CN116873431B - Multi-heavy-load AGV storage and transportation method based on rock plate intelligent warehouse - Google Patents
Multi-heavy-load AGV storage and transportation method based on rock plate intelligent warehouse Download PDFInfo
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Abstract
The invention discloses a multi-heavy-load AGV storage and transportation method based on a rock plate intelligent warehouse, which comprises the following steps: constructing a cargo space allocation algorithm; constructing a heavy load AGV scheduling rule algorithm; generating a plurality of combined optimization solving algorithms through a composite algorithm framework according to a cargo space distribution algorithm and a heavy load AGV scheduling rule algorithm; constructing a simulation platform; determining a plurality of simulation calculation examples, and performing simulation solution on each simulation calculation example by adopting a plurality of combined optimization solution algorithms in a simulation platform to obtain a plurality of simulation results; analyzing the different performance evaluation indexes of each simulation result to determine a combined optimization solving algorithm which performs best under the different performance evaluation indexes of each simulation result; and correspondingly generating a storage and transportation strategy of the multi-load AGV. The invention solves the problems that the existing multi-heavy-load AGV intelligent warehouse is unfavorable for the management of the rock plate intelligent warehouse because goods come in and go out of the warehouse due to unreasonable coordination of goods space distribution and AGV dispatching.
Description
Technical Field
The invention relates to the technical field of multi-heavy-load AGV storage and transportation, in particular to a multi-heavy-load AGV storage and transportation method based on a rock plate intelligent warehouse.
Background
Along with the construction work of strengthening the storage link from automation to intelligent conversion in quick steps in China, the storage industry and logistics industry conversion bring higher demands for the intellectualization. Along with the pushing of intelligent development, the transportation equipment in the warehouse operation moves towards the intellectualization, replaces fork truck through introducing automatic guided vehicle (Automated Guided Vehicle, abbreviated as AGV), has realized unmanned operation.
The problem of storage and transportation of the multi-load AGVs in the intelligent rock plate warehouse can be described as follows: a plurality of AGVs and a plurality of transportation tasks are arranged in one rock plate intelligent warehouse, the transportation tasks are divided into a warehouse-in transportation task and a warehouse-out transportation task, and the warehouse-in transportation tasks come from a workshop production line and arrive randomly according to uniform distribution. The delivery tasks of the warehouse-out come from orders placed by clients, and the arrival time of the delivery tasks has certain randomness. When the warehouse entry transport task arrives at the docking station, the rock board storage strategy arranges the appropriate cargo space for the arriving rock board and needs to meet the storage constraint of the goods and the standard cargo space. After the goods position of the warehousing transport task is determined, the AGV scheduling strategy schedules the idle AGVs, the scheduled AGVs go to the docking station, and the warehousing task is stored and warehoused, so that the warehousing transport task is completed. When the delivery time is reached, the AGV scheduling strategy schedules the idle trolley along with the delivery of the customer order, and the scheduled AGV delivers the rock plate from the warehouse goods space according to the customer order, so that the delivery transport task is completed.
The existing multi-load AGV intelligent warehouse has space-time imbalance between the warehouse-in task and the warehouse-out task, namely, the warehouse-in task is carried out in the daytime, and the warehouse-out of orders is carried out at night, so that the warehouse-in time is long, and the warehouse-out time is short. Due to unreasonable matching of goods space distribution and AGV dispatching, goods are often not easy to get in and out of the warehouse, and management of the intelligent rock plate warehouse is not facilitated.
Disclosure of Invention
Aiming at the defects, the invention provides a multi-heavy-load AGV storage and transportation method based on a rock plate intelligent warehouse, which aims to solve the problems that the warehouse-in task and the warehouse-out task of the existing multi-heavy-load AGV intelligent warehouse have time-space imbalance, and the condition that goods come in and out of the warehouse are not enough due to unreasonable matching of goods space distribution and AGV scheduling, so that the management of the rock plate intelligent warehouse is not facilitated.
To achieve the purpose, the invention adopts the following technical scheme:
a multi-heavy-load AGV storage and transportation method based on a rock plate intelligent warehouse comprises the following steps:
step S1: constructing a cargo space distribution algorithm, wherein the cargo space distribution algorithm comprises a cargo space distribution algorithm associated with the cargo delivery frequency of goods, a cargo space distribution algorithm based on the cargo value, a random cargo space distribution algorithm, a cargo space distribution algorithm closest to a connection point and a middle storage area cargo space distribution algorithm;
step S2: constructing a heavy-load AGV scheduling rule algorithm, wherein the heavy-load AGV scheduling rule algorithm comprises a first-come-first-serve rule algorithm, a nearest rule algorithm, a farthest rule algorithm, a highest idle rule algorithm, a shortest running distance rule algorithm and a random rule algorithm;
step S3: generating a plurality of combined optimization solving algorithms through a composite algorithm framework according to the goods space distribution algorithm and the heavy-load AGV scheduling rule algorithm;
step S4: constructing a simulation platform;
step S5: determining a plurality of simulation calculation examples, and performing simulation solution on each simulation calculation example by adopting a plurality of combined optimization solution algorithms in the simulation platform to obtain a plurality of simulation results;
step S6: analyzing different performance evaluation indexes of each simulation result to determine a combined optimization solving algorithm with the best performance under the different performance evaluation indexes of each simulation result;
step S7: and correspondingly generating a storage and transportation strategy of the multi-load AGV based on a combined optimization solving algorithm with the optimal performance under different performance evaluation indexes of each simulation result.
Preferably, in step S1, before constructing the cargo space allocation algorithm, the following steps are further included:
step S11: determining a cargo coding mode;
step S12: coding cargo positions in the intelligent warehouse by using the cargo coding mode;
step S13: the cargo state after the encoding is completed is divided.
Preferably, in step S1, in the cargo space allocation algorithm for associating the cargo delivery frequency, the cargo delivery frequency calculation formula is as follows:
wherein, GI i Indicating the frequency of delivery of class I dimensional specification goods, i=1, 2, … I; GN (GN) i Representing the ex-warehouse quantity of the goods of class i in the past;
in the goods position distribution algorithm based on the goods value, the value calculation formula of the goods is as follows:
therein, GVI i A value coefficient representing a class I size item, i=1, 2, … I; g i Representing the specification of the i-class goods, and representing the specification by the standard goods digits occupied by the goods; GN (GN) i Indicating the amount of the i-class goods to be delivered to the warehouse.
Preferably, before determining the plurality of simulation cases in step S5, the method further includes the steps of:
and determining the influence factors of the simulation calculation examples, wherein the influence factors of the simulation calculation examples comprise AGV speed in an empty state, AGV speed in a loading state, docking station capacity, order arrival time interval and task type proportion.
Preferably, in step S6, the determination of the combined optimization solution algorithm that performs best comprises the steps of:
the relative deviation index (Relative Deviation Index, RDI) is used to compare the target values of each combined optimization solution algorithm under each simulation example, and the specific calculation formula is as follows:
wherein RDI IM Representing the relative deviation value of the combined optimization solving algorithm M under the simulation example I; i represents a simulation example; FO (FO) IM Representing a simulation result of the combined optimization solving algorithm M under the simulation example I; best I And Worst I Respectively representing the optimal result and the worst result of all the combined optimization solving algorithms under the simulation example I.
Preferably, in step S6, each simulation result is analyzed for a performance evaluation index, where the performance evaluation index includes a maximum finishing time of the transport task, a total transport distance of the AGV for performing the transport task, a warehouse entry transport distance of the AGV for performing the transport task, a warehouse exit transport distance of the AGV for performing the transport task, a cargo space utilization rate, and an AGV balance.
The technical scheme provided by the embodiment of the application can comprise the following beneficial effects:
in the scheme, a cargo space distribution algorithm and a heavy-load AGV scheduling rule algorithm are combined into a plurality of combined optimization solving algorithms through a composite algorithm framework, and actual system state change and production environment are simulated through a simulation technology to schedule. And analyzing the multiple simulation results obtained in the simulation process to obtain a combined optimization solving algorithm with the best performance under the different performance evaluation indexes of each simulation result. The storage and transportation strategy of the multi-load AGVs generated by the optimal combination optimization solving algorithm is applied to the management of the actual intelligent rock plate warehouse, so that the entering and exiting of the rock plates can be completed in time, the management of the intelligent rock plate warehouse is more efficient, and the market competitiveness of a rock plate enterprise is further improved.
Drawings
FIG. 1 is a flow chart of steps of a multi-heavy-load AGV storage and transportation method based on a rock plate intelligent warehouse.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
A multi-heavy-load AGV storage and transportation method based on a rock plate intelligent warehouse comprises the following steps:
step S1: constructing a cargo space distribution algorithm, wherein the cargo space distribution algorithm comprises a cargo space distribution algorithm associated with the cargo delivery frequency of goods, a cargo space distribution algorithm based on the cargo value, a random cargo space distribution algorithm, a cargo space distribution algorithm closest to a connection point and a middle storage area cargo space distribution algorithm;
step S2: constructing a heavy-load AGV scheduling rule algorithm, wherein the heavy-load AGV scheduling rule algorithm comprises a first-come-first-serve rule algorithm, a nearest rule algorithm, a farthest rule algorithm, a highest idle rule algorithm, a shortest running distance rule algorithm and a random rule algorithm;
step S3: generating a plurality of combined optimization solving algorithms through a composite algorithm framework according to the goods space distribution algorithm and the heavy-load AGV scheduling rule algorithm;
step S4: constructing a simulation platform;
step S5: determining a plurality of simulation calculation examples, and performing simulation solution on each simulation calculation example by adopting a plurality of combined optimization solution algorithms in the simulation platform to obtain a plurality of simulation results;
step S6: analyzing different performance evaluation indexes of each simulation result to determine a combined optimization solving algorithm with the best performance under the different performance evaluation indexes of each simulation result;
step S7: and correspondingly generating a storage and transportation strategy of the multi-load AGV based on a combined optimization solving algorithm with the optimal performance under different performance evaluation indexes of each simulation result.
According to the multi-heavy-load AGV storage and transportation method based on the intelligent rock plate warehouse, as shown in fig. 1, a cargo space distribution algorithm is constructed, wherein the cargo space distribution algorithm comprises a cargo space distribution algorithm related to the cargo delivery frequency of goods, a cargo space distribution algorithm based on the cargo value, a random cargo space distribution algorithm, a cargo space distribution algorithm closest to a connection point and a cargo space distribution algorithm of an intermediate storage area. In the embodiment, the storage and transportation problem of the multi-load AGVs in the rock plate intelligent warehouse is decomposed into two sub-problems, namely a cargo space distribution sub-problem and an AGV scheduling sub-problem. Aiming at the problem of goods space distribution, 5 goods space distribution algorithms are constructed by dividing the goods space states and respectively considering the goods ex-warehouse frequency, the goods value, the goods arrival time, the warehousing transportation distance and the ex-warehouse transportation distance. When the goods arrive at the connection station, the goods are distributed by using a goods location distribution algorithm, the state of all relevant goods locations currently needs to be known during distribution, the goods location state represents the relation between the goods locations and the goods objects, and the goods location state is divided into idle, occupied, picking, reservation and damage. Further, the cargo space allocation algorithm (Location Allocation Algorithm for Associated Goods Outgoing Frequency, abbreviated as LAAAGOF) associated with the cargo space allocation frequency refers to allocating the cargo space with a higher cargo space frequency to a cargo space with a closer cargo space distance from the unloading point and allocating the cargo space with a lower cargo space frequency to a cargo space with a farther cargo space distance from the unloading point according to the cargo space allocation frequency of the cargo space. The goods location allocation algorithm (Location Allocation Algorithm Based on Commodity Value, abbreviated as LAABCV) based on the goods value refers to that, when goods location allocation is performed on goods, the goods with high goods value and high ex-warehouse frequency are preferentially allocated to the goods location closer to the unloading point in consideration of the goods value. A random cargo allocation algorithm (Random Location Allocation Algorithm, RLAA for short) refers to randomly selecting cargo in an empty warehouse area when cargo allocation of items is performed. The goods position distribution algorithm (Algorithm for Assigning the Closest Storage Space to the Receiving Point, abbreviated as AACSSRP) closest to the connection point refers to considering the delivery frequency of various goods when the goods arrive at the connection station, and according to the delivery frequency of the goods, the goods with high delivery frequency are preferentially distributed in the goods positions of the storage areas close to the connection station, and the goods with low delivery frequency are preferentially distributed in the goods positions of the storage areas far from the connection station. The intermediate warehouse area cargo space allocation algorithm (Algorithm for the Allocation of Storage Spaces in the Intermediate Storage Area, abbreviated as aassesa) refers to considering the delivery frequency of various types of cargoes when the cargoes arrive at the docking station, and preferentially allocating the cargoes with high delivery rate in the intermediate warehouse area cargo space according to the delivery frequency of the cargoes, and preferentially allocating the cargoes with low delivery frequency in the warehouse area cargo space far away from the docking station.
The second step is to construct a heavy-duty AGV scheduling rule algorithm, wherein the heavy-duty AGV scheduling rule algorithm comprises a first-come-first-serve rule algorithm, a nearest rule algorithm, a farthest rule algorithm, a highest idle rule algorithm, a shortest running path rule algorithm and a random rule algorithm. In this embodiment, to the sub-problem of AGV dispatch, having considered AGV priority, AGV and the distance of point of connection, AGV and the distance of point of discharge, AGV idle rate, AGV transport distance and random selection respectively, 6 kinds of AGV dispatch rule algorithms have been constructed. In the daytime production process, when the goods processing of a workshop is completed and reaches a connection station, after the goods space distribution of the goods is completed through a goods space distribution algorithm, the warehouse system calls an AGV scheduling rule algorithm to carry out tasks on idle AGVsAssigning, namely carrying the goods of the docking station to the assigned goods space by the scheduled AGV; in the process of delivering orders at night, a customer order arrives, the warehouse system calls an AGV scheduling rule algorithm to assign tasks to idle AGVs, and the scheduled AGVs carry goods from a target goods space to a unloading point according to order information and goods storage information in a warehouse area and then return to a designated stay point. Further described, the first come first served rule algorithm (FCFS) specifically includes the following steps: initializing an idle trolley set Q, and adding all idle trolleys into the idle trolley set Q according to a numbering sequence; step two, if the transportation task arrivesThen the first element (AGV) in the set is selected and removed from the set of empty carts Q; otherwise, waiting for a new idle trolley to be added into the idle trolley set Q, selecting a first element in the set, and removing the first element from the idle trolley set Q; and thirdly, returning to a designated stay point after the called trolley completes the transportation task, setting the trolley state to be an idle state, and adding the trolley state to the rear of the |Q| element in the idle trolley set Q. The latest rule algorithm (in short, search) specifically includes the following steps: initializing an idle trolley set Q, and adding all idle trolleys into the idle trolley set Q according to a numbering sequence; step two, the transport task arrives, if +.>Calculating the distance between each trolley in the idle trolley set Q and the transport task to obtain a trolley distance set D, wherein the element numbers are represented by 1,2 and … c; step three, solving the trolley closest to the transportation task: d, d c ＝Min{d 1 ,d 2 ,…d c }, d is c Removing the trolley c from the distance set D of the trolley, and removing the trolley c from the idle trolley set Q; and fourthly, returning to a designated stay point after the called trolley completes the transportation task, setting the trolley state to be an idle state, adding the trolley state to the idle trolley set Q, and updating the distance set Q of the trolley. Furthest rule algorithm(Farthest for short) specifically comprises the following steps: initializing an idle trolley set Q, and adding all idle trolleys into the idle trolley set Q according to a numbering sequence; step two, the transport task arrives, if +.>Calculating the distance between each trolley in the idle trolley set Q and the transport task to obtain a trolley distance set Q, wherein the element numbers are represented by 1,2 and … c; step three, solving a trolley with the farthest distance from the transportation task: d, d c ＝Max{d 1 ,d 2 ,…d c }, d is c Removing the trolley c from the distance set D of the trolley, and removing the trolley c from the idle trolley set Q; and fourthly, returning to a designated stay point after the called trolley completes the transportation task, setting the trolley state to be an idle state, adding the trolley state to the idle trolley set Q, and updating the distance set D of the trolley. The highest idle rule algorithm (HighestIdle) specifically includes the following steps:
initializing an idle trolley set Q, and adding all idle trolleys into the idle trolley set Q according to a numbering sequence; step two, if the transportation task arrivesCalculating the idle rate of each trolley in the idle trolley set Q, namely the sum of the trolley transportation time/the trolley transportation time and the trolley waiting time, to obtain an idle rate set F of the trolley, wherein the element numbers are represented by 1,2 and … c; step three, solving a trolley with the farthest distance from the transportation task: f (f) c ＝Max{f 1 ,f 2 ,…f c And f is }, f c Removing the trolley c from the idle rate set F of the trolley, and removing the trolley c from the idle trolley set Q; and fourthly, returning to a designated stay point after the called trolley completes the transportation task, setting the trolley state to be an idle state, adding the trolley state into the idle trolley set Q, and updating the idle rate set F of the trolley. The shortest path running rule algorithm (shorttest distance) specifically comprises the following steps:
step one is initializing an idle trolley set QA trolley travel distance set S, the element numbers being indicated by 1,2, … c; step two, if the transportation task arrivesCalculating the running distance s of each trolley in the idle trolley set Q c The method comprises the steps of carrying out a first treatment on the surface of the Step three, the trolley with the shortest total travel distance is obtained: s is(s) c ＝Min{s 1 ,s 2 ,…s c Calling the trolley c and removing the trolley c from the idle trolley set Q; and fourthly, returning to a designated stop point after the called trolley completes the transportation task, setting the trolley state to be an idle state, adding the trolley state to the idle trolley set Q, and updating the trolley travel distance set S. The Random rule algorithm (Random for short) specifically comprises the following steps: step one, initializing an idle trolley set Q, wherein the element numbers are represented by 1,2 and … c; step two, the transport task arrives, if +.>Randomly selecting a trolley: c=rand ({ 1,2, … c }), rand being a random method for randomly selecting elements in a specified set, calling trolley c, and removing trolley c from the set of idle trolleys Q; and thirdly, returning to a designated stay point after the called trolley completes the transportation task, setting the trolley state to be an idle state, and adding the trolley state into the idle trolley set Q.
And thirdly, generating a plurality of combined optimization solving algorithms through a composite algorithm framework according to the cargo space distribution algorithm and the heavy-load AGV scheduling rule algorithm. In the embodiment, 5 goods space allocation algorithms and 6 AGV scheduling rule algorithms form 30 combined optimization solving algorithms through a composite algorithm framework, and the 30 combined optimization solving algorithms are beneficial to simulation solving of simulation examples by using the combined optimization solving algorithms subsequently.
The fourth step is to build a simulation platform. In this embodiment, a Plant formulation Simulation platform is built according to the actual workshop layout, and a cargo space allocation algorithm and an AGV scheduling rule algorithm are embedded.
And fifthly, determining a plurality of simulation examples, and performing simulation solution on each simulation example by adopting a plurality of combined optimization solution algorithms in the simulation platform to obtain a plurality of simulation results. In this embodiment, in combination with the production scenario of the rock plate enterprise, simulation calculation examples of the influence factors, namely, the speed of the AGV in the empty state, the speed of the AGV in the loading state, the capacity of the docking station, the arrival time interval of the order and the task type proportion are determined, and for each simulation calculation example, 30 combination optimization solution algorithms are adopted to perform simulation solution to obtain a simulation result corresponding to each simulation calculation example, so that the effectiveness and the suitability of the algorithm design scheme can be checked and analyzed.
And step six, analyzing different performance evaluation indexes of each simulation result to determine a combined optimization solving algorithm with the best performance under the different performance evaluation indexes of each simulation result. In this embodiment, the performance evaluation index includes a maximum completion time of the transport task, a total transport distance of the AGV for performing the transport task, a warehouse entry transport distance of the AGV for performing the transport task, a warehouse exit transport distance of the AGV for performing the transport task, a cargo space utilization rate, and an AGV balance. In order to determine the optimal algorithm in the proposed combined optimization solution algorithm, the target values of each combined optimization solution algorithm under each simulation example are compared by using relative deviation indexes (Relative Deviation Index, RDI), so that the influence of different influence factors on the target value different performance evaluation indexes is analyzed.
And seventh, based on the combined optimization solving algorithm with the best performance under the different performance evaluation indexes of the simulation results, correspondingly generating the storage and transportation strategy of the multi-load AGV. Specifically, the optimal combination optimization solving algorithm is performed, so that the cargo space distribution algorithm and the AGV scheduling rule algorithm are reasonably matched. The storage and transportation strategy of the multi-load AGVs generated by the optimal combination optimization solving algorithm is applied to the management of the actual intelligent rock plate warehouse, so that the entering and exiting of the rock plates can be completed in time, the management of the intelligent rock plate warehouse is more efficient, and the market competitiveness of a rock plate enterprise is further improved.
Preferably, in step S1, before constructing the cargo space allocation algorithm, the method further includes the steps of:
step S11: determining a cargo coding mode;
step S12: coding cargo positions in the intelligent warehouse by using the cargo coding mode;
step S13: the cargo state after the encoding is completed is divided.
In this embodiment, firstly, a cargo space coding mode is determined, then, a unique code is carried out on cargo spaces in an intelligent warehouse by using a unified cargo coding mode, so as to realize dynamic binding of cargo and cargo spaces, then, cargo space states are reasonably divided according to the characteristics of entering and exiting of a rock board, and finally, a cargo space distribution algorithm is constructed. Further, the storage area cargo space codes and the cargo space states are divided to facilitate management and use of cargo space resources of the storage area. The cargo space code generally adopts letters, numbers or symbols to represent information such as the position, the size and the like of the cargo space so as to quickly and accurately find the cargo space. The goods space state classifies the use condition of the goods space, such as idle, occupied, picking, reservation and damage, so as to grasp the use condition of the goods space. The allocation of cargo space is closely related to cargo space coding and cargo space status partitioning. In the allocation of cargo space, the available cargo space is preferably selected for allocation in consideration of cargo space states. Meanwhile, the occupied or reserved goods space needs to be tracked, and occupied goods space resources are released in time so as to store the next batch of goods.
Preferably, in step S1, in the cargo space allocation algorithm for associating the cargo delivery frequency, the cargo delivery frequency calculation formula is as follows:
wherein, GI i Indicating the frequency of delivery of class I dimensional specification goods, i=1, 2, … I; GN (GN) i Representing the ex-warehouse quantity of the goods of class i in the past;
in the goods position distribution algorithm based on the goods value, the value calculation formula of the goods is as follows:
therein, GVI i A value coefficient representing a class I size item, i=1, 2, … I; g i Representing the specification of the i-class goods, and representing the specification by the standard goods digits occupied by the goods; GN (GN) i Indicating the amount of the i-class goods to be delivered to the warehouse.
In this embodiment, in the cargo space allocation algorithm associated with the cargo shipment frequency, the cargo shipment frequency is derived from past historical order data. When the arrived goods are not in the goods delivery frequency data, the frequency is defaulted to 0 (new product), and after the delivery of a batch of orders is completed, the delivery frequency of the goods is updated. The larger the value of the ex-warehouse frequency of the article, the closer the article is allocated to the article position to the discharge point. In a cargo space allocation algorithm based on the value of an item, the larger the value coefficient value of the item, the closer the item is allocated to the cargo space to the discharge point.
Preferably, before determining the plurality of simulation cases in step S5, the method further includes the steps of:
and determining the influence factors of the simulation calculation examples, wherein the influence factors of the simulation calculation examples comprise AGV speed in an empty state, AGV speed in a loading state, docking station capacity, order arrival time interval and task type proportion.
In this embodiment, determining the influence factor of the simulation example is beneficial to the subsequent analysis of the influence factor on the target value different performance evaluation indexes. Further, the AGV speed in the idle state is 1.0m/s and 1.2 m/s; AGV speed in loading state is 0.6m/s and 0.8 m/s; the capacity of the docking station is 1 or 2; the order arrival time interval has three levels, namely poisson distribution which obeys the average value of 5min, 10min and 15 min; the task types are divided into warehousing tasks and ex-warehouse tasks, the task type proportion refers to the ratio of warehousing quantity to ex-warehouse quantity, and the ratio is low, medium and high, and the corresponding values are 1/7, 3/7 and 5/7.
Preferably, in step S6, the determination of the combined optimization solution algorithm that performs best includes the steps of:
the relative deviation index (Relative Deviation Index, RDI) is used to compare the target values of each combined optimization solution algorithm under each simulation example, and the specific calculation formula is as follows:
wherein FO IM Representing the relative deviation value of the combined optimization solving algorithm M under the simulation example I; i represents a simulation example; FO (FO) IM Representing a simulation result of the combined optimization solving algorithm M under the simulation example I; best I And Worst I Respectively representing the optimal result and the worst result of all the combined optimization solving algorithms under the simulation example I.
In this embodiment, the comparison of the relative deviation indexes to the target values of each combined optimization solution algorithm under each simulation example is beneficial to determining the optimal algorithm in all combined optimization solution algorithms under each simulation example, so as to further realize that the storage and transportation strategy of the multi-load AGVs generated by the optimal algorithm is applied to the management of the actual intelligent rock plate warehouse.
Preferably, in step S6, analysis of different performance evaluation indexes is performed on each simulation result, where the performance evaluation indexes include a maximum finishing time of the transport task, a total transport distance of the AGV for executing the transport task, a warehouse entry transport distance of the AGV for executing the transport task, a warehouse exit transport distance of the AGV for executing the transport task, a cargo space utilization rate, and an AGV balance.
In this embodiment, when the simulation results are analyzed by using the maximum completion time of the transportation task as the performance evaluation index, the best algorithm in the 30 combination optimization solving algorithms is an RLAA-search combination algorithm. When the simulation results are analyzed by taking the total transport distance of the AGV in executing the transport task as a performance evaluation index, the cargo space distribution algorithm LAABCV is better in overall performance, and can be combined with the AGV scheduling rule algorithm Nearest, random to obtain a smaller total transport distance. When the simulation results are analyzed by taking the warehousing transportation distance of the AGV in executing the transportation task as a performance evaluation index, the best algorithm in the 30 combination optimization solving algorithms is an AASSRP_Farthest combination algorithm. When the simulation results are analyzed by taking the ex-warehouse transport distance of the AGV in executing the transport task as a performance evaluation index, the optimal algorithm in the 30 combination optimization solving algorithms is the LAAAGOF_FCFS combination algorithm. When the simulation results are analyzed by taking the cargo space utilization rate as the performance evaluation index, the cargo space utilization rate of the storage areas under different AGV dispatching rule algorithms is not changed greatly, so that only the algorithm which is best represented in different cargo space allocation algorithms is considered, and the cargo space allocation algorithm AASSRP is adopted in the embodiment, so that the average cargo space utilization rate of each storage area of the rock plate warehouse can reach more than 70%. When each simulation result is analyzed by taking AGV balance as a performance evaluation index, the optimal algorithm in the 30 combination optimization solving algorithms is an AACS RP-Shorttestdistance combination algorithm.
Furthermore, functional units in various embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations of the above embodiments may be made by those skilled in the art within the scope of the invention.
Claims (6)
1. A multi-heavy-load AGV storage and transportation method based on a rock plate intelligent warehouse is characterized in that: the method comprises the following steps:
step S1: constructing a cargo space distribution algorithm, wherein the cargo space distribution algorithm comprises a cargo space distribution algorithm associated with the cargo delivery frequency of goods, a cargo space distribution algorithm based on the cargo value, a random cargo space distribution algorithm, a cargo space distribution algorithm closest to a connection point and a middle storage area cargo space distribution algorithm;
step S2: constructing a heavy-load AGV scheduling rule algorithm, wherein the heavy-load AGV scheduling rule algorithm comprises a first-come-first-serve rule algorithm, a nearest rule algorithm, a farthest rule algorithm, a highest idle rule algorithm, a shortest running distance rule algorithm and a random rule algorithm;
step S3: generating 30 combination optimization solving algorithms through a composite algorithm framework according to the goods space distribution algorithm and the heavy-load AGV scheduling rule algorithm;
step S4: constructing a simulation platform;
step S5: determining a plurality of simulation calculation examples, and performing simulation solution on each simulation calculation example by adopting a plurality of combined optimization solution algorithms in the simulation platform to obtain a plurality of simulation results;
step S6: analyzing different performance evaluation indexes of each simulation result to determine a combined optimization solving algorithm with the best performance under the different performance evaluation indexes of each simulation result;
step S7: and correspondingly generating a storage and transportation strategy of the multi-load AGV based on a combined optimization solving algorithm with the optimal performance under different performance evaluation indexes of each simulation result.
2. The multi-heavy-load AGV storage and transportation method based on the intelligent rock plate warehouse of claim 1 is characterized in that: in step S1, before constructing the cargo space allocation algorithm, the method further includes the steps of:
step S11: determining a cargo coding mode;
step S12: coding cargo positions in the intelligent warehouse by using the cargo coding mode;
step S13: the cargo state after the encoding is completed is divided.
3. The multi-heavy-load AGV storage and transportation method based on the intelligent rock plate warehouse of claim 1 is characterized in that: in step S1, in the cargo space allocation algorithm related to the cargo delivery frequency, the cargo delivery frequency calculation formula is as follows:
wherein, GI i Indicating the frequency of delivery of class I dimensional specification goods, i=1, 2, … I; GN (GN) i Representing the ex-warehouse quantity of the goods of class i in the past;
in the goods position distribution algorithm based on the goods value, the value calculation formula of the goods is as follows:
therein, GVI i A value coefficient representing a class I size item, i=1, 2, … I; g i Representing the specification of the i-class goods, and representing the specification by the standard goods digits occupied by the goods; GN (GN) i Indicating the amount of the i-class goods to be delivered to the warehouse.
4. The multi-heavy-load AGV storage and transportation method based on the intelligent rock plate warehouse of claim 1 is characterized in that: in step S5, before determining the plurality of simulation examples, the method further includes the steps of:
and determining the influence factors of the simulation calculation examples, wherein the influence factors of the simulation calculation examples comprise AGV speed in an empty state, AGV speed in a loading state, docking station capacity, order arrival time interval and task type proportion.
5. The multi-heavy-load AGV storage and transportation method based on the intelligent rock plate warehouse of claim 1 is characterized in that: in step S6, the determination of the best performing combinatorial optimization solution algorithm comprises the steps of:
the relative deviation index (Relative Deviation Index, RDI) is used to compare the target values of each combined optimization solution algorithm under each simulation example, and the specific calculation formula is as follows:
wherein RDI IM Representing the relative deviation value of the combined optimization solving algorithm M under the simulation example I; i represents a simulation example; FO (FO) IM Representing a simulation result of the combined optimization solving algorithm M under the simulation example I; best I And Worst I Respectively representing the optimal result and the worst result of all the combined optimization solving algorithms under the simulation example I.
6. The multi-heavy-load AGV storage and transportation method based on the intelligent rock plate warehouse of claim 1 is characterized in that: in step S6, analysis of different performance evaluation indexes is performed on each simulation result, where the performance evaluation indexes include a maximum finishing time of the transport task, a total transport distance of the AGV for executing the transport task, a warehouse-in transport distance of the AGV for executing the transport task, a warehouse-out transport distance of the AGV for executing the transport task, a cargo space utilization rate, and an AGV balance.
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