CN115660551A - Multi-AGV scheduling optimization method and system for power grid measurement material unattended warehouse - Google Patents
Multi-AGV scheduling optimization method and system for power grid measurement material unattended warehouse Download PDFInfo
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Abstract
The invention discloses a multi-AGV scheduling optimization method and a multi-AGV scheduling optimization system for an unattended warehouse for power grid material metering, wherein the method comprises the following steps: constructing a double-layer planning model comprising an order distribution layer and an AGV task scheduling planning layer; the AGV task scheduling planning system comprises an AGV task scheduling planning layer, an order distribution layer, an AGV task scheduling planning layer and a planning layer, wherein the order distribution layer is an upper layer, and the AGV task scheduling planning layer is a lower layer; solving the order distribution problem of the order distribution layer by adopting a wolf optimization algorithm; and solving the problem of AGV trolley transportation task planning of the AGV trolley transportation task planning layer by adopting a whale optimization algorithm. The method mainly divides the problem into two parts of order allocation and AGV task scheduling planning, and adopts different algorithms to solve according to different optimization targets. The dispatching optimization method for multiple AGV of the unattended warehouse is provided for management of the metering warehouse center, the operation efficiency of the warehouse can be effectively improved, the warehousing operation cost in actual production can be reduced, and a basis is provided for intellectualization of warehouse management.
Description
Technical Field
The invention relates to the technical field of storage AGV scheduling, in particular to a multi-AGV scheduling optimization method and system for an unattended warehouse for power grid measurement materials.
Background
Warehouse management is to effectively control and manage a series of activities such as receiving, storing and delivering goods in a warehouse, maintain the goods in the warehouse and ensure normal operation of daily operation activities. The traditional manual storage management mode has the problems of low efficiency, easy 'bin explosion' and the like.
In recent years, with the rapid development of electronic commerce, the types of commodities in a warehouse are more and more, and the storage location distribution situation is more complicated. In order to improve the order sorting efficiency and reduce the cost, the unmanned warehouse is gradually taken as the development direction and the target of the automatic warehouse logistics system, and domestic and foreign e-commerce enterprises such as the capital and amazon and the like have started to use the automatic unmanned warehouse. An Automated Guided Vehicle (AGV) plays an indispensable role in an unmanned warehouse as one of important vehicles for warehouse logistics. The AVG can greatly improve the picking operation efficiency and save manpower, so that the dispatching problem of the AGV in the unmanned storehouse is the core problem to be solved at present.
The existing AGV dispatching research has the defects that the problem of planning of task distribution paths cannot be comprehensively considered, the task distribution is unreasonable, the consideration of avoiding congestion is lacked, and the like. Therefore, a multi-AGV scheduling optimization method for an unattended warehouse for power grid measurement of materials is needed to effectively improve the operation efficiency in the aspect of material scheduling of the measurement center warehouse management.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and title of the application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The invention is provided in view of the problems that the conventional AGV scheduling research fails to comprehensively consider task allocation path planning, is unreasonable in task allocation and lacks consideration for avoiding congestion.
Therefore, the invention aims to provide a multi-AGV scheduling optimization method for an unattended warehouse for power grid measurement materials.
In order to solve the technical problems, the invention provides the following technical scheme:
the invention relates to a multi-AGV scheduling optimization method for an unattended warehouse for power grid measurement materials, which comprises the following steps: constructing a double-layer planning model comprising an order distribution layer and an AGV task scheduling planning layer;
the AGV task scheduling planning layer is a lower layer;
solving the order distribution problem of the order distribution layer by adopting a wolf optimization algorithm;
and solving the problem of AGV trolley transportation task planning of the AGV trolley transportation task planning layer by adopting a whale optimization algorithm.
The invention relates to a multi-AGV scheduling optimization method for a power grid metering material unattended warehouse, which comprises the following steps: the two-level planning model includes a two-level planning model,
the optimization goal of the upper level is to minimize the total distance between the picking station and the tray;
the optimization goal of the lower layer is to make the AGV complete the task most quickly.
The invention relates to a multi-AGV scheduling optimization method for an unattended warehouse for power grid measurement materials, which comprises the following steps: the optimization goal of the upper level is to minimize the total distance between the picking station and the pallet including,
obtaining a distance matrix of the positions of the tray shelves and the stations through an A-x algorithm, obtaining a mapping relation between each shelf and each picking station according to the distance matrix by taking the shortest path as a priority, meeting the corresponding shelf set of the demands of all the picking stations, and obtaining the total path length of the shelves corresponding to the corresponding stations, namely the path length of different orders on different picking stations;
the route lengths of different orders on different picking stations are optimized through a wolf optimization algorithm, each group of mapping relation between the orders and the stations is obtained, and efficient order station distribution is achieved.
The invention relates to a multi-AGV scheduling optimization method for an unattended warehouse for power grid measurement materials, which comprises the following steps: the lower level optimization objective is to maximize the AGV's task performance,
setting each group of mapping relation between the order and the station obtained by the upper layer as a task, and numbering the tasks;
distributing different tasks to the AGV to carry, and simultaneously obtaining all the distance lengths from an initial point to the completion of the carrying tasks of the AGV by utilizing an A-star algorithm;
and (3) optimizing the distribution of the carrying tasks of the AGV cars by combining a whale optimization algorithm by taking all the distance lengths from the initial point to the completion of the carrying tasks of all the AGV cars as optimization conditions to obtain the optimal carrying tasks and corresponding paths of the AGV cars.
The invention relates to a multi-AGV scheduling optimization method for an unattended warehouse for power grid measurement materials, which comprises the following steps: solving the order allocation problem of the order allocation layer using the grayish optimization algorithm includes,
the objective function for minimizing the order transit distance is constructed as follows:
wherein q is wp For the transport distance from the pallet w to the picking station p, x wp Is a variable from 0 to 1;
calculating an objective function for minimizing the transportation distance of the order according to the constraint condition;
wherein all constraints need to be satisfied.
The invention relates to a multi-AGV scheduling optimization method for a power grid metering material unattended warehouse, which comprises the following steps: the constraint conditions include the number of the first and second constraints,
the quantity of the commodities sent to the picking station p is equal to the demand quantity of the picking station p;
the quantity of the commodities i conveyed to the picking station p by the trays w is less than or equal to that of the commodities i in each tray w;
judging whether the commodity i of the picking station p is constrained by a 0-1 variable obtained by calculation provided by the tray w;
the number of transports is constrained.
The invention relates to a multi-AGV scheduling optimization method for an unattended warehouse for power grid measurement materials, which comprises the following steps: the problem of solving the AGV car transportation task planning of the AGV car transportation task planning layer by adopting a whale optimization algorithm comprises the following steps of,
an objective function is constructed that minimizes AGV transport distance as follows:
wherein,
is a 0-1 variable constraint;
if the goods transported by the pallet w to the picking station p are transported by the trolley k, then
Is 1;
if the goods transported by the pallet w to the picking station p are transported by the trolley k, then
Is 0;
calculating an objective function for minimizing the transport distance of the AGV according to the following constraint conditions;
the number of the AGV trolleys which can be used at any time is less than or equal to the number of the idle trolleys, and the specific formula is as follows:
wherein,
the number of AGV trolleys which are idle at the time t;
the AGV trolley transports the tray w to a picking station p, then finishes picking and then needs to return to the goods position of the storage tray w, and the specific formula is as follows:
wherein,
and
corresponding;
the number of AGV trolleys arriving at the picking station at any time is smaller than the maximum temporary storage of the picking station, and the specific formula is as follows:
wherein, b is the maximum temporary storage of the picking station;
each pallet is transported by only one AGV car at any time, and the specific formula is as follows:
in a second aspect, an embodiment of the present invention provides a multiple AGV scheduling optimization system for an unattended warehouse for power grid measurement materials, including,
the model building module is used for building a double-layer planning model comprising an order distribution layer and an AGV task scheduling planning layer;
and the algorithm solving module is used for solving the order distribution problem of the order distribution layer by adopting a wolf optimization algorithm and solving the problem of AGV car transportation task planning of the AGV car transportation task planning layer by adopting a whale optimization algorithm.
In a third aspect, an embodiment of the present invention provides a computing device, including:
a memory and a processor;
the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, and the computer executable instructions when executed by the processor realize the steps of the power grid metering material unattended warehouse multi-AGV dispatching optimization method in any one of claims 1 to 7.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the method for optimizing multiple AGV scheduling in an unattended warehouse for power grid metering supplies according to any one of claims 1 to 7 are implemented.
The invention has the beneficial effects that: the invention constructs a double-layer optimization model taking the shortest path as a target aiming at complex and coupled relations among orders and stations, stations and shelves, shelves and AGVs, mainly divides the problem into two parts of order allocation and AGV task scheduling planning, and adopts different algorithms to solve according to different optimization targets. The dispatching optimization method for multiple AGV of the unattended warehouse is provided for management of the metering warehouse center, the operation efficiency of the warehouse can be effectively improved, the warehousing operation cost in actual production can be reduced, and a basis is provided for intellectualization of warehouse management.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a flowchart of a multi-AGV scheduling optimization method for an unattended warehouse for power grid material metering according to the present invention.
Fig. 2 is a schematic diagram of rasterization of a warehouse map of the power grid metering material unattended warehouse multi-AGV scheduling optimization method.
Fig. 3 is a schematic diagram of an order allocation and AGV delivery double-layer model of the power grid metering material unattended warehouse multi-AGV scheduling optimization method and system.
Fig. 4 is a specific flowchart of a double-layer model and double-layer model solution of the power grid measurement material unattended warehouse multi-AGV scheduling optimization method.
Fig. 5 is a convergence curve diagram of an order distribution path of the power grid metering material unattended warehouse multi-AGV scheduling optimization method.
Fig. 6 is a convergent curve diagram of an AGV order distribution fitness function of the power grid metering material unattended warehouse multi-AGV scheduling optimization method of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as specifically described herein, and it will be appreciated by those skilled in the art that the present invention may be practiced without departing from the spirit and scope of the present invention and that the present invention is not limited by the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Furthermore, the present invention is described in detail with reference to the drawings, and for convenience of illustration, the cross-sectional views illustrating the device structures are not enlarged partially according to the general scale when describing the embodiments of the present invention, and the drawings are only exemplary, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Example 1
Referring to fig. 1, an embodiment of the present invention provides a multiple AGV scheduling optimization method for an unattended warehouse for power grid measurement materials, including:
as shown in FIG. 1, the process of the present invention is as follows:
s1: and constructing a double-layer planning model comprising an order distribution layer and an AGV task scheduling planning layer. It should be noted that:
firstly, the map of the warehouse is rasterized, images of the warehouse nodes and edges are constructed, and the positions in the grid are numbered.
Setting a total of Q orders in the warehouse, wherein Q belongs to Q; w trays W belongs to W; i commodities, I belongs to I; v is the set of AGV feasible nodes.
As shown in FIG. 2, the grid lines are the path nodes that the AGV may freely pass through; the middle 8 large rectangular squares represent a storage area, and a tray or a common shelf can be placed in the storage area; white squares represent reserved nodes, black squares are obstacles, and the AGV cannot reach the places; six rectangular areas formed by three small squares are picking station nodes, and a plurality of tray parking positions are generally arranged at the places where the picking robots pack commodities and then take the commodities out of a warehouse from a conveyor belt. Meanwhile, the positions in the grid are represented by serial numbers, specifically, the positions are numbered sequentially from bottom to top, for example, the serial numbers in the left two columns of ellipses are position numbers.
As shown in fig. 3-4, a two-tier model of order allocation and AGV delivery is established.
The optimization goal of the order distribution floor is to minimize the total distance between the picking stations and the pallets.
In the modeling, the following assumptions are made:
the inventory in the warehouse may satisfy all the order requirements in this phase.
The replenishment and recycling are not considered.
Neglecting the influence that the AGV opens and stops the characteristic and cause freight, whole process assumes that the AGV dolly is all at the uniform velocity and traveles.
In the modeling, the following basic assumptions are made:
the transfer robot AGV transfers only one tray (with various kinds of goods) at a time.
The AVG can only move in four directions of up, down, left and right at each step, and can not move obliquely.
The AGV executes tasks only by meeting the requirement of the number of commodities as soon as possible and does not need to wait for all commodities in the same order to be discharged.
For the same station, the number of the trays capable of being accommodated and placed at the same time is limited, and new ex-warehouse trays can be accommodated only after the empty parking positions exist.
After the outbound task is completed, the AGV is idle and can be scheduled to perform the next task without waiting at the docking station.
S2: and solving the order distribution problem of the order distribution layer by adopting a wolf optimization algorithm. It should be noted that:
the objective function for minimizing the order transit distance is constructed as follows:
wherein q is wp For the transport distance from the pallet w to the picking station p, x wp Is a variable from 0 to 1.
And calculating the objective function according to the constraint conditions.
The quantity of items sent to the picking station p is equal to the demand of the picking station p, and the specific formula is as follows:
wherein, y wpi Number of articles i, d, directed from tray w to picking station p pi For the demand of picking station p for item I, I ∈ I is I items in the warehouse.
The quantity of the commodities i conveyed to the picking station p by the trays w is less than or equal to the quantity of the commodities i in each tray w, and the specific formula is as follows:
wherein, y wpi Indicating the number of articles i, N, sent from the tray w to the picking station p wi And W belongs to W pallets in the warehouse.
Whether the items i at pick station p are provided by tray w is as follows:
the variable 0-1 is restricted, and the specific formula is as follows:
wherein x is the case if the tray w is delivered to the picking station p wp 1, if tray w is not delivering goods to picking station p, then x wp Is 0.
The transportation quantity is restrained: y is wpi ≥0。
Aiming at the order distribution problem, an A-x algorithm is used for obtaining a shortest distance matrix which satisfies the constraint between nodes, wherein the shortest distance matrix comprises the shortest path between each tray and the picking station and the shortest distance matrix between the tray recovery position and the picking station.
Randomly disordering order numbers, and uniformly distributing the order numbers to a sorting station as an initial population; the quantity of the trays needed by the picking stations is sorted, the orders and the picking stations are divided equally, and the order quantity of each station is kept as uniform as possible.
Since the goods on the tray are fixed, different orders are placed on different picking stations, and the traveling distances of the AGVs are different, the optimal corresponding relation between the picking stations and the orders needs to be found out in order to meet the aim of minimizing the total traveling path of the AGVs.
Because the gray wolf optimization algorithm has stronger global search capability and search precision in processing the large-scale scheduling problem, the gray wolf optimization algorithm is used for solving the optimal result of order station distribution, and the distance between the randomly distributed order and the picking station is optimized by taking the shortest path as an optimization target, so that each optimized picking station and the distributed order number are obtained.
S3: and solving the problem of AGV trolley transportation task planning of the AGV trolley transportation task planning layer by adopting a whale optimization algorithm. It should be noted that:
the lower optimization objective is to minimize AGV transport distance.
Because the upper layer only considers the mapping relations and the corresponding distances between the tray shelf and the picking stations and between the tray recovery positions, in order to enable the AGV to finish the task most quickly, each group of mapping relations between the orders and the stations obtained in the previous step are set as one task, the tasks are numbered, different tasks are distributed to the AGV to be carried, and meanwhile, the A algorithm is used for obtaining all the distance lengths from the initial point to the completion of the carrying task of the AGV.
An objective function is constructed that minimizes AGV transport distance as follows:
wherein,
for a variable constraint of 0-1, if the items for which pallet w is destined for picking station p are transported by cart k, then
1, if the goods with pallets w transported to the picking station p are transported by the trolley k, then
Is 0.
And calculating the objective function according to the constraint conditions.
The number of the AGV trolleys which can be used at any time is less than or equal to the number of the idle trolleys, and the specific formula is as follows:
wherein,
the number of AGV carts that are idle at time t.
The AGV trolley transports the tray w to a picking station p and then finishes picking and then needs to return to the goods position of the storage tray w, and the specific formula is as follows:
wherein,
and
corresponding;
the number of AGV trolleys arriving at the picking station at any time is smaller than the maximum temporary storage of the picking station, and the specific formula is as follows:
wherein, b is the maximum temporary storage of the picking station;
each pallet is transported by only one AGV car at any time, and the specific formula is as follows:
and further obtaining the mapping relation between each shelf tray and the corresponding different picking stations according to the distribution relation between the orders and the stations.
After the optimal distribution scheme of the trays and the sorting stations is obtained, aiming at the problem of AGV distribution, the mapping relation between each tray and each station is a task for an AGV to walk, and n tasks are provided on the assumption that n corresponding relations exist, namely n tasks to be executed by the AGV.
And (3) processing the data of the trays and the picking stations obtained by further obtaining the mapping relation between each shelf tray and the corresponding different picking stations through the distribution relation of the orders and the stations: all mapping relations are regarded as tasks to be executed by the AGVs, and the tasks comprise empty tray recovery, tray conveying from the storage device to the picking station and returning from the picking station to the storage area; and numbering all task orders, and randomly and uniformly distributing the task orders to a plurality of AGV trolleys to serve as an initial population so as to facilitate later algorithm optimization.
And obtaining the shortest path from the initial position to the task to be executed of each AGV trolley through an A-algorithm.
And solving the path according to the order carried by the AGV, further optimizing by using a whale optimization algorithm and taking the minimized AGV transportation distance as a target, optimizing the path cost obtained by different orders born by the AGV, and further obtaining an optimal AGV order carrying scheme.
This embodiment still provides a many AGV dispatch optimization system of unmanned on duty warehouse of electric wire netting measurement material, includes:
and the model building module is used for building a double-layer planning model comprising an order distribution layer and an AGV task scheduling planning layer.
And the algorithm solving module is used for solving the order distribution problem of the order distribution layer by adopting a wolf optimization algorithm and solving the problem of AGV car transportation task planning of the AGV car transportation task planning layer by adopting a whale optimization algorithm.
The embodiment further provides a computing device, which is suitable for a situation of a multi-AGV scheduling optimization method for an unattended warehouse for power grid measurement of materials, and the method includes:
a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, so that the power grid metering material unattended warehouse multi-AGV dispatching optimization method provided by the embodiment is realized.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and an input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
The present embodiment further provides a storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method for optimizing multiple AGV scheduling in an unattended warehouse for power grid metering supplies according to the foregoing embodiments.
The storage medium proposed by the present embodiment belongs to the same inventive concept as the data storage method proposed by the above embodiments, and technical details that are not described in detail in the present embodiment can be referred to the above embodiments, and the present embodiment has the same beneficial effects as the above embodiments.
Example 2
Referring to fig. 5 to 6, this embodiment is another embodiment of the present invention, and provides a verification test of a power grid measurement material unattended warehouse multiple AGV scheduling optimization method and system, which verifies and explains technical effects adopted in the method.
Using the actual data for a warehouse, the data is shown in the following table:
TABLE 1 Pallet and Commodity correspondence data
The convergence curve of the order distribution path obtained by using the gray wolf algorithm to minimize the order transportation distance is shown in fig. 5, and part of the data is shown in table 2:
TABLE 2 orders assigned to picking stations
The optimization is performed by using the shortest path as the optimization target, as shown in fig. 6, it can be obtained that after order distribution is performed by the algorithm, the total path is shortened from 37260 to 35880, and is shortened by 1162 meters, further proving that the invention has superiority in processing order distribution.
Through the distribution relationship between the orders and the stations, the mapping relationship between each shelf tray and the corresponding different picking stations can be further obtained, and the distribution relationship between the orders and the stations is shown in table 3.
TABLE 3 Grating map position of picking station to tray
The table is 18 × 148 matrix, meaning 148 trays correspond to 18 picking stations, data [182,155,182] in the table is that the tray 182 in the grid table meets the picking station whose position is 155, the tray returns to the original position in the storage position after the picking is completed, and the next round of picking of the goods on the tray is performed, and each data in table 4 represents a mapping relation between the shelf and the picking station, that is, the task to be carried by the AGV.
TABLE 4 picking station corresponding to position in grid diagram
An AGV task allocation fitness function convergence curve obtained after 200 iterations through a whale optimization algorithm is shown in fig. 6, the number of tasks carried by an AGV is shown in table 5, an AGV task carrying order allocation table is shown in table 6, for example, the first task of the AGV numbered 1 is 393, the specific task in the corresponding table 6 is [535,691,535], the designation 393 task order is that the AGV numbered 1 takes a tray from a starting point to a goods shelf with the designation 535 and then delivers the tray to a goods picking platform with the designation 697, and the goods picking operation is performed to further execute the task order number 782.
TABLE 5AGV carts and corresponding task counts
TABLE 6 concrete task number index
As shown in fig. 6, in the initial process, the total path length of all the tasks mapped by the picking stations and the pallet racks by the AGVs after the tasks are completed is 45675m, and the tasks carried by different AGVs are optimized by a whale optimization algorithm to perform shortest path optimization, so that the optimized total path length is 44513m, and 1162mAGV paths are reduced, and therefore, the operation path of the AGVs can be greatly optimized by optimizing the carrying orders of the AGVs by the algorithm.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A multi-AGV dispatching optimization method for an unattended warehouse for power grid measurement materials is characterized by comprising the following steps:
constructing a double-layer planning model comprising an order distribution layer and an AGV task scheduling planning layer;
the AGV task scheduling planning system comprises an AGV task scheduling planning layer, an order distribution layer and an AGV task scheduling planning layer, wherein the order distribution layer is an upper layer, and the AGV task scheduling planning layer is a lower layer;
solving the order distribution problem of the order distribution layer by adopting a wolf optimization algorithm;
and solving the problem of AGV trolley transportation task planning of the AGV trolley transportation task planning layer by adopting a whale optimization algorithm.
2. The method for optimizing the scheduling of multiple AGVs in the power grid metering material unattended warehouse according to claim 1, wherein the method comprises the following steps: the optimization goal of the upper level is to minimize the total distance between the picking station and the tray;
the optimization goal of the lower layer is to make the AGV complete the task most quickly.
3. The method for optimizing the scheduling of multiple AGVs in the power grid metering material unattended warehouse according to claim 2, wherein the method comprises the following steps: the optimization goal of the upper level is to minimize the total distance between the picking station and the pallet including,
obtaining a distance matrix of the positions of the tray shelves and the stations through an A-x algorithm, obtaining a mapping relation between each shelf and each picking station by taking the shortest path as a priority according to the distance matrix, meeting the corresponding shelf set of all picking station requirements, and obtaining the total path lengths of the shelves and the corresponding stations, namely the path lengths of different orders on different picking stations;
the route lengths of different orders on different picking stations are optimized through a wolf optimization algorithm, each group of mapping relation between the orders and the stations is obtained, and efficient order station distribution is achieved.
4. The method for optimizing the scheduling of multiple AGVs in the power grid metering material unattended warehouse according to claim 2, wherein the method comprises the following steps: the lower level optimization objective is to maximize the AGV's task performance,
setting each group of mapping relation between the order obtained by the upper layer and the station as a task, and numbering the tasks;
distributing different tasks to the AGV to carry, and simultaneously obtaining all the distance lengths from an initial point to the completion of the carrying tasks of the AGV by utilizing an A-star algorithm;
and (3) optimizing the distribution of the carrying tasks of the AGV cars by combining a whale optimization algorithm by taking all the distance lengths from the initial point to the completion of the carrying tasks of all the AGV cars as optimization conditions to obtain the optimal carrying tasks and corresponding paths of the AGV cars.
5. The method for optimizing the dispatching of multiple AGVs in the power grid metering material unattended warehouse as claimed in claim 1 or 3, wherein: solving the order allocation problem of the order allocation layer using the grayish optimization algorithm includes,
the objective function for minimizing the order transit distance is constructed as follows:
wherein q is wp To be driven from a trayw transport distance to picking station p, x wp Is a variable from 0 to 1;
calculating an objective function for minimizing the transportation distance of the order according to the constraint condition;
wherein all constraints need to be satisfied.
6. The method for optimizing the scheduling of multiple AGVs in the power grid metering material unattended warehouse according to claim 5, wherein the method comprises the following steps: the constraint conditions include the number of the first and second constraints,
the quantity of the commodities sent to the picking station p is equal to the demand quantity of the picking station p;
the quantity of the commodities i conveyed to the picking station p by the trays w is less than or equal to that of the commodities i in each tray w;
judging whether the commodity i of the picking station p is constrained by a 0-1 variable obtained by calculation provided by the tray w;
the number of transports is constrained.
7. The method for optimizing the scheduling of multiple AGVs in the power grid metering material unattended warehouse according to claim 1 or 4, wherein the method comprises the following steps: the problem of solving the AGV car transportation task planning of the AGV car transportation task planning layer by adopting a whale optimization algorithm comprises the following steps of,
an objective function is constructed that minimizes AGV transport distance as follows:
wherein,
is a 0-1 variable constraint;
if the goods transported by the pallet w to the picking station p are transported by the trolley k, then
Is 1;
if the goods transported by the pallet w to the picking station p are transported by the trolley k, then
Is 0;
an objective function for minimizing the AGV transport distance is calculated according to all the following constraints:
the number of the AGV trolleys which can be used at any time is less than or equal to the number of the idle trolleys, and the specific formula is as follows:
wherein,
the number of AGV trolleys which are idle at the time t;
the AGV trolley transports the tray w to a picking station p and then finishes picking and then needs to return to the goods position of the storage tray w, and the specific formula is as follows:
wherein,
and
corresponding;
the number of AGV trolleys arriving at the picking station at any time is smaller than the maximum temporary storage of the picking station, and the specific formula is as follows:
wherein, b is the maximum temporary storage of the picking station;
each pallet is transported by only one AGV car at any time, and the specific formula is as follows:
8. a multi-AGV dispatching optimization system for an unattended warehouse for power grid material metering comprises,
the model building module is used for building a double-layer planning model comprising an order distribution layer and an AGV task scheduling planning layer;
and the algorithm solving module is used for solving the order distribution problem of the order distribution layer by adopting a wolf optimization algorithm and solving the problem of AGV car transportation task planning of the AGV car transportation task planning layer by adopting a whale optimization algorithm.
9. A computing device, comprising:
a memory and a processor;
the memory is used for storing computer-executable instructions, and the processor is used for executing the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the power grid metering material unattended warehouse multi-AGV scheduling optimization method in any one of claims 1 to 7 are achieved.
10. A computer-readable storage medium storing computer-executable instructions, which when executed by a processor, implement the steps of the power grid metering material unattended warehouse multiple AGV scheduling optimization method according to any one of claims 1 to 7.
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| CN116029536B (en) * | 2023-03-28 | 2023-06-27 | 深圳市实佳电子有限公司 | NFC technology-based intelligent warehouse cargo scheduling method, NFC technology-based intelligent warehouse cargo scheduling device, NFC technology-based intelligent warehouse cargo scheduling equipment and NFC technology-based intelligent warehouse cargo scheduling medium |
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