph expression method, abstracting each trunk road and each storage roadway in the road network into edges in the initial directed graph G, and abstracting an intersection into a vertex in the initial directed graph G.
And based on the preset passing direction of each road in the traffic network, orienting each corresponding edge in the initial directed graph G to ensure that the initial directed graph conversion has connectivity.
S102, converting the initial directed graph into a strongly connected directed graph by adopting a Hopcroft-Tarjan algorithm;
because the initial directed graph contains cut edges, the method adopts a Hopcroft-Tarjan algorithm to convert the initial directed graph into a strongly-communicated directed graph so as to ensure that a plurality of four-way shuttles in the warehousing system can operate simultaneously, and further comprises that the plurality of four-way shuttles in the warehousing system can operate smoothly.
Specifically, the method comprises the following steps:
step 1: taking a vertex V in G, let L (V) be 1, L be { V }, and U be V- { V },
Figure RE-GDA0003182386390000051
step 2: taking vertex U in L such that L (U) is maximum and satisfies that there is a vertex adjacent to U in U, then taking one vertex w adjacent to U from U such that the edge uw becomes an oriented edge U → w, while letting L (w) L (U) +1, L ═ L { w }, U ═ U- { w }, a ═ a { [ U → w };
and step 3: if L is not equal to V, turning to the step 2, otherwise executing the step 4;
and 4, step 4: for the currently unoriented edge mn, the orientation is performed according to the following method: if l (m) > l (n), the side after the designated direction is m → n, otherwise the side is n → m.
In the above steps, V, U and w all represent a single intersection, V represents a total set of all intersections, R represents a set of roads whose directions have been determined, L is a set of intersections to which a label has been given, U is a set of intersections to which no label has been given, and mn represents an unoriented road.
Further, according to the layout characteristics and the warehouse-in and warehouse-out characteristics of the warehousing system, the strong communication directional diagram is combined with the road directional information of the road section of the warehouse-in and warehouse-out port, so that the final road directional strategy of the warehousing system is obtained.
Wherein the final road orientation strategy of the warehousing system is the final road orientation map, as shown in fig. 4.
S2, constructing the scheduling time model based on the road orientation strategy; and determining model constraints of the scheduling time model according to the scheduling time model and scheduling rules of the warehousing system.
The scheduling time model is a model of time required by each four-way shuttle vehicle to complete an operation task, which is constructed based on the road orientation strategy according to the warehouse entry and exit scheduling task, the warehouse entry and exit operation flow and the layout characteristics of the warehouse system, and is a correlation function between scheduling position information in the scheduling scheme and the scheduling time.
In this embodiment, the in-out scheduling task includes an in-storage scheduling task and an out-of-storage scheduling task; when the warehousing scheduling task is received, the warehousing scheduling process executed by the four-way shuttle is shown as a left-side flow chart in fig. 5, and includes:
1) after receiving the warehousing dispatching task, the four-way shuttle car runs from the current position to the warehousing port, goods are obtained from the warehousing port, and at the moment, the four-way shuttle car is changed from an empty car state to a loaded state;
2) taking a storage roadway entrance where a target goods position in the warehousing scheduling task is located as a destination, and acquiring a current warehousing planned path of the four-way shuttle car according to the final road directional diagram;
3) and the four-way shuttle vehicle drives to the goods storage roadway where the target goods location is located according to the current warehousing planned route, places goods after reaching the target goods location, and updates the goods location state information of the goods location, so that the warehousing scheduling task is completed.
When the outbound scheduling task is received, the right-side flowchart in the outbound scheduling flowchart 5 executed by the four-way shuttle vehicle includes:
1) when the ex-warehouse scheduling task is generated, the state of the goods position corresponding to the task is changed, the goods are taken out of the warehouse of the goods position, and other ex-warehouse tasks are not generated;
2) after receiving the ex-warehouse scheduling task, the four-way shuttle car runs to the target goods position from the current position, obtains goods from the target goods position, and changes the empty state of the four-way shuttle car into the loaded state;
3) taking an outbound port in the outbound scheduling task as a destination, and acquiring the current outbound planning path of the four-way shuttle car according to the final road directional diagram;
4) and the four-way shuttle car runs to the warehouse-out opening according to the current warehouse-out planning path, places the goods, updates the goods position state of the goods position, and finishes the warehouse-out scheduling task.
In this embodiment, the scheduling task information of the warehousing system may be described as: distributing n in-out tasks to m four-way shuttle vehicles for execution, wherein all task sets A are { A }1,A2,…,AmThe four-way shuttle set S ═ S1,s2,…,sm}; the ith four-way shuttle vehicle obtains a task set of
Figure RE-GDA0003182386390000061
Wherein n represents the total number of tasks; k is the number of tasks assigned to the ith four-way shuttle.
The four-way shuttle vehicle with the number of i in the objective function finishes kiThe scheduling time of each task is as follows:
Figure RE-GDA0003182386390000071
j is the number of the assigned task of the ith four-way shuttle vehicle;
Figure RE-GDA0003182386390000072
executing the cargo phase running time of the j task for the ith four-way shuttle;
Figure RE-GDA0003182386390000073
executing the empty stage running time of the jth task for the ith four-way shuttle; sPosjIs the starting point of the j task; ePosjIs the end point of the j-th task.
Acquiring starting point position information of each four-way shuttle car and position information contained in each task information, and acquiring a planned path corresponding to each car based on the final road directional diagram; and acquiring the scheduling time of the four-way shuttle vehicle based on the planned path.
The warehouse-in and warehouse-out scheduling tasks comprise warehouse-in scheduling tasks and warehouse-out scheduling tasks; for the warehousing scheduling task, a single four-way shuttle car firstly runs from a starting point to the warehousing port to load goods, then obtains a corresponding planned path according to the final directed graph, runs from the warehousing port to a target goods position corresponding to the task based on the planned path, and the running time of a no-load stage is as follows:
Figure RE-GDA0003182386390000074
the run time during full load is:
Figure RE-GDA0003182386390000075
wherein l is the length of a single cargo space, w is the width of a single cargo space, tuFor the steering time, t, of a four-way shuttleoLoading and unloading time t of the four-way shuttle at the entrance and exitsLoading and unloading time of the four-way shuttle at the cargo space, v is average speed of the four-way shuttle, (x)out,yout) As the coordinates of the warehouse-out opening, (x)e,ye) And a represents the steering times of the four-way shuttle in the no-load stage, and b represents the steering times of the four-way shuttle in the full-load stage as the target goods space coordinate.
For the dispatching task of ex-warehouse, a single four-way shuttle car firstly runs from a starting point to a target goods position, then obtains a corresponding planning path based on the final directional graph, and runs from the target goods position to the ex-warehouse port based on the planning path, wherein the no-load phase running time is as follows:
Figure RE-GDA0003182386390000076
wherein (x)in,yin) Coordinates of the warehouse entry are obtained;
the run time during full load is:
Figure RE-GDA0003182386390000077
and d represents the steering times of the four-way shuttle car in the full-load stage of the warehouse-out, and is determined by the position of the warehouse entry relative to the target goods space and the layout characteristics of the warehousing system.
In order to ensure that the task allocation mode of the four-way shuttle is reasonably arranged on the basis of meeting the dispatching rule condition; determining model constraints of the scheduling time model based on the scheduling time model and scheduling rules of the warehousing system, wherein the model constraints at least comprise:
constraint 1: each four-way shuttle is assigned at least one task, namely:
ki＞0,i∈[1,m]
constraint 2: the running time of each four-way shuttle vehicle in the idle phase cannot be a negative value, namely:
Figure RE-GDA0003182386390000081
constraint 3: the four-way shuttle has to run for a positive value during the loading phase, i.e.:
Figure RE-GDA0003182386390000082
constraint 4: the sum of the number of tasks executed by each four-way shuttle is the total number of tasks, namely:
Figure RE-GDA0003182386390000083
constraint 5: a single task can only be performed by one four-way shuttle, namely:
Figure RE-GDA0003182386390000084
wherein，kiThe number of tasks distributed by the ith four-way shuttle is shown, j is the number of the task distributed by the four-way shuttle with the number of i,
Figure RE-GDA0003182386390000085
the empty phase running time of the task numbered j is executed for the four-way shuttle numbered i,
Figure RE-GDA0003182386390000086
for the four-way shuttle numbered i, the cargo phase running time for the task numbered j is executed, AiSet of tasks representing the ith four-way shuttle, AqAnd AwRespectively representqAnd the task set of the w-th four-way shuttle.
The optimization goal of the warehousing system scheduling is to minimize the scheduling time for completing all scheduling job tasks, namely, the total time for completing warehousing and ex-warehousing tasks of each four-way shuttle is minimized. Based on the optimization objective, determining an objective function of the warehousing system scheduling time model as follows:
fun C＝min(max(Ti))
and S3, based on the scheduling time model and the model constraint, optimizing the initial scheduling scheme set by adopting a hybrid genetic algorithm with the minimum scheduling time as an optimization target to obtain an optimized new scheduling scheme set.
The initial scheduling scheme is an in-out initial scheduling scheme which is input from the outside or obtained in advance through other modes based on the total task information of the warehousing system, so that each in-out scheduling operation is completed through execution of each four-way shuttle.
In the invention, the hybrid genetic algorithm introduces the strong local search capability of the simulated annealing algorithm (SA) into the search process of the genetic algorithm, and the algorithm has the global search capability of the genetic algorithm and the high-efficiency local search capability of the simulated annealing algorithm.
The principle of the hybrid genetic algorithm adopted by the invention is as follows: firstly, carrying out genetic operation on an initial population by using a genetic algorithm to achieve the aim of evolving the population, and then carrying out sampling judgment on a result obtained by the evolution of the genetic algorithm through a Metropolis sampling process of a simulated annealing algorithm; and taking the sampled result as an initial population for carrying out next generation evolution operation on the genetic algorithm, and repeating the process to obtain an optimization result, namely obtaining an optimized new scheduling scheme set.
In this embodiment, the step is specifically shown in fig. 6, and includes the following sub-steps:
s301, based on the scheduling time model, screening out a better scheduling scheme subset from the initial scheduling scheme set;
wherein the total scheduling time of the preferred scheduling scheme subset is less than the scheduling scheme subsets other than the preferred scheduling scheme subset in the initial scheduling scheme set;
specifically, the step S301 includes:
1) calculating the scheduling time of each scheduling scheme in the initial scheduling scheme set, calculating the fitness of each scheduling scheme based on the scheduling time, and sequencing the fitness of each scheduling scheme;
constructing a fitness function based on the maximum completion time of each four-way shuttle vehicle, as follows:
Figure RE-GDA0003182386390000091
wherein Y is the fitness of each four-way shuttle vehicle; t ismaxThe maximum completion time of each four-way shuttle vehicle for executing the tasks is represented, namely the maximum completion time in the completion time required for executing each task; t is1The maximum completion time required for the first four-way shuttle to complete the task; t ismThe maximum completion time required for the mth four-way shuttle to complete the task.
2) Selecting each scheduling scheme with high adaptability in the initial scheduling scheme set through a championship selection method to obtain the optimal scheduling scheme subset;
the selection process is to adopt a championship method to select the scheduling schemes, select 2 scheduling schemes each time to carry out fitness comparison, put the scheduling schemes with high fitness values into a cross pool, and carry out cyclic operation to obtain N populations; the population is the subset of the better scheduling scheme.
Figure RE-GDA0003182386390000092
Wherein P represents the probability of each scheduling scheme being selected in the championship selection method, N represents the number of obtained populations, miRepresenting the probability that the current task i is indexed.
S302, constructing a double-segment chromosome code corresponding to each scheduling scheme in the preferred scheduling scheme subset by adopting a double-segment chromosome coding method;
specifically, in order to describe two kinds of information of the execution sequence of the in-out scheduling task and the four-way shuttle of the scoring task at the same time, a two-segment chromosome coding method as shown in fig. 7 is adopted. The two-segment chromosomal coding comprises a first coding and a second coding; the first codes are codes based on task sequence numbers, and each code represents a corresponding warehouse entry and exit scheduling task and is used for representing the execution sequence of each warehouse entry and exit scheduling task; the second code is a code for dividing the number of tasks based on each four-way shuttle vehicle, wherein each number represents the number of the tasks executed by the four-way shuttle vehicle.
Each two-segment chromosome code corresponds to a scheduling scheme so as to describe the number of tasks distributed by each four-way shuttle vehicle and the execution sequence of the tasks distributed by each four-way shuttle vehicle.
S303, based on the model constraint, performing cross and variation operation on each scheduling scheme in the better scheduling scheme subset to obtain a cross-varied scheduling scheme;
and crossing and mutating the double-segment chromosome codes corresponding to each scheduling scheme in the preferred scheduling scheme subset selected in the step S301 by adopting a self-adaptive crossing and mutating method so as to avoid the problems of premature and local convergence of the algorithm.
The crossing process is to cross the task code, i.e. the first code, in the double-segment chromosome coding by adopting a two-point crossing mode, as shown in fig. 8. In the figure, the parent 1 and the parent 2 are two first codes before crossing, and the child 1 and the child 2 are two first codes after crossing.
For the second code, namely the vehicle code part, if the number of the division tasks executed by the four-way shuttle vehicle is greatly changed by using a two-point intersection method for intersection operation, a better scheme is not generated; therefore, in order to obtain a scheduling scheme with shorter job time, the second code is directly retained in the scheduling scheme after the cross mutation without being crossed.
The mutation operation comprises: performing a crossover mutation operation on the first code. By adopting a method of exchanging variation, as shown in fig. 9, two variation points are randomly selected, and the coding values of the variation points are exchanged to obtain a new scheduling scheme; when the second code is subjected to variation operation, randomly selecting w vehicles, and performing adjacent value variation operation on the corresponding scoring task number of the selected vehicles; wherein w is any integer between 1 and 4.
After the mutation operation is executed, whether the currently obtained chromosome meets the model constraint is checked, and the chromosome which does not meet the model constraint is corrected until the model constraint is met.
The correction method comprises the following steps: as shown in fig. 10, for example, when the parent is [2,1,3,4], and [1,3,4] is selected to perform the neighbor variation operation, the variation result is [0,4,3], and the number of tasks assigned to car No. 2 is 0. In order to repair the 0-value gene, subtracting 1 from the maximum value of the current gene, adding 1 to the 0-value gene to obtain the filial generation [2,1,3,3] subjected to the 0-value gene repair, wherein the total number of tasks is 9 and is smaller than the total number of tasks before mutation, 10, and adding the difference to the minimum value of the gene to obtain the filial generation [2,2,3,3] subjected to the repair total operation, wherein the filial generation meets all constraint conditions in the model constraint.
In this embodiment, the improved variation method can avoid generating an illegal solution, and further improve the search efficiency of a feasible solution.
S304, optimizing each scheduling scheme after cross variation based on a multi-position neighborhood exchange method of task sequencing to obtain a new scheduling scheme;
the invention provides a multi-position neighborhood switching method based on task sequencing, which can effectively increase the diversity of a scheduling scheme. The exchange process can be described as follows: for the cross-mutated scheduling scheme i, random generation [1,40 ]]K (k ≦ 40) different integers within the interval: x is the number of1,x2,…xk. Sequentially for the xth of the scheduling scheme iiAnd (i is more than or equal to 1 and less than or equal to 40) performing multi-position neighborhood exchange on the part of the task sequences at the positions. First, in [1, max]An integer len is randomly determined in the interval, len is the length of task exchange, and max is the set maximum value. Then, randomly selecting two segments of task sequences with len length from the current partial scheduling scheme, and performing standard neighborhood exchange; and checking whether the exchanged scheduling scheme i' meets each constraint condition in the constraint model, if so, reserving the exchange, and if not, canceling the exchange. And repeating the process until the k partial task sequences are exchanged, and outputting the exchanged new scheduling scheme i'.
S306, detecting whether the new scheduling scheme meets a preset optimization judgment condition, if so, replacing the new scheduling scheme with the original scheduling scheme, and if not, reserving the original scheduling scheme to obtain a new better scheduling scheme subset;
specifically, each new scheduling scheme is sampled by adopting a simulated annealing method, and whether the sampled scheduling time of each new scheduling scheme meets Metropolis acceptance criteria or not is detected.
Wherein, the initial value of the control parameter t of the simulated annealing process is determined according to the following formula:
Figure RE-GDA0003182386390000111
in the formula (f)maxAnd fminThe fitness P of the best scheduling scheme and the worst scheduling scheme in the current group respectivelyrIs the initial acceptance probability of the control parameter t.
The simulated annealing process adopts the following steps of cooling the control parameter t:
t(k+1)＝λtk
in the formula, λ is a constant, λ ∈ [0,1], and the smaller the value of λ is, the faster the falling speed of t is.
The preset optimization judgment condition comprises but is not limited to Metropolis acceptance criteria; preferably, in this embodiment, the Metropolis acceptance criterion is adopted as the predetermined condition.
And detecting whether each scheduling scheme after switching meets Metropolis acceptance criteria, if so, replacing the original scheduling scheme before switching, and if not, reserving the original scheduling scheme before switching to obtain the new better scheduling scheme subset.
S307, replacing the corresponding better scheduling scheme subset with the new better scheduling scheme subset to obtain a new scheduling scheme set; taking the new scheduling scheme set as the initial scheduling scheme set, returning to step S301, and repeatedly executing steps S301 to S307 until the scheduling time of each scheduling scheme in the new scheduling scheme set meets a preset scheduling time convergence condition;
specifically, the preset scheduling time convergence condition is as follows: the difference of the scheduling time of each scheduling method in the new scheduling scheme set is less than a preset time difference; in one embodiment, the predetermined time difference is 1 s.
S4, extracting the scheduling scheme with the minimum scheduling time in the new scheduling scheme set as the optimal scheduling scheme.
To further prove the beneficial effects of the scheduling optimization method of the warehousing system provided by the invention, the following specific embodiments are adopted for comparison and description.
Taking a one-car warehousing system as an example, the allocation of warehousing and ex-warehouse tasks and the operation sequence of the four-way shuttle car are optimized. The warehousing system is divided into an upper warehousing area and a lower warehousing area, wherein the depth of a goods position is 6, and the number of roadways is 32. The warehousing system comprises 5 four-way shuttle cars, 40 warehousing tasks need to be executed in a certain time window, coordinate position points of each car when the car is idle are shown in table 1, and warehousing tasks are shown in table 2, wherein tasks with task starting points of (0, 0) represent warehousing tasks, and tasks with task ending points of (0, 14) represent warehousing tasks.
TABLE 1 coordinate points of starting position of four-way shuttle
Figure RE-GDA0003182386390000121
Table 240 in-out task coordinate points
Figure RE-GDA0003182386390000122
Figure RE-GDA0003182386390000131
For verifying the effectiveness of the method according to the invention, reference is made to the selection of control parameters [6 ]]And [12 ]]The value of (1). The parameters chosen here are the population number popSize 200, the maximum number of iterations G300, and the cross parameter kc0.7, variation parameter km0.1, 5 as parameter of adaptive probability adjusting function, 10% as proportion pro of elite in population, and initial acceptance probability P of control parameter tr0.12, and a cooling coefficient λ of 0.92. The program runs on a MATLAB 2016b platform, and the computer is configured with Intel (R) core (TM) i7-7700HQ CPU @2.80GHz and 8.00GB RAM.
The execution sequence of the tasks of the 5 four-way shuttles randomly obtained according to the existing warehousing control program is shown in table 3, the operation time of the 5 shuttles is 512.34s, 497.21s, 522.87s, 501.46s and 516.27s respectively, and the total time for completing the operation tasks is 522.87s because the 5 four-way shuttles run simultaneously.
TABLE 3 Job assignment and execution sequence for random four-way shuttle
Figure RE-GDA0003182386390000132
The execution sequence of the operation tasks of the 5 four-way shuttle vehicles obtained after the optimization of the method is shown in table 4, the operation time of the 5 four-way shuttle vehicles is 425.74s, 424.59s, 431.63s, 437.06s and 429.48s respectively, the total time for completing the operation tasks is 437.06s, and the optimization efficiency is improved by 16.41%.
TABLE 4 optimized work tasks and execution sequence for the rear four-way shuttle
Figure RE-GDA0003182386390000133
Figure RE-GDA0003182386390000141
To verify the effectiveness of the method of the present invention, the GA algorithm, the SA algorithm and the IHGA algorithm (the method of the present invention) were repeated 100 times for each of the cases with the task size of 40,80 and 120, respectively, and the cargo space was randomly generated for each task size, and the experimental results of the 3 algorithms are shown in table 5.
As can be seen from the results in Table 5, the accuracy and stability of the algorithm are greatly improved and the solution time is reduced for IHGA compared with GA and SA. And with the continuous increase of task scale, compare in GA and SA algorithm, the advantage of IHGA algorithm's solution accuracy and stability is constantly enlarged, and the optimization effect is more obvious, and warehouse entry task distribution and four-way shuttle execution order are more reasonable, and the task completion time promotes more greatly, and system operating efficiency is higher.
TABLE 53 comparison of the results of the Algorithm experiments
Figure RE-GDA0003182386390000142
Example 2
The present invention provides an electronic device in this embodiment, the electronic device includes: a processor, a memory, and a display. Wherein the memory stores a computer program; the processor is in communication connection with the memory and executes the scheduling optimization method of the warehousing system when the computer program is called.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
Example 3
The invention also provides a computer readable storage medium, on which a computer program is stored, which when invoked by a processor implements the method for scheduling optimization of a warehousing system of the invention. The computer-readable storage medium may include a Random Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
In summary, compared with the existing scheduling optimization method, the scheduling optimization method of the warehousing system, the electronic device and the computer readable storage medium provided by the invention can greatly shorten the scheduling time required by task completion, and further improve the efficiency of the warehousing system operation, thereby realizing the optimization of the task scheduling strategy and enabling the task allocation and the execution sequence of the four-way shuttle to be more reasonable; in addition, the method is based on the improved hybrid genetic algorithm and is better than the simulated annealing algorithm based on the existing scheduling optimization method, and the improved hybrid genetic algorithm is better than the simulated annealing algorithm in the convergence time consumption of the algorithm, so that the optimal solution can be found more quickly, and the scheduling efficiency of the warehousing system is further improved.
Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (12)

1. A scheduling optimization method for a warehousing system, configured to optimize each scheduling scheme in an initial scheduling scheme set to obtain an optimal scheduling scheme, the method comprising:
determining a scheduling time model, and determining model constraints for the scheduling time model; the scheduling time model is used for obtaining scheduling time of each scheduling scheme;
based on the scheduling time model and the model constraint, optimizing the initial scheduling scheme set by adopting a hybrid genetic algorithm with the minimum scheduling time as an optimization target to obtain an optimized new scheduling scheme set;
and extracting the scheduling scheme with the minimum scheduling time in the new scheduling scheme set as an optimal scheduling scheme.
2. The method of claim 1, wherein the determining an implementation manner of the scheduling time model comprises:
constructing a road orientation strategy of the warehousing system by adopting a graph theory method;
and constructing an association function between the scheduling position information and the scheduling time in the scheduling scheme based on the road orientation strategy.
3. The scheduling optimization method of the warehousing system according to claim 2, wherein the implementation of the road orientation strategy for constructing the warehousing system by using the graph theory method comprises:
constructing an initial directed graph of road network distribution in a warehousing system by adopting a directed graph expression method, and orienting each edge in the initial directed graph;
and converting the initial directed graph into a strongly connected directed graph by adopting a Hopcroft-Tarjan algorithm.
4. The scheduling optimization method of the warehousing system according to claim 1, wherein the implementation manner of optimizing the initial scheduling scheme set by using a hybrid genetic algorithm with the minimum scheduling time as an optimization target based on the scheduling time model comprises:
performing encoding, crossing and variation, domain switching and detection updating on each scheduling scheme in the initial scheduling scheme set to obtain a new scheduling scheme set;
taking the new scheduling scheme set as the initial scheduling scheme set, and repeatedly executing the processes until the scheduling time of each scheduling scheme in the new scheduling scheme set meets a preset scheduling time convergence condition;
the coding is to adopt a double-segment chromosome coding method to construct double-segment chromosome codes corresponding to each scheduling scheme;
the crossing and mutation are performed on each two-segment chromosome code based on the model constraint;
performing a neighborhood exchange process based on task sequencing on each double-segment chromosome code after the cross mutation by the domain exchange to obtain a new scheduling scheme corresponding to each double-segment chromosome code after the domain exchange;
and the detection updating is to detect whether each new scheduling scheme meets a preset optimization judgment condition, if so, the new scheduling scheme meeting the optimization judgment condition is replaced by the corresponding original scheduling scheme, and if not, the corresponding original scheduling scheme is reserved.
5. The method according to claim 4, wherein the implementation manner of optimizing the initial scheduling scheme set by using a hybrid genetic algorithm with the minimum scheduling time as an optimization target based on the scheduling time model and the model constraint further comprises:
screening out a better scheduling scheme subset from the initial scheduling scheme set based on the scheduling time model; the total scheduling time of the preferred scheduling scheme subset is less than other scheduling scheme subsets in the initial scheduling scheme set;
performing the encoding, the interleaving and mutation, the domain switching, and the detection update for each scheduling scheme in the subset of better scheduling schemes to obtain a new subset of better scheduling schemes;
replacing the subset of better scheduling schemes with the subset of new better scheduling schemes to obtain a new set of scheduling schemes.
6. The method of claim 5, wherein the screening out a subset of better scheduling schemes from the set of initial scheduling schemes based on the scheduling time model comprises:
and acquiring the scheduling time of each scheduling scheme in the initial scheduling scheme set based on the scheduling time model, and screening the optimal scheduling scheme subset in the initial scheduling scheme set by adopting a championship method.
7. The scheduling optimization method of a warehousing system according to any one of claims 4 to 6, wherein the implementation manner of detecting whether each new scheduling scheme satisfies a preset optimization judgment condition includes:
and sampling each new scheduling scheme by adopting a simulated annealing method, and detecting whether the sampled scheduling time of each new scheduling scheme meets Metropolis acceptance criteria.
8. The scheduling optimization method for the warehousing system according to any one of claims 4 to 6, wherein a single scheduling scheme includes a serial number of each scheduling task and the number of the scheduling tasks divided by each four-way shuttle, and then the implementation manner of constructing the two-segment chromosome coding corresponding to the scheduling scheme by using the two-segment chromosome coding method includes:
and setting the serial number of each scheduling task as a first code in the double-segment chromosome codes, and setting the number of the scheduling tasks distributed by each four-way shuttle as a second code in the double-segment chromosome codes.
9. The method of claim 8, wherein the implementation of performing intersection and mutation on each two-segment chromosome coding based on the model constraints comprises:
for the double-segment chromosome codes corresponding to each scheduling scheme, adopting a two-point crossing mode to cross the task sequence in the first codes, and not crossing the second codes;
randomly selecting two variation points in the first code, and exchanging the numerical values of the two variation points; randomly selecting a plurality of vehicles from the second codes, and performing neighbor value variation operation on the task number correspondingly divided by the selected vehicles to obtain new double-segment chromosomes; wherein the number of randomly selected vehicles is not more than 4;
and judging whether a new scheduling scheme corresponding to the new double-segment chromosome meets the model constraint in the scheduling time model, if so, executing the subsequent steps, and if not, correcting the new double-segment chromosome operator.
10. The method of claim 9, wherein the modifying the new two-segment chromosomal engineer is performed by:
detecting whether each gene numerical value in the new double-segment chromosome code has 0, if so, obtaining the total number n of the genes with the numerical value of 0, subtracting n from the numerical value of the gene with the maximum numerical value of the double-segment chromosome code, and adding 1 to the numerical value of the gene with the 0 to obtain the repaired double-segment chromosome code;
and obtaining the numerical sum of each gene coded by the repaired double-segment chromosome and the numerical sum of each gene coded by the repaired double-segment chromosome, comparing the two numerical sums, if the former is smaller than the latter, obtaining the difference value of the two numerical sums, and adding the difference value to the numerical value of the minimum value in the repaired double-segment chromosome.
11. An electronic device, comprising:
a memory for storing a computer program;
a processor, communicatively coupled to the memory, for executing the scheduling optimization method of the warehousing system of any of claims 1-10 when the computer program is invoked.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for scheduling optimization of a warehousing system according to any one of claims 1 to 10.
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