CN115239245A - Task scheduling and optimizing method for stereoscopic warehouse - Google Patents

Task scheduling and optimizing method for stereoscopic warehouse Download PDF

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CN115239245A
CN115239245A CN202210927461.8A CN202210927461A CN115239245A CN 115239245 A CN115239245 A CN 115239245A CN 202210927461 A CN202210927461 A CN 202210927461A CN 115239245 A CN115239245 A CN 115239245A
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陶飞
陈雷
程江峰
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Abstract

The invention discloses a task scheduling and optimizing method for a stereoscopic warehouse, which comprises the following steps: step 1, constructing a multi-target stereoscopic warehouse goods space distribution model; step 2, acquiring a current task request, and solving a target goods position address according to a goods position allocation method; step 3, generating a task sequence according to the obtained target goods location address and the task request; step 4, carrying out task sequence optimization on the generated task sequence; and 5, issuing the final optimized task sequence result to a stacker. The invention can obviously improve the goods space distribution and task execution efficiency of the stereoscopic warehouse, reduce the invalid moving distance of the stacker, improve the stability and storage efficiency of the stereoscopic warehouse, and is suitable for the traditional automatic stereoscopic warehouse in-out scheduling.

Description

Task scheduling and optimizing method for stereoscopic warehouse
Technical Field
The invention belongs to the field of industrial digitization and computer science, and particularly relates to a task scheduling and optimizing method for a stereoscopic warehouse.
Background
The production of the manufacturing industry in China has the characteristics of large yield and quick growth, so that the storage of products is particularly important. The traditional warehouse can not meet the high-efficiency production requirement due to low warehousing and scheduling efficiency. An Automatic stereoscopic warehouse (AS/RS) is used AS a novel warehouse technology, and Automatic carrying equipment such AS a stacker And an AGV (Automatic Guided Vehicle) is adopted, so that unmanned cargo Storage is realized, and the Storage efficiency of the stereoscopic warehouse is greatly improved. Therefore, the automatic stereoscopic warehouse has wide application in various industries and becomes an important component of the logistics industry. With the rapid development of the second industry in China and the exponential development of the express delivery industry, the application scenes of the automatic stereoscopic warehouse in China are wider and wider.
After receiving the warehouse entry and exit task request, the automatic stereoscopic warehouse can generate a warehouse entry and exit goods location target address according to the goods location distribution rule. Because the current goods position distribution rule adopted in China is too single, the storage efficiency and the stability and the safety of the stereoscopic warehouse are difficult to be considered, and the finally distributed target goods position is unreasonable. The storage efficiency of an automated stereoscopic warehouse depends on the scheduling policy approach of the warehouse itself. Therefore, it is important to balance the distribution targets of the cargo spaces under a plurality of constraint conditions and finally obtain the optimal target cargo space. The method aims to solve the reasonable target cargo space by researching the cargo space distribution rule of the stereoscopic warehouse and optimizing a cargo space distribution model and a model solving method, reduce the invalid moving distance of the stacker, improve the shelf stability of the stereoscopic warehouse and reduce the processing cost of enterprises, and is a topic with great research significance.
In summary, the existing cargo space allocation method has a single rule and lacks consideration of multiple target factors. And currently, a mode of fixing task sequences is adopted, so that the scheduling efficiency of the stereoscopic warehouse is seriously restricted, and a task sequence optimization method is lacked for adjusting the execution sequence of the task sequences.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the problems of unreasonable goods allocation and low task sequence execution efficiency in the scheduling process of a stacker crane of a stereoscopic warehouse, the task scheduling and optimizing method for the stereoscopic warehouse is provided, and the problems of warehouse-in and warehouse-out goods allocation and task sequence optimization in the scheduling of the stereoscopic warehouse can be effectively solved by adopting a simulated annealing algorithm and an improved genetic-particle swarm optimization algorithm, so that the production efficiency is obviously improved, and the processing cost is obviously saved.
The technical solution of the invention is as follows: a task scheduling and optimizing method for a stereoscopic warehouse comprises the following implementation steps:
step 1, determining a goods location distribution target of a stereoscopic warehouse, a goods location distribution rule of the stereoscopic warehouse and a goods location constraint condition of the stereoscopic warehouse, constructing a multi-target stereoscopic warehouse goods location distribution model according to the goods location distribution target of the stereoscopic warehouse, the goods location distribution rule of the stereoscopic warehouse and the goods location constraint condition of the stereoscopic warehouse, and respectively establishing a multi-target stereoscopic warehouse goods location distribution model for two task types of an ex-warehouse task and an in-warehouse task based on different constraint conditions and targets when the goods location of the stereoscopic warehouse is distributed for the ex-warehouse task and the in-warehouse task; different from the traditional single target scheduling method, the multi-target goods allocation model has a plurality of consideration factors and is more efficient than the traditional method; the distribution targets of the goods space of the multi-target stereoscopic warehouse comprise the task amount of a stacker, the moving distance of the stacker, the goods conditioning time and the height of the center of gravity of a goods shelf; the goods allocation rule of the stereoscopic warehouse comprises first-in first-out; the stereoscopic warehouse goods location distribution constraint conditions comprise the total number of the goods locations of the warehouse, the goods conditioning time and the limits of empty and full goods locations;
step 2, acquiring the state information of all goods spaces of the current stereoscopic warehouse, the current position of the stacker and the task quantity data in real time through the database, and driving the multi-target stereoscopic warehouse goods space distribution model in the step 1 through real-time data to realize real-time synchronization of the stacker, the stereoscopic warehouse and the multi-target stereoscopic warehouse goods space distribution model; acquiring an in-out task request of the current stereoscopic warehouse in real time through a PLC (programmable logic controller) or WCS (trusted communications system) system, converting a multi-target cargo space allocation model into a single-target cargo space allocation model, wherein the target of the single-target cargo space allocation model is the target conversion of the multi-target cargo space allocation model in the step 1And the target function in the single target goods allocation model comprises an ex-warehouse target function and an in-warehouse target function, wherein the ex-warehouse target function is as follows: f outmin ＝μ 1 F 1min -μ 2 F 2max -μ 3 F 3max (ii) a The warehousing objective function is: f inmin ＝μ 4 F 1min +μ 5 F 2min ，F 1min 、F 2max 、F 3max 、F 2min The movement distance of the stacker, the height of the gravity center of the goods shelf, the conditioning time of the goods and the height of the gravity center of the goods shelf in the multi-target goods space distribution model in the step 1 are functionally expressed as mu 1 、μ 2 、μ 3 、μ 4 、μ 5 The method comprises the steps that weighted values of the moving distance of a stacker, the height of the center of gravity of a goods shelf, the conditioning time of goods, the moving distance of the stacker and the height of the center of gravity of the goods shelf are obtained, and the best goods location distribution address is obtained by searching the goods location with the minimum target function in the current goods location address;
step 3, repeating the step 2 according to all the warehouse entry and exit tasks obtained from the PLC until all the tasks are completely distributed; according to the warehouse-in and warehouse-out tasks of each stacker, a task sequence [ x ] of each stacker is constructed 1 ,x 2 ,…,x n ]Wherein n is the number of tasks of the stacker, x n Representing the current nth warehouse entry and exit task to be executed;
step 4, according to the task sequence of each stacker in the step 3, combining the positions of the stackers, the positions of the warehouse entry and exit platforms and the types of operation tasks, namely warehouse exit tasks and warehouse entry tasks, developing task sequence optimization and improving the efficiency of task scheduling of the stereoscopic warehouse; the target of the task sequence optimization is that the moving distance of the stacker is shortest, and the target function of the task sequence optimization is the moving distance value of the stacker; searching the optimal task sequence by adopting an improved genetic-particle swarm algorithm, wherein the task sequence is used as particles in a particle swarm, and the movement distance of a stacker of the task sequence is used as a target function value; the particle updating method adopts the crossing idea of a genetic algorithm, selects two task sequences, and sequentially crosses the tasks of the two task sequences to obtain two brand-new task sequences; after all task sequences are updated according to the updating method, the objective function values of the newly obtained task sequences are recalculated, and the task sequence with the minimum objective function value is used as the optimal task sequence;
step 5, repeating the step 4, and when the threshold value N of the iteration times is reached IterMax And stopping iteration to obtain an optimal task sequence, wherein the optimal task sequence is a task sequence optimization result, and finally, issuing the task sequence optimization result to a stacker for execution.
Further, the specific contents of the multi-target stereoscopic warehouse goods space allocation model in the step 1 are as follows:
(1) Warehouse-out task multi-target stereoscopic warehouse goods space allocation model
Aiming at the problem of goods position distribution, a two-section distribution method is adopted, namely, firstly dividing a roadway and then dividing goods positions; firstly, according to the task amount target min (Tv) of the stacker j ) Wherein Tv is j Representing the task amount of the j-th stacker, preferentially distributing the tasks to the stacker with the least task amount, and then distributing the cargo space according to a cargo space distribution target function, wherein the target function comprises the moving distance of the stacker
Figure BDA0003780266260000031
Wherein x i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; height of center of gravity F of goods shelf 2max (x i ,y i ,z i )＝x i Wherein x is i 、y i 、z i Respectively representing the layer, the row and the line of the ith goods position address; conditioning time of goods F 3max ＝Ts i Wherein Ts i Representing the conditioning time of the current goods in the stereoscopic warehouse; and finally, establishing a multi-target goods space distribution model of the ex-warehouse task according to the constraint conditions, wherein the multi-target goods space distribution model comprises the following steps:
Figure BDA0003780266260000032
wherein, sf i (w i ,x i ,y i ,z i ) E.g. Sf represents the set of full goods positions in the stereoscopic warehouseClosing the full goods space limit, wherein w, x, y and Z respectively represent a roadway, a layer, a row and a row of goods space addresses, Z represents a set of goods space addresses, w is more than or equal to 1 and less than or equal to 5,1 and less than or equal to 9,0 and less than or equal to y is less than or equal to 1,1 and less than or equal to Z is less than or equal to 22, namely the total number of the warehouse goods spaces is limited; t is the cargo conditioning time limit;
(2) Warehousing task multi-target stereoscopic warehouse goods space allocation model
Aiming at the problem of goods position distribution, a two-section distribution method is adopted, namely, firstly dividing a roadway and then dividing goods positions; firstly, according to the task amount target min (Tv) of the stacker j ) Wherein Tv j Representing the task amount of the jth stacker, and preferentially distributing the tasks to the stacker with the least task amount; then, the goods position is distributed according to a goods position distribution target function, wherein the target function comprises the moving distance of the stacker
Figure BDA0003780266260000033
Wherein x i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; stability of the goods shelf F 2max (x i ,y i ,z i )＝x i ，x i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; and finally, establishing a multi-target goods location distribution model of the warehousing task according to the constraint conditions to construct a model solving domain as follows:
Figure BDA0003780266260000041
wherein, se i (w i ,x i ,y i ,z i ) And e Se represents the set of all empty goods positions in the stereoscopic warehouse, namely the empty goods position limit, w, x, y and Z respectively represent the roadway, the layer, the column and the row of goods position addresses, Z represents the set of goods position addresses, w is more than or equal to 1 and less than or equal to 5,1 and less than or equal to x is more than or equal to 9,0 and less than or equal to y is more than or equal to 1,1 and less than or equal to Z is more than or equal to 22, namely the warehouse goods position total number limit.
Further, the improved genetic-particle swarm algorithm in the step 4 is specifically realized as follows:
(1) Taking the current task sequence as one particle in the iterative initial particle swarm, and taking the rest other particles as the randomly generated task sequence;
(2) In order to obtain a task sequence with the shortest moving distance of the stacker under the premise of current task, the objective function is the sum of the moving distances of the stacker for executing the task sequence, and the fitness function is the same as the objective function, and comprises the following steps:
Figure BDA0003780266260000042
wherein n represents the number of warehousing tasks, m represents the number of ex-warehouse tasks, and (x) i1 ,y i1 ,z i1 ),(x i2 ,y i2 ,z i2 ) Representing the coordinates of two cargo spaces in a task pair of a compound command, (x) j1 ,y j1 ,z j1 ),(x j2 ,y j2 ,z j2 ) Representing the coordinates of the cargo space in a single command task;
(3) Updating rules of the particle swarm, including self-learning probability w of the task sequence and learning probability c of the locally optimal task sequence 1 And global optimal task sequence learning probability c 2 The original task sequence is used as one parent task sequence 1 of the cross operation, the other parent task sequence 2 receives reverse (parent 1) of the current task sequence with a certain probability (the term of the function is indicated by brackets), the local optimal task sequence pbest and the global optimal task sequence gbest, and the probability of the selection of the parent task sequence 2 is as follows:
Figure BDA0003780266260000043
and when the parent task sequences parent1 and parent2 are selected, generating a new task sequence in a random sequence crossing mode.
Compared with the prior art, the invention has the advantages that:
(1) According to the multi-target stereoscopic warehouse goods space distribution model, a plurality of targets for stereoscopic warehouse goods space distribution are constructed. Compared with the previous single target distribution model, the method can comprehensively consider a plurality of targets distributed by the goods space of the stereoscopic warehouse, avoid the problem that a certain objective function value is too low, and take the dispatching efficiency and the stability and the safety of the stereoscopic warehouse into consideration.
(2) The invention is based on the genetic-particle swarm fusion algorithm, introduces the cross thought of the genetic algorithm into the particle swarm algorithm, and can more quickly and accurately obtain the optimal task sequence compared with other algorithms. Meanwhile, compared with the transmitted 'first-come first-serve' and 'fixed-order' task sequence execution method, the method can obviously reduce the moving distance of the stacker and improve the overall scheduling efficiency of the stereoscopic warehouse.
Drawings
Fig. 1 is a block diagram of a task scheduling and optimizing method for a stereoscopic warehouse according to the present invention;
FIG. 2 is a multi-objective stereoscopic warehouse cargo space allocation model of the present invention;
FIG. 3 is a flow chart of an improved genetic particle swarm algorithm of the present invention.
Detailed Description
The invention relates to a task scheduling and optimizing method for a stereoscopic warehouse, and provides the task scheduling and optimizing method for the stereoscopic warehouse aiming at the problems of unreasonable goods allocation and low task sequence execution efficiency in the scheduling process of a stacker of the stereoscopic warehouse. By adopting a simulated annealing algorithm and a genetic-particle swarm fusion algorithm, the problems of warehouse-in and warehouse-out goods location distribution and task sequence optimization in stereoscopic warehouse scheduling can be effectively solved, the production efficiency can be obviously improved, and the processing cost can be obviously saved.
As shown in fig. 1, the task scheduling and optimizing method for a stereoscopic warehouse of the present invention is specifically implemented as follows:
step 1, determining a goods location distribution target of a stereoscopic warehouse, a goods location distribution rule of the stereoscopic warehouse and a goods location constraint condition of the stereoscopic warehouse, constructing a multi-target stereoscopic warehouse goods location distribution model according to the goods location distribution target of the stereoscopic warehouse, the goods location distribution rule of the stereoscopic warehouse and the goods location constraint condition of the stereoscopic warehouse, and respectively establishing a multi-target stereoscopic warehouse goods location distribution model for two task types of an ex-warehouse task and an in-warehouse task based on different constraint conditions and targets when the goods location of the stereoscopic warehouse is distributed for the ex-warehouse task and the in-warehouse task; different from the traditional single target scheduling method, the multi-target goods allocation model has a plurality of consideration factors and is more efficient than the traditional method; the distribution targets of the goods space of the multi-target stereoscopic warehouse comprise the task amount of a stacker, the moving distance of the stacker, the goods conditioning time and the height of the center of gravity of a goods shelf; the goods allocation rule of the stereoscopic warehouse comprises first-in first-out; the stereoscopic warehouse goods location distribution constraint conditions comprise the total number of the goods locations of the warehouse, the goods conditioning time and the limits of empty and full goods locations;
as shown in fig. 2, the specific contents of the multi-target stereoscopic warehouse cargo space allocation model are as follows:
most of the goods space allocation methods adopted by the current stereoscopic warehouse are single targets or single rules, the considered factors are not comprehensive enough, and the efficiency and the safety of the whole dispatching are influenced. The problem is well solved by the multi-target cargo space allocation model established in the step 1. Based on the difference of constraint conditions and targets of the ex-warehouse task and the in-warehouse task when allocating the goods space, respectively establishing a multi-target stereoscopic warehouse goods space allocation model for the two task types of the ex-warehouse task and the in-warehouse task:
(1) Warehouse-out task multi-target stereoscopic warehouse goods space allocation model
A warehousing task multi-target stereoscopic warehouse cargo space allocation model is shown in fig. 2, and a two-section allocation method is adopted for the cargo space allocation problem, namely, firstly dividing a roadway and then dividing cargo spaces; firstly, according to the task amount target min (Tv) of the stacker j ) Wherein Tv j Representing the task amount of the j-th stacker, preferentially distributing the tasks to the stacker with the least task amount, and then distributing the cargo space according to a cargo space distribution target function, wherein the target function comprises the moving distance of the stacker in the figure 2
Figure BDA0003780266260000061
Wherein x i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; height of center of gravity F of the shelf in FIG. 2 2max (x i ,y i ,z i )＝x i Wherein x is i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; conditioning time F of goods in fig. 2 3max ＝Ts i Wherein Ts i Representing the conditioning time of the current goods in the stereoscopic warehouse; and finally, establishing a multi-target goods space distribution model of the ex-warehouse task according to the constraint conditions, wherein the multi-target goods space distribution model comprises the following steps:
Figure BDA0003780266260000062
wherein, sf i (w i ,x i ,y i ,z i ) E.g. Sf represents a set of full positions in the stereoscopic warehouse, namely the full position limit in the step 1, w, x, y and Z respectively represent a roadway, a layer, a column and a row of a position address, Z represents a set of position addresses of goods, w is more than or equal to 1 and less than or equal to 5,1 and less than or equal to x is more than or equal to 9,0 and less than or equal to y is more than or equal to 1,1 and less than or equal to Z is more than or equal to 22, namely the total number limit of the positions in the warehouse in the step 1; and T is the cargo conditioning time limit in the step 1.
(2) Warehouse entry task multi-target stereoscopic warehouse goods space distribution model
A warehousing task multi-target stereoscopic warehouse cargo space allocation model is shown in fig. 2, and a two-section allocation method is adopted for the cargo space allocation problem, namely, firstly dividing a roadway and then dividing cargo spaces; firstly, according to the task amount target min (Tv) of the stacker j ) Wherein Tv j Representing the task quantity of the jth stacker, and preferentially distributing the tasks to the stacker with the least task quantity; then, the goods position distribution is carried out according to a goods position distribution target function, wherein the target function comprises the moving distance of the stacker in the figure 2
Figure BDA0003780266260000063
Wherein x i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; shelf stability F in FIG. 2 2max (x i ,y i ,z i )＝x i ，x i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; and finally, establishing a multi-target goods location distribution model of the warehousing task according to the constraint conditions to construct a model solving domain as follows:
Figure BDA0003780266260000064
wherein, se i (w i ,x i ,y i ,z i ) The epsilon Se represents a set of all empty goods places in the stereoscopic warehouse, namely the empty goods place limit in the step 1, w, x, y and Z respectively represent a roadway, a layer, a column and a row of goods place addresses, Z represents a set of goods place addresses, w is more than or equal to 1 and less than or equal to 5,1 and less than or equal to x is more than or equal to 9,0 and less than or equal to y is more than or equal to 1,1 and less than or equal to Z is more than or equal to 22, namely the total goods place limit of the warehouse in the step 1;
step 2, acquiring the state information of all goods spaces of the current stereoscopic warehouse, the current position of the stacker and the task quantity data in real time through the database, and driving the multi-target stereoscopic warehouse goods space distribution model in the step 1 through real-time data to realize real-time synchronization of the stacker, the stereoscopic warehouse and the multi-target stereoscopic warehouse goods space distribution model; acquiring an in-out task request of a current stereoscopic warehouse in real time through a Programmable Logic Controller (PLC) or a Warehouse Control System (WCS) system, converting a multi-target cargo space distribution model into an individual target cargo space distribution model, wherein the target of the individual target cargo space distribution model is formed by converting the target of the multi-target cargo space distribution model in the step 1, the target function in the individual target cargo space distribution model comprises an out-warehouse target function and an in-warehouse target function, and the out-warehouse target function is as follows: f outmin ＝μ 1 F 1min -μ 2 F 2max -μ 3 F 3max (ii) a The warehousing objective function is: f inmin ＝μ 4 F 1min +μ 5 F 2min ，F 1min 、F 2max 、F 3max 、F 2min The movement distance of the stacker, the height of the gravity center of the goods shelf, the conditioning time of the goods and the height of the gravity center of the goods shelf in the multi-target goods space distribution model in the step 1 are functionally expressed as mu 1 、μ 2 、μ 3 、μ 4 、μ 5 The weight values of the moving distance of the stacker, the gravity height of the goods shelf, the conditioning time of the goods, the moving distance of the stacker and the gravity height of the goods shelf are calculated, and the goods position with the minimum target function in the current goods position address is searchedThe optimal goods allocation address;
step 3, repeating the step 2 according to all the warehouse-in and warehouse-out tasks obtained from the PLC until all the tasks finish the goods allocation; according to the warehouse-in and warehouse-out tasks of each stacker, a task sequence [ x ] of each stacker is constructed 1 ,x 2 ,…,x n ]Where n is the number of tasks of the stacker, x n Representing the current nth warehouse entry and exit task to be executed;
step 4, according to the task sequence of each stacker in the step 3, combining the positions of the stackers, the positions of the stackers entering and exiting the warehouse, and the types of the operation tasks, carrying out task sequence optimization, specifically realizing a process as shown in fig. 3, and improving the efficiency of task scheduling of the stereoscopic warehouse; the traditional stereoscopic warehouse task sequence execution adopts a fixed sequence mode, and the step of task sequence optimization does not exist, so that the invention innovatively provides the task sequence optimization to improve the efficiency of task scheduling of the stereoscopic warehouse. The target of the task sequence optimization is that the moving distance of the stacker is shortest, and the target function of the task sequence optimization is the moving distance value of the stacker. The optimal task sequence is searched by adopting an improved genetic-particle swarm algorithm. And taking the task sequence as a particle in the particle swarm, and taking the movement distance of the stacker of the task sequence as an objective function value. The particle updating method in fig. 3 adopts the crossing idea of the genetic algorithm, selects two task sequences, and sequentially crosses the tasks of the two task sequences, thereby obtaining two completely new task sequences. And after all the task sequences are updated according to the updating method, recalculating the objective function values of the newly obtained task sequences, and taking the task sequence with the minimum objective function value as the optimal task sequence.
The improved genetic-particle swarm optimization is specifically realized as follows:
(1) Initializing a particle swarm in the first step in fig. 3, wherein a current task sequence is used as one particle in an iterative initial particle swarm, and all the rest other particles are randomly generated task sequences;
(2) In order to obtain a task sequence with the shortest moving distance of the stacker under the premise of the current task, the objective function is the sum of the moving distances of the stacker for executing the task sequence, and the fitness function for calculating the fitness of the particles in fig. 3 is the same as the objective function, and is as follows:
Figure BDA0003780266260000081
wherein n represents the number of warehousing tasks, m represents the number of ex-warehouse tasks, and (x) i1 ,y i1 ,z i1 ),(x i2 ,y i2 ,z i2 ) Representing the coordinates of two cargo spaces in a task pair of a compound command, (x) j1 ,y j1 ,z j1 ),(x j2 ,y j2 ,z j2 ) Representing the coordinates of the cargo space in a single command task;
(3) The update rule of the particle group in FIG. 3 retains the conventional parameters, such as the self-learning probability w of the task sequence, and the local optimal task sequence learning probability c 1 And global optimal task sequence learning probability c 2 The original task sequence is used as one parent task sequence 1 of the cross operation, the other parent task sequence 2 receives the reverse sequence reverse (parent 1) of the current task sequence, the local optimal task sequence pbest and the global optimal task sequence gbest with a certain probability, and the probability of the parent task sequence 2 is as follows:
Figure BDA0003780266260000082
when two parent task sequences (parent 1 and parent 2) are selected, a new task sequence is generated in a random sequence crossing mode;
step 5, repeating the step 4, and when the threshold value N of the iteration times is reached IterMax And stopping iteration to obtain an optimal task sequence, wherein the optimal task sequence is a task sequence optimization result. And finally, the task sequence optimization result is sent to a stacker for execution.
In a word, the invention improves the genetic-particle swarm algorithm, constructs a multi-target stereoscopic warehouse goods allocation model, provides a goods allocation method for a stereoscopic warehouse, and optimizes a task sequence by adopting the genetic-particle swarm algorithm so as to improve the scheduling efficiency of the stereoscopic warehouse and reduce the production cost.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (3)

1. A task scheduling and optimizing method for a stereoscopic warehouse is characterized by comprising the following steps:
step 1, determining a goods space allocation target of a stereoscopic warehouse, a goods space allocation rule of the stereoscopic warehouse and a goods space constraint condition of the stereoscopic warehouse, constructing a multi-target stereoscopic warehouse goods space allocation model according to the goods space allocation target of the stereoscopic warehouse, the goods space allocation rule of the stereoscopic warehouse and the goods space constraint condition of the stereoscopic warehouse, and respectively establishing a multi-target stereoscopic warehouse goods space allocation model for two task types of an ex-warehouse task and an in-warehouse task based on different constraint conditions and targets when the goods space of the stereoscopic warehouse is allocated for the ex-warehouse task and the in-warehouse task; different from the traditional single target scheduling method, the multi-target goods allocation model has a plurality of consideration factors and is more efficient than the traditional method; the distribution targets of the goods space of the multi-target stereoscopic warehouse comprise the task amount of a stacker, the moving distance of the stacker, the goods conditioning time and the height of the center of gravity of a goods shelf; the goods allocation rule of the stereoscopic warehouse comprises first-in first-out; the stereoscopic warehouse goods location distribution constraint conditions comprise the total number of the goods locations of the warehouse, the goods conditioning time and the limits of empty and full goods locations;
step 2, acquiring the state information of all goods spaces of the current stereoscopic warehouse, the current position of the stacker and the task quantity data in real time through the database, and driving the multi-target stereoscopic warehouse goods space distribution model in the step 1 through real-time data to realize real-time synchronization of the stacker, the stereoscopic warehouse and the multi-target stereoscopic warehouse goods space distribution model; by means of PLC or WCS systemsThe method comprises the steps of obtaining an in-out task request of a current stereoscopic warehouse in real time, converting a multi-target cargo space allocation model into a single-target cargo space allocation model, wherein a target of the single-target cargo space allocation model is formed by converting a target of the multi-target cargo space allocation model in the step 1, a target function in the single-target cargo space allocation model comprises an out-warehouse target function and an in-warehouse target function, and the out-warehouse target function is as follows: f outmin ＝μ 1 F 1min -μ 2 F 2max -μ 3 F 3max (ii) a The warehousing objective function is: f inmin ＝μ 4 F 1min +μ 5 F 2min ，F 1min 、F 2max 、F 3max 、F 2min The functional representation of the movement distance of the stacker, the height of the gravity center of the goods shelf, the conditioning time of the goods and the height of the gravity center of the goods shelf in the multi-target goods space distribution model in the step 1 is mu 1 、μ 2 、μ 3 、μ 4 、μ 5 The method comprises the steps that weighted values of the moving distance of a stacker, the height of the center of gravity of a goods shelf, the conditioning time of goods, the moving distance of the stacker and the height of the center of gravity of the goods shelf are obtained, and the best goods location distribution address is obtained by searching the goods location with the minimum target function in the current goods location address;
step 3, repeating the step 2 according to all the warehouse-in and warehouse-out tasks obtained from the PLC until all the tasks finish the goods allocation; according to the warehouse-in and warehouse-out tasks of each stacker, a task sequence [ x ] of each stacker is constructed 1 ,x 2 ,…,x n ]Where n is the number of tasks of the stacker, x n Representing the current nth warehouse entry and exit task to be executed;
step 4, according to the task sequence of each stacker in the step 3, combining the positions of the stackers, the positions of the warehouse entry and exit platforms and the types of operation tasks, namely warehouse exit tasks and warehouse entry tasks, developing task sequence optimization and improving the efficiency of task scheduling of the stereoscopic warehouse; the target of the task sequence optimization is that the moving distance of the stacker is shortest, and the target function of the task sequence optimization is the moving distance value of the stacker; searching the optimal task sequence by adopting an improved genetic-particle swarm algorithm, wherein the task sequence is used as particles in a particle swarm, and the movement distance of a stacker of the task sequence is used as a target function value; the particle updating method adopts the crossing idea of a genetic algorithm, selects two task sequences, and sequentially crosses the tasks of the two task sequences to obtain two brand-new task sequences; after all task sequences are updated according to the updating method, the objective function values of the newly obtained task sequences are recalculated, and the task sequence with the minimum objective function value is used as the optimal task sequence;
step 5, repeating the step 4, and when the threshold value N of the iteration times is reached IterMax And stopping iteration to obtain an optimal task sequence, wherein the optimal task sequence is a task sequence optimization result, and finally, issuing the task sequence optimization result to a stacker for execution.
2. The stereoscopic warehouse-oriented task scheduling and optimizing method according to claim 1, wherein: the specific contents of the multi-target stereoscopic warehouse goods space distribution model in the step 1 are as follows:
(1) Warehouse-out task multi-target stereoscopic warehouse goods space allocation model
Aiming at the problem of goods position distribution, a two-section distribution method is adopted, namely, firstly dividing a roadway and then dividing goods positions; firstly, according to the task amount target min (Tv) of the stacker j ) Wherein Tv j Representing the task amount of the j-th stacker, preferentially distributing the tasks to the stacker with the least task amount, and then distributing the cargo space according to a cargo space distribution target function, wherein the target function comprises the moving distance of the stacker
Figure FDA0003780266250000021
Wherein x i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; height of center of gravity F of goods shelf 2max (x i ,y i ,z i )＝x i Wherein x is i 、y i 、z i Respectively representing the layer, the row and the line of the ith goods position address; conditioning time of goods F 3max ＝Ts i Wherein Ts i Representing the conditioning time of the current goods in the stereoscopic warehouse; and finally, establishing a multi-target goods space allocation model of the ex-warehouse task according to constraint conditionsThe following are:
Figure FDA0003780266250000022
wherein, sf i (w i ,x i ,y i ,z i ) The epsilon Sf represents a set of full positions in the stereoscopic warehouse, namely the limit of the full positions, w, x, y and Z respectively represent a roadway, a layer, a column and a row of addresses of the full positions, Z represents a set of addresses of the full positions, w is more than or equal to 1 and less than or equal to 5,1 and less than or equal to x is more than or equal to 9,0 and less than or equal to y is more than or equal to 1,1 and less than or equal to Z is less than or equal to 22, namely the limit of the total number of the warehouse and the full positions; t is the cargo conditioning time limit;
(2) Warehousing task multi-target stereoscopic warehouse goods space allocation model
Aiming at the problem of goods position distribution, a two-section distribution method is adopted, namely, firstly dividing a roadway and then dividing goods positions; firstly, according to the task amount target min (Tv) of the stacker j ) Wherein Tv j Representing the task quantity of the jth stacker, and preferentially distributing the tasks to the stacker with the least task quantity; then, the goods position is distributed according to a goods position distribution target function, wherein the target function comprises the moving distance of the stacker
Figure FDA0003780266250000023
Wherein x i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; stability of the goods shelf F 2max (x i ,y i ,z i )＝x i ，x i 、y i 、z i The layer, the row and the row respectively represent the ith goods position address; and finally, establishing a multi-target goods location distribution model of the warehousing task according to the constraint conditions to construct a model solving domain as follows:
Figure FDA0003780266250000031
wherein, se i (w i ,x i ,y i ,z i ) The epsilon Se represents the set of all empty goods positions in the stereoscopic warehouse, namely the empty goods positionsAnd w, x, y and Z respectively represent a roadway, a layer, a column and a row of the goods location address, Z represents a set of goods location addresses, w is more than or equal to 1 and less than or equal to 5,1 and less than or equal to 9,0 and less than or equal to 1,1 and less than or equal to Z and less than or equal to 22, namely the total number of the warehouse goods locations is limited.
3. The stereoscopic warehouse-oriented task scheduling and optimizing method according to claim 1, wherein the improved genetic-particle swarm algorithm in the step 4 is specifically implemented as follows:
(1) Taking the current task sequence as one particle in the iterative initial particle swarm, and taking the rest other particles as the randomly generated task sequence;
(2) In order to obtain a task sequence with the shortest moving distance of the stacker under the premise of the current task, the objective function is the sum of the moving distances of the stacker for executing the task sequence, and the fitness function is the same as the objective function, and comprises the following steps:
Figure FDA0003780266250000032
wherein n represents the number of warehousing tasks, m represents the number of ex-warehouse tasks, and (x) i1 ,y i1 ,z i1 ),(x i2 ,y i2 ,z i2 ) Representing the coordinates of two cargo spaces in a task pair of a compound command, (x) j1 ,y j1 ,z j1 ),(x j2 ,y j2 ,z j2 ) Representing the coordinates of the cargo space in a single command task;
(3) The updating rule of the particle swarm comprises a self-learning probability w of the task sequence and a local optimal task sequence learning probability c 1 And global optimal task sequence learning probability c 2 The original task sequence is used as one parent task sequence 1 of the cross operation, the other parent task sequence 2 receives the reverse order reverse (parent 1) of the current task sequence, the local optimal task sequence pbest and the global optimal task sequence gbest with a certain probability, and the probability of the selection of the parent sequence 2 is as follows:
Figure FDA0003780266250000033
and when the two parent task sequences parent1 and parent2 are selected, generating a new task sequence in a random sequence crossing mode.
CN202210927461.8A 2022-08-03 2022-08-03 Task scheduling and optimizing method for stereoscopic warehouse Pending CN115239245A (en)

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CN116873431A (en) * 2023-07-07 2023-10-13 湘南学院 Multi-heavy-load AGV storage and transportation method based on rock plate intelligent warehouse
CN117592760A (en) * 2024-01-18 2024-02-23 湖北浩蓝智造科技有限公司 Method, system, equipment and medium for distributing warehouse-in and warehouse-out tasks of stacker

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116873431A (en) * 2023-07-07 2023-10-13 湘南学院 Multi-heavy-load AGV storage and transportation method based on rock plate intelligent warehouse
CN116873431B (en) * 2023-07-07 2024-02-06 湘南学院 Multi-heavy-load AGV storage and transportation method based on rock plate intelligent warehouse
CN117592760A (en) * 2024-01-18 2024-02-23 湖北浩蓝智造科技有限公司 Method, system, equipment and medium for distributing warehouse-in and warehouse-out tasks of stacker
CN117592760B (en) * 2024-01-18 2024-04-09 湖北浩蓝智造科技有限公司 Method, system, equipment and medium for distributing warehouse-in and warehouse-out tasks of stacker

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