CN113636254A - A kind of intelligent storage system storage tank self-adaptive optimization adjustment method and system - Google Patents
A kind of intelligent storage system storage tank self-adaptive optimization adjustment method and system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
- B65G1/137—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed
- B65G1/1373—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed for fulfilling orders in warehouses
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G1/00—Storing articles, individually or in orderly arrangement, in warehouses or magazines
- B65G1/02—Storage devices
- B65G1/04—Storage devices mechanical
- B65G1/137—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed
- B65G1/1371—Storage devices mechanical with arrangements or automatic control means for selecting which articles are to be removed with data records
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
- B65G2203/02—Control or detection
- B65G2203/0208—Control or detection relating to the transported articles
- B65G2203/0233—Position of the article
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
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Abstract
本发明公开了一种智能仓储系统储物槽自适应优化调整方法与系统，首先构建一种能够自适应调整空间的智能仓储系统，主要包含仓储货架结构、信息采集单元和储物槽自适应优化控制系统；然后，预测出不同原材料未来的储存空间需求信息，获取所有原材料需要占用的储物槽空间尺寸；进而读取仓储货架的整体信息并设置约束条件，构建储物槽自适应优化调整的数学模型；基于数学模型，采用遗传算法对储物槽优化问题进行求解，得到最优的储物槽配置结构方案；最后，智能仓储系统的控制系统根据所得到的最优的储物槽配置结构方案，对仓储货架结构进行调节，实现对各储物槽进行自适应调整。
The invention discloses a method and system for self-adaptive optimization and adjustment of storage slots in an intelligent storage system. First, an intelligent storage system capable of self-adaptively adjusting space is constructed, which mainly includes storage rack structures, information collection units and self-adaptive optimization of storage slots. The control system; then, predicts the future storage space demand information of different raw materials, and obtains the storage slot space size that all raw materials need to occupy; then reads the overall information of the storage shelves and sets the constraints, and builds the storage slot adaptive optimization adjustment. Mathematical model; based on the mathematical model, the genetic algorithm is used to solve the storage tank optimization problem, and the optimal storage tank configuration structure scheme is obtained; finally, the control system of the intelligent storage system is based on the obtained optimal storage tank configuration structure. The solution is to adjust the structure of the storage rack and realize the self-adaptive adjustment of each storage slot.
Description
Technical Field
The invention relates to the technical field of warehouse logistics, in particular to a method and a system for adaptively optimizing and adjusting a storage tank of an intelligent warehousing system.
Background
To meet the increasingly intense market competition, manufacturing companies often produce a wide variety of products, often requiring the use of a wide variety of raw materials. In order to ensure stable and reliable operation of production, a certain raw material stock needs to be reserved in a production workshop. The reasonable utilization of the inventory turnover efficiency and the inventory space of the enterprise plays an important role in reducing the inventory cost and improving the production benefit of the enterprise.
Currently, consumer demand for personalized customized products is becoming more and more widespread. To meet such demands, enterprises need to continuously adjust the types and specifications of raw materials. Due to the dynamic change characteristic of user requirements, the types and specifications of raw materials are also dynamically changed, the space requirements of the storage tank in the storage system are also dynamically changed, and the space size of the storage tank needs to be adjusted. At present, the enterprise often uses manpower to adjust the space of the storage tank based on experience, which causes more space waste. With the development of new generation information technologies such as internet of things, embedded type and the like and the wide application of the new generation information technologies in manufacturing enterprises, the real-time states of a production system and a logistics system in a manufacturing workshop can be dynamically sensed, so that the requirement of a raw material storage space can be predicted according to the comprehensive analysis of the manufacturing process, storage data and the prediction condition of the size change of raw materials, and the size of a storage tank can be dynamically adjusted.
At present, the invention patent research of the storage tank adjustment of the intelligent storage system comprises the following steps: the Chinese patent 'a logistics management intelligent storage device' provides an intelligent storage device comprising a storage goods shelf, a lifting device and a mobile device, so that workers can store goods to a higher position of the goods shelf conveniently; the Chinese patent intelligent warehousing system provides an intelligent warehousing system comprising a warehousing cabinet and an operating platform, and an information management unit and a mobile robot which are contained in the warehousing cabinet can dynamically receive container information and dynamically plan the path of the mobile robot, so that the problem of inconvenience in storing and taking the warehousing cabinet is solved; the Chinese patent self-adaptive control method and the storage control system applied to intelligent storage can carry out self-adaptive stacking and loading on material packages by controlling a car loader, so that the stacking and loading can be smoothly finished without adjusting the posture of a vehicle under the condition that a driver stops the vehicle; chinese patent 'an intelligent warehousing multi-robot task allocation method based on a self-adaptive task pool' combines an intelligent algorithm to realize the optimized allocation of tasks dynamically added into a warehousing system, and the operation of the algorithm and the intelligent tasks of the robots are performed in parallel, so that the operation efficiency of the warehousing system is improved.
Although the above invention has a better promoting effect on the adaptive control of the intelligent warehousing system, in terms of the adaptive optimization and adjustment of the storage tank of the warehousing system to be solved by the invention, breakthroughs need to be made on the following new problems: because under the individualized customization environment, the raw and other materials of products are constantly changing, the space setting of storing groove needs to be dynamically adjusted, and current intelligent storage system mainly focuses on the automation realization of article access process and the self-adaptation distribution process of access task, and storage space's adjustment is often based on staff's experience and is carried out, brings storage space distribution unreasonable, the extravagant serious scheduling problem of space, and then can make storage logistics system raw and other materials deposit the volume and receive the restriction, storage operating efficiency receives the influence.
Disclosure of Invention
The invention provides a method and a system for adaptively optimizing and adjusting a storage tank of an intelligent storage system, aiming at solving the defects in the prior art.
The technical scheme adopted by the invention is as follows:
a self-adaptive optimization and adjustment method for a storage tank of an intelligent warehousing system comprises the following steps:
step 1: the obtained product order information, purchasing demand information and inventory information of different raw materials are used for predicting the future storage space demand information of the different raw materials;
step 2: making an optimal configuration decision scheme of the storage tank space of the intelligent storage system, and carrying out self-adaptive adjustment on the intelligent storage system;
step 2.1: acquiring the space size of a storage tank occupied by all raw materials;
step 2.2: reading the overall information of the storage shelf, wherein the overall information comprises the maximum size of each layer of shelf, the dependency relationship among different storage tanks and the constraint conditions set by the sizes of the storage tanks;
step 2.3: constructing a mathematical model for adaptive optimization and adjustment of the storage tank;
step 2.4: solving the storage tank optimization problem by adopting a genetic algorithm based on a mathematical model to obtain an optimal storage tank configuration structure scheme;
step 2.5: the intelligent storage system adjusts the storage shelf structure according to the optimal storage groove configuration structure scheme based on the obtained optimal storage groove configuration structure scheme, and self-adaptive adjustment of each storage groove is achieved.
Further, the method for predicting the storage space requirement information in step 1 comprises the following steps:
step 1.1: acquiring newly added production order information from an enterprise management system to obtain the personalized customization requirements of products;
step 1.2: obtaining the raw material use requirement of the order based on the composition structure of the personalized customized product;
step 1.3: analyzing the order completion condition currently processed by an enterprise and the inventory information of each material in the warehousing system to obtain the residual available quantity information of the raw materials in the warehousing system;
step 1.4: comparing the attribute information of the remaining available raw materials with the requirement information of the new order for the corresponding raw materials; if the attribute information of the existing raw material meets the requirements of the new product, the raw material is removed from the raw material requirements of the new order; otherwise, jumping to step 1.5;
step 1.5: based on the detailed requirements of the raw materials forming the product, obtaining a raw material requirement information table, and releasing the purchasing requirements to a purchasing department;
step 1.6: the purchasing department obtains the storage demand information of different raw materials, wherein the information comprises the packaging form and size, the quantity of the raw materials in a single package, the storage position, the environmental demand and the storage quantity;
step 1.7: and releasing the storage requirement information of the raw materials to the storage system.
Further, the method for obtaining the space size of the storage tank occupied by all the raw materials in the step 2.1 comprises the following steps:
step 2.1.1: randomly selecting space storage requirement information of a raw material, and obtaining the size of the space of the nearest storage groove;
step 2.1.2: iterating the step 2.1.1 until the storage tank space occupied by all the raw materials is calculated;
step 2.1.3: releasing the acquired storage tank space requirement information to a data centralized analysis unit;
further, the method for constructing the mathematical model for the storage tank adaptive optimization adjustment comprises the following steps:
step 2.3.1: constructing an objective function of the storage tank self-adaptive optimization adjustment, which respectively comprises the following steps:
maximizing space utilization objective function
Maximizing raw material storage capacity objective function
Wherein x isi,yi,ziThe sizes of the length, width and height effectively occupied by the ith raw material are respectively indicated, i is 1, 2, 3 … and n, and n is the total amount of the raw materials; hjRepresents the total volume of the jth shelf, j is 1, 2, 3 … and m, and m is the total number of shelves; n is a radical ofiIndicating the number of i-th raw materials;
step 2.3.2: and constructing constraint conditions for space optimization configuration of the storage tank, wherein the constraint conditions mainly comprise the limitation of the total length of each row of the storage shelf, the total height of the shelf, the height of a single-layer shelf and the width of the shelf.
Further, the shelf total length constraint is expressed as
Wherein S isLJaRefers to the collection of raw materials, T, intended to be placed on the jth shelf in row aaPlace the total amount of raw material, L, in row a for the J-th palletJIs the total length constraint of the jth shelf.
Further, the total shelf height constraint is expressed as
Wherein S isLJbRefers to the collection of raw materials, T, intended to be placed on the column b of the J-th palletbPlace the total amount of stock material in column b for the J shelf, HJAs the J-th shelfThe total height constraint.
Further, the single level shelf height constraint is expressed as
Wherein S isHJbRefers to the collection of raw materials intended to be placed on row a of the J-th pallet, HJaIs the height constraint of the jth shelf row a.
Further, the shelf width constraint is expressed as
Wherein S isWJRefers to the collection of raw materials, W, intended to be placed on the J-th shelfJIs the width constraint of the jth shelf.
Further, the process of solving the storage tank optimization problem by adopting the genetic algorithm in the step 2.4 is as follows:
step 2.4.1: encode the storing groove space arrangement scheme, the coding mode is: the chromosome is an M multiplied by N matrix, M refers to the required quantity of the storage tanks, N refers to the total number of the storage shelves, each element in the chromosome represents a position number corresponding to each raw material occupying the storage shelf, and the position numbers are arranged in sequence from the lower left corner to the upper right corner;
step 2.4.2: initializing a parent population based on the raw material number; the method comprises the following steps that a serial number is generated when raw materials enter an enterprise, and population initialization is carried out based on the serial number;
step 2.4.3: randomly selecting two initialized parent individuals, randomly selecting cross points for crossing to generate a new offspring population, and merging the parent and the offspring to form a new population;
step 2.4.4: calculating a fitness function of each offspring individual in the new population, wherein the fitness function is formulated according to two target functions;
step 2.4.5: competing the individuals in the combined population according to a fitness function, reserving the individuals with high fitness and forming a new generation of population;
step 2.4.6: repeating the steps until an iteration termination condition is met;
step 2.4.7: and obtaining an optimal storage tank configuration structure scheme based on the optimal solution obtained by iteration.
An intelligent storage system comprises a storage shelf structure, an information acquisition unit and a storage tank self-adaptive optimization control system; an execution motor of the storage shelf structure receives an instruction sent by the storage tank self-adaptive optimization control system, and the execution motor drives the storage shelf structure to realize self-adaptive adjustment of the space of the storage tank;
the information acquisition unit comprises an external information access unit and a real-time warehousing information acquisition unit, and the external information access unit acquires the information of raw materials entering or leaving the warehousing system; the real-time storage information acquisition unit accurately acquires the use states of raw materials and a goods shelf in the storage system by using intelligent sensing equipment;
the storage tank self-adaptive optimization control system comprises a data centralized analysis unit and a self-adaptive control unit: the data centralized analysis unit predicts the raw material storage space requirement from the information acquisition unit and obtains an optimal storage tank configuration structure scheme; the self-adaptive control unit is connected with an execution motor of the storage shelf structure, generates a working instruction according to an optimal configuration scheme, actively adjusts the storage shelf structure and realizes self-adaptive adjustment of each storage groove.
The invention has the beneficial effects that:
compared with the prior art, the intelligent warehousing system capable of adaptively adjusting the space is constructed by configuring the intelligent equipment, on one hand, real-time state information of all aspects of the warehousing system can be sensed, and on the other hand, the decision-making system can perform autonomous analysis and adjustment on the space of the storage tank based on real-time state data; then, a decision-making system of the intelligent warehousing system can be combined with a raw material purchasing system and a product production system, and the space requirements for storing different raw materials are predicted by comprehensively analyzing purchasing data, production field use data and production plan data; finally, the decision-making system of the intelligent storage analyzes the reasonable space size which can be provided by the decision-making system aiming at the space requirement based on the raw materials, and actively obtains the optimal storage tank adjusting scheme based on the genetic algorithm while ensuring that the raw materials can be smoothly stored and taken, and independently adjusts the size of each storage tank according to the optimizing scheme. Based on the invention, the problem that storage space cannot be used or waste is serious due to frequent change of raw materials of the current manufacturing enterprises, and further storage quantity of the raw materials is limited can be solved, and self-adaptive high-efficiency operation of the intelligent workshop storage system is ensured.
Drawings
FIG. 1 is a schematic diagram of the overall structure of the intelligent warehousing system;
FIG. 2 is a schematic view of the shelf structure of the present invention;
FIG. 3 is a logic diagram of the overall operation of the method of the present invention;
FIG. 4 is a flow chart of the raw material storage space requirement prediction of the method of the present invention;
FIG. 5 is a flow chart of the space optimization configuration of the adaptive storage tank according to the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention designs a self-adaptive optimization adjustment method for a storage tank of an intelligent storage system, and aims to dynamically predict the requirement of a raw material storage space on the basis of the intelligent storage system, so that the spatial structure of the storage tank is configured in a self-adaptive optimization manner, and the efficiency of the storage system is improved. The specific implementation steps are as follows:
an intelligent warehousing system is constructed based on advanced embedded control, internet of things and cloud computing technologies and comprises a warehousing shelf structure, an information acquisition unit and a storage tank adaptive optimization control system, wherein the warehousing shelf structure is provided with a real-time state sensing function and an intelligent decision analysis and adaptive optimization adjustment function; the method comprises the following specific steps:
1. design of a dynamically adjustable storage shelf structure reference is made to fig. 1. The storage shelf structure consists of a plurality of layers of transverse plates, vertical plates, telescopic rods and an actuating motor; as shown in fig. 2, each telescopic rod is assembled by a plurality of telescopic structures, the telescopic rod is used as a framework of the storage shelf, and the space formed inside the telescopic rod is a storage groove. One end of the telescopic rod is connected with an actuating motor, and the actuating motor drives the telescopic rod to do telescopic motion along the horizontal direction. A transverse plate is fixedly connected between the opposite telescopic structures of the two telescopic rods which are parallel to each other on the same horizontal plane and used for placing raw materials; at the same horizontal height, an adjustable space is formed between the adjacent transverse plates.
In a similar way, the two ends of each horizontal telescopic rod are respectively provided with a vertical telescopic rod, the two vertical telescopic rods on the same side are fixedly provided with a vertical plate on a telescopic structure with the same height, and an adjustable space is arranged between the adjacent vertical plates.
In the process of self-adaptive adjustment of the storage tank, the horizontal telescopic rod is driven to move to one side through the execution motor, so that the adjustable space between the transverse plates is reduced or enlarged; similarly, the actuating motor drives the adjacent vertical telescopic rod to move to one side, so that the adjustable space between the transverse plates is reduced or enlarged; to obtain the expected dimensions; the self-adaptive adjustment of the storage space of the storage tank is realized.
2. The information acquisition demand of analysis intelligent warehousing system disposes the information acquisition unit, and the information acquisition unit contains two parts: the system comprises an external information access unit and a real-time warehousing information acquisition unit. The external information access unit is connected with a raw material purchasing system, an enterprise management system and a workshop processing process management system to obtain the information of raw materials which are about to enter or leave the warehousing system; the real-time storage information acquisition unit is mainly used for accurately acquiring the use states of raw materials and goods shelves in the storage system by utilizing intelligent sensing equipment. The real-time warehousing information acquisition unit specifically comprises the following equipment:
2.1, configuring Radio Frequency Identification (RFID) tags on the raw materials according to the state acquisition requirements of the raw materials, and configuring RFID readers at the main entrance and exit of the storage system and the central position nodes of the goods shelf so as to realize the real-time acquisition of the quantity and the position information of the raw materials.
2.2, aiming at the position and the state information of the carrying equipment and the goods storing and taking robot in the warehousing system, the position and the state information can be collected by arranging equipment such as a GPS (global positioning system), a wireless sensor and the like on the walls of the carrying equipment, the robot and the warehousing system.
2.3, aiming at the quality initial inspection information of the raw materials in the warehousing system, the two-dimensional and three-dimensional picture information of the raw materials can be obtained by arranging the machine vision device on the carrying equipment, and then a quality inspection model is obtained through a deep learning algorithm, so that the quality qualified information of the raw materials is obtained, and the quality of the raw materials flowing into a workshop is ensured to meet the processing requirement.
3. And a storage tank self-adaptive optimization control system is configured to realize self-adaptive optimization and autonomous adjustment of the storage tank space. The storage tank self-adaptive optimization control system comprises a data centralized analysis unit and a self-adaptive control unit: the data centralized analysis unit predicts the raw material storage space requirement from the information acquisition unit and obtains an optimal storage tank configuration structure scheme; the self-adaptive control unit is respectively connected with a power unit (motor) in the storage shelf structure, generates a working instruction according to an optimal configuration scheme, actively adjusts the storage shelf structure and realizes self-adaptive adjustment of each storage groove.
The intelligent warehousing system capable of adaptively adjusting the space is constructed, real-time state information of all aspects of the warehousing system can be sensed, and the decision-making system can perform autonomous analysis and adjustment on the space of the storage tank based on real-time state data; then, a decision-making system of the intelligent warehousing system can be combined with a raw material purchasing system and a product production system, and the space requirements for storing different raw materials are predicted by comprehensively analyzing purchasing data, production field use data and production plan data; finally, the decision-making system of the intelligent storage analyzes the reasonable space size which can be provided by the decision-making system aiming at the space requirement based on the raw materials, and actively obtains the optimal storage tank adjusting scheme based on the genetic algorithm while ensuring that the raw materials can be smoothly stored and taken, and independently adjusts the size of each storage tank according to the optimizing scheme.
A self-adaptive optimization and adjustment method for a storage tank of an intelligent warehousing system comprises the following steps:
step 1: referring to fig. 3(b) and 4, based on the product order information, the purchasing demand information and the inventory information of the quantity, the position, the quality and the like of different raw materials obtained in the information acquisition unit, the future storage space demand information of different raw materials is predicted. The method comprises the following specific steps:
step 1.1: and acquiring newly added production order information from the enterprise management system to obtain the personalized customization requirements of the product.
Step 1.2: and obtaining the raw material use requirement of the order based on the composition structure of the personalized customized product.
Step 1.3: and analyzing the order completion condition currently processed by the enterprise and the inventory information of all materials in the warehousing system to obtain the information of the residual available quantity of the raw materials in the warehousing system.
Step 1.4: and comparing the attribute information of the remaining available raw materials with the requirement information of the new order for the corresponding raw materials. If the attribute information of the existing raw material meets the requirements of the new product, the raw material is removed from the raw material requirements of the new order; otherwise, jump to step 1.5.
Step 1.5: based on the detailed demand of the raw materials constituting the product, a raw material demand information table is obtained, releasing the purchase demand to the purchasing department.
Step 1.6: the purchasing department selects a proper product supplier through an invitation form or a direct purchasing form, and obtains the storage requirement information of different raw materials according to the selection, wherein the information comprises a packaging form and size, the quantity of the raw materials in a single package, a storage position and environmental requirement and a storage quantity.
Step 1.7: and releasing the storage demand information of the raw materials to a storage system based on the purchase analysis.
Step 2: referring to fig. 1 and 5, an optimal configuration decision scheme of the storage tank space of the intelligent warehousing system is made and adaptive adjustment is performed.
Step 2.1: and (4) acquiring the space size of the storage tank occupied by all raw materials.
Step 2.1.1: and randomly selecting the space storage requirement information of one raw material to obtain the size of the space of the nearest storage groove. Because the transverse plate or the vertical plate consists of the non-adjustable solid plate and the adjustable structure,and the adjustable structure has a maximum value and a minimum value, so that the storage slots in the storage shelves can only provide specific sizes, and the size acquirable values can be expressed by the following formula:
wherein k is1,gIndicates the number of solid plates in the g-th storage tank, k2,gNumber of adjustable structures in the g-th storage tank, wsx,gDenotes the fixed width, w, of the solid plate in the g-th storage tanktj,gThe width of the adjustable structure in the g-th storage groove is shown and is an adjustable value, and the width is obtained according to the design size of the adjustable structure. In order to ensure that the storage space is utilized to the maximum extent and ensure that raw materials can be smoothly stored and taken, the storage groove space needs to be selected to be larger than the required size.
Step 2.1.2: and (5) iterating the step 2.1.1 until the storage tank space occupied by all the raw materials is calculated.
Step 2.1.3: and releasing the acquired storage tank space requirement information to a storage tank data centralized analysis unit.
Step 2.2: and reading the overall information of the storage shelf, wherein the overall information comprises the maximum size of each layer of shelf, the dependency relationship among different storage tanks and the constraint conditions set by the sizes of the storage tanks.
Step 2.3: and constructing a mathematical model for the self-adaptive optimization and adjustment of the storage tank.
Step 2.3.1: and constructing an objective function of the storage tank self-adaptive optimization adjustment. Two optimization objectives are included, maximum space utilization and maximum raw material storage quantity, respectively. Wherein, maximizing the space utilization rate can be expressed by the following formula:
The maximum raw material stock quantity can be expressed by the following formula:
Step 2.3.2: and constructing constraint conditions for space optimization configuration of the storage tank. The constraint conditions mainly comprise the total length of each row of the storage shelves, the total height of the shelves, the height of a single-layer shelf and the width limit of the shelves, and the constraint that each shelf can store articles can be respectively expressed by the following formula.
The total length constraint of the goods shelf can be expressed by formula
Is shown in which SLJaRefers to the collection of raw materials that are expected to be placed on row a of the jth shelf. T isaThe total amount of raw material in row a is placed for the jth shelf. L isJIs the total length constraint of the jth shelf.
The total height constraint of the goods shelf can be expressed by formula
Is shown in which SLJbRefers to the collection of raw materials expected to be placed on column b of the J-th shelf. T isbThe total amount of stock material in column b is placed for the J shelf. HJIs the total height constraint of the jth shelf.
Single level shelf height constraints may be formulated
Is represented by the formula, wherein SHJbRefers to the collection of raw materials that are expected to be placed on row a of the jth shelf. HJaIs the height constraint of the jth shelf row a.
Shelf width constraints may be formulated
Is represented by the formula, wherein SWJRefers to the collection of raw materials that are expected to be placed on the jth shelf. WJIs the width constraint of the jth shelf.
The constraints that each shelf can store items refer to constraints such as location definition of different areas in the warehousing system.
Step 2.4: aiming at the problems, the genetic algorithm is adopted to solve the storage tank optimization problem.
Step 2.4.1: encode the storing groove space arrangement scheme, the coding mode is: the chromosome is an M multiplied by N matrix, M refers to the required quantity of the storage tanks, N refers to the total number of the storage shelves, each element in the chromosome represents a corresponding position number of each raw material occupying the storage shelf, and the position numbers are arranged in the sequence from the lower left corner to the upper right corner.
Step 2.4.2: the parent population is initialized based on the raw material number. Raw materials have a number when entering an enterprise, and the number is generally compiled according to the raw material category, so that population initialization can be carried out based on the number.
Step 2.4.3: randomly selecting two initialized parent individuals, randomly selecting cross points for crossing to generate a new child population, and merging the parent and the child to form a new population.
Step 2.4.4: and calculating the fitness function of each filial generation individual in the new population, wherein the fitness function is formulated according to the two target functions.
Step 2.4.5: and (4) competing the individuals in the combined population according to the fitness function, reserving the individuals with high fitness and forming a new generation of population.
Step 2.4.6: and repeating the steps until an iteration termination condition is met. The iteration termination condition includes two kinds: the iteration times reach the set value and the target function reaches the expected value.
Step 2.4.7: and obtaining an optimal storage tank configuration structure scheme based on the optimal solution obtained by iteration.
Step 2.5: the intelligent storage system adjusts the storage shelf structure based on the obtained optimal scheme, namely the control system adjusts the storage shelf structure according to the optimal storage groove configuration structure scheme, and self-adaptive adjustment of each storage groove is realized.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (10)
1. A self-adaptive optimization and adjustment method for a storage tank of an intelligent warehousing system is characterized by comprising the following steps:
step 1: the obtained product order information, purchasing demand information and inventory information of different raw materials are used for predicting the future storage space demand information of the different raw materials;
step 2: making an optimal configuration decision scheme of the storage tank space of the intelligent storage system, and carrying out self-adaptive adjustment on the intelligent storage system;
step 2.1: acquiring the space size of a storage tank occupied by all raw materials;
step 2.2: reading the overall information of the storage shelf, wherein the overall information comprises the maximum size of each layer of shelf, the dependency relationship among different storage tanks and the constraint conditions set by the sizes of the storage tanks;
step 2.3: constructing a mathematical model for adaptive optimization and adjustment of the storage tank;
step 2.4: solving the storage tank optimization problem by adopting a genetic algorithm based on a mathematical model to obtain an optimal storage tank configuration structure scheme;
step 2.5: the intelligent storage system adjusts the storage shelf structure according to the optimal storage groove configuration structure scheme based on the obtained optimal storage groove configuration structure scheme, and self-adaptive adjustment of each storage groove is achieved.
2. The method for adaptively optimizing and adjusting the storage tank of the intelligent storage system according to claim 1, wherein the method for predicting the storage space requirement information in the step 1 comprises the following steps:
step 1.1: acquiring newly added production order information from an enterprise management system to obtain the personalized customization requirements of products;
step 1.2: obtaining the raw material use requirement of the order based on the composition structure of the personalized customized product;
step 1.3: analyzing the order completion condition currently processed by an enterprise and the inventory information of each material in the warehousing system to obtain the residual available quantity information of the raw materials in the warehousing system;
step 1.4: comparing the attribute information of the remaining available raw materials with the requirement information of the new order for the corresponding raw materials; if the attribute information of the existing raw material meets the requirements of the new product, the raw material is removed from the raw material requirements of the new order; otherwise, jumping to step 1.5;
step 1.5: based on the detailed requirements of the raw materials forming the product, obtaining a raw material requirement information table, and releasing the purchasing requirements to a purchasing department;
step 1.6: the purchasing department obtains the storage demand information of different raw materials, wherein the information comprises the packaging form and size, the quantity of the raw materials in a single package, the storage position, the environmental demand and the storage quantity;
step 1.7: and releasing the storage requirement information of the raw materials to the storage system.
3. The method for adaptively optimizing and adjusting the storage tank of the intelligent storage system according to claim 1, wherein the method for acquiring the space size of the storage tank occupied by all raw materials in the step 2.1 comprises the following steps:
step 2.1.1: randomly selecting space storage requirement information of a raw material, and obtaining the size of the space of the nearest storage groove;
step 2.1.2: iterating the step 2.1.1 until the storage tank space occupied by all the raw materials is calculated;
step 2.1.3: and releasing the acquired storage tank space requirement information to a data centralized analysis unit.
4. The storage tank adaptive optimization and adjustment method of the intelligent warehousing system as claimed in claim 1, wherein the method for constructing the mathematical model for the storage tank adaptive optimization and adjustment comprises:
step 2.3.1: constructing an objective function of the storage tank self-adaptive optimization adjustment, which respectively comprises the following steps:
maximizing space utilization objective function
Maximizing raw material storage capacity objective function
Wherein x isi,yi,ziThe sizes of the length, width and height effectively occupied by the ith raw material are respectively indicated, i is 1, 2, 3 … and n, and n is the total amount of the raw materials; hjRepresents the total volume of the jth shelf, j is 1, 2, 3 … and m, and m is the total number of shelves; n is a radical ofiIndicating the number of i-th raw materials;
step 2.3.2: and constructing constraint conditions for space optimization configuration of the storage tank, wherein the constraint conditions mainly comprise the limitation of the total length of each row of the storage shelf, the total height of the shelf, the height of a single-layer shelf and the width of the shelf.
5. The adaptive optimization and adjustment method for the storage tank of the smart storage system according to claim 4, wherein the constraint of total shelf length is expressed as
Wherein S isLJaRefers to the collection of raw materials, T, intended to be placed on the jth shelf in row aaPlace the total amount of raw material, L, in row a for the J-th palletJIs the total length constraint of the jth shelf.
6. The method as claimed in claim 4, wherein the total shelf height constraint is expressed as
Wherein S isLJbRefers to the collection of raw materials, T, intended to be placed on the column b of the J-th palletbPlace the total amount of stock material in column b for the J shelf, HJIs the total height constraint of the jth shelf.
7. The adaptive optimization and adjustment method for the bin of the smart storage system as claimed in claim 4, wherein the constraint of the height of the single-layer shelf is expressed as
Wherein S isHJbRefers to the collection of raw materials intended to be placed on row a of the J-th pallet, HJaIs the height constraint of the jth shelf row a.
8. The adaptive optimization and adjustment method for the bin of the smart storage system as claimed in claim 4, wherein the shelf width constraint is expressed as
Wherein S isWJRefers to the collection of raw materials, W, intended to be placed on the J-th shelfJIs the width constraint of the jth shelf.
9. The method for adaptively optimizing and adjusting the storage tank of the intelligent storage system according to any one of claims 4 to 8, wherein the process of solving the storage tank optimization problem by using a genetic algorithm in the step 2.4 is as follows:
step 2.4.1: encode the storing groove space arrangement scheme, the coding mode is: the chromosome is an M multiplied by N matrix, M refers to the required quantity of the storage tanks, N refers to the total number of the storage shelves, each element in the chromosome represents a position number corresponding to each raw material occupying the storage shelf, and the position numbers are arranged in sequence from the lower left corner to the upper right corner;
step 2.4.2: initializing a parent population based on the raw material number; the method comprises the following steps that a serial number is generated when raw materials enter an enterprise, and population initialization is carried out based on the serial number;
step 2.4.3: randomly selecting two initialized parent individuals, randomly selecting cross points for crossing to generate a new offspring population, and merging the parent and the offspring to form a new population;
step 2.4.4: calculating a fitness function of each offspring individual in the new population, wherein the fitness function is formulated according to two target functions;
step 2.4.5: competing the individuals in the combined population according to a fitness function, reserving the individuals with high fitness and forming a new generation of population;
step 2.4.6: repeating the steps until an iteration termination condition is met;
step 2.4.7: and obtaining an optimal storage tank configuration structure scheme based on the optimal solution obtained by iteration.
10. An intelligent warehousing system based on the intelligent warehousing system storage tank adaptive optimization adjustment method of claim 9, characterized by comprising a warehousing shelf structure, an information acquisition unit and a storage tank adaptive optimization control system; an execution motor of the storage shelf structure receives an instruction sent by the storage tank self-adaptive optimization control system, and the execution motor drives the storage shelf structure to realize self-adaptive adjustment of the space of the storage tank;
the information acquisition unit comprises an external information access unit and a real-time warehousing information acquisition unit, and the external information access unit acquires the information of raw materials entering or leaving the warehousing system; the real-time storage information acquisition unit accurately acquires the use states of raw materials and a goods shelf in the storage system by using intelligent sensing equipment;
the storage tank self-adaptive optimization control system comprises a data centralized analysis unit and a self-adaptive control unit: the data centralized analysis unit predicts the raw material storage space requirement from the information acquisition unit and obtains an optimal storage tank configuration structure scheme; the self-adaptive control unit is connected with an execution motor of the storage shelf structure, generates a working instruction according to an optimal configuration scheme, actively adjusts the storage shelf structure and realizes self-adaptive adjustment of each storage groove.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114496154A (en) * | 2021-12-31 | 2022-05-13 | 武汉库柏特科技有限公司 | Method and device for calculating laminate medicine slot of medicine dispensing machine |
| CN118616343A (en) * | 2024-08-12 | 2024-09-10 | 武汉华工赛百数据系统有限公司 | Workpiece sorting method, device, computer equipment and readable storage medium |
| CN119160559A (en) * | 2024-09-20 | 2024-12-20 | 达以云景科技(广州)有限公司 | Cloud warehouse material storage location dynamic management system and method based on time control |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473616A (en) * | 2013-09-17 | 2013-12-25 | 四川航天系统工程研究所 | Dynamic goods allocation planning method and system for processing multi-variety goods and material storage |
| CN107861490A (en) * | 2017-12-25 | 2018-03-30 | 长沙智能制造研究总院有限公司 | Based on industrial cloud platform intelligence manufacture bearing assembling line |
| CN109190806A (en) * | 2018-08-15 | 2019-01-11 | 浙江优尼家装饰科技有限公司 | A kind of plate components dynamic slotting optimization method towards storage |
| CN109636092A (en) * | 2018-10-29 | 2019-04-16 | 中国电子科技集团公司第二十八研究所 | A kind of warehouse goods yard distribution method based on double factor optimization |
| CN109886447A (en) * | 2018-12-17 | 2019-06-14 | 江苏省水利防汛物资储备中心 | For flood control take precautions against drought goods and materials intelligent storage goods yard distribution method and system |
| US20200065758A1 (en) * | 2018-08-24 | 2020-02-27 | Exel Inc. d/b/a DHL Supply Chain (USA) | Shipping carton optimization system and method |
| CN111222827A (en) * | 2019-12-31 | 2020-06-02 | 云南电网有限责任公司楚雄供电局 | Goods position management method and device, storage medium and electronic equipment |
| CN111815019A (en) * | 2020-06-04 | 2020-10-23 | 厦门汉印电子技术有限公司 | Production material storage method, device, equipment and readable storage medium |
| US20210110333A1 (en) * | 2016-11-29 | 2021-04-15 | Hangzhou Hikrobot Technology Co., Ltd | Ex-warehousing method and device |
| CN112766857A (en) * | 2021-01-25 | 2021-05-07 | 海尔数字科技(青岛)有限公司 | Intelligent warehouse management platform, system and method |
-
2021
- 2021-07-28 CN CN202110854813.7A patent/CN113636254B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103473616A (en) * | 2013-09-17 | 2013-12-25 | 四川航天系统工程研究所 | Dynamic goods allocation planning method and system for processing multi-variety goods and material storage |
| US20210110333A1 (en) * | 2016-11-29 | 2021-04-15 | Hangzhou Hikrobot Technology Co., Ltd | Ex-warehousing method and device |
| CN107861490A (en) * | 2017-12-25 | 2018-03-30 | 长沙智能制造研究总院有限公司 | Based on industrial cloud platform intelligence manufacture bearing assembling line |
| CN109190806A (en) * | 2018-08-15 | 2019-01-11 | 浙江优尼家装饰科技有限公司 | A kind of plate components dynamic slotting optimization method towards storage |
| US20200065758A1 (en) * | 2018-08-24 | 2020-02-27 | Exel Inc. d/b/a DHL Supply Chain (USA) | Shipping carton optimization system and method |
| CN109636092A (en) * | 2018-10-29 | 2019-04-16 | 中国电子科技集团公司第二十八研究所 | A kind of warehouse goods yard distribution method based on double factor optimization |
| CN109886447A (en) * | 2018-12-17 | 2019-06-14 | 江苏省水利防汛物资储备中心 | For flood control take precautions against drought goods and materials intelligent storage goods yard distribution method and system |
| CN111222827A (en) * | 2019-12-31 | 2020-06-02 | 云南电网有限责任公司楚雄供电局 | Goods position management method and device, storage medium and electronic equipment |
| CN111815019A (en) * | 2020-06-04 | 2020-10-23 | 厦门汉印电子技术有限公司 | Production material storage method, device, equipment and readable storage medium |
| CN112766857A (en) * | 2021-01-25 | 2021-05-07 | 海尔数字科技(青岛)有限公司 | Intelligent warehouse management platform, system and method |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114496154A (en) * | 2021-12-31 | 2022-05-13 | 武汉库柏特科技有限公司 | Method and device for calculating laminate medicine slot of medicine dispensing machine |
| CN118616343A (en) * | 2024-08-12 | 2024-09-10 | 武汉华工赛百数据系统有限公司 | Workpiece sorting method, device, computer equipment and readable storage medium |
| CN118616343B (en) * | 2024-08-12 | 2024-11-05 | 武汉华工赛百数据系统有限公司 | Workpiece sorting method, device, computer equipment and readable storage medium |
| CN119160559A (en) * | 2024-09-20 | 2024-12-20 | 达以云景科技(广州)有限公司 | Cloud warehouse material storage location dynamic management system and method based on time control |
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|---|---|
| CN113636254B (en) | 2022-09-16 |
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