CN111798140A - Intelligent arrangement method for stored goods - Google Patents

Intelligent arrangement method for stored goods Download PDF

Info

Publication number
CN111798140A
CN111798140A CN202010649220.2A CN202010649220A CN111798140A CN 111798140 A CN111798140 A CN 111798140A CN 202010649220 A CN202010649220 A CN 202010649220A CN 111798140 A CN111798140 A CN 111798140A
Authority
CN
China
Prior art keywords
goods
cargo
warehoused
space
function
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010649220.2A
Other languages
Chinese (zh)
Inventor
刘平英
陈思华
秦熙蕾
范瑜轩
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Information Science and Technology
Original Assignee
Nanjing University of Information Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Information Science and Technology filed Critical Nanjing University of Information Science and Technology
Priority to CN202010649220.2A priority Critical patent/CN111798140A/en
Publication of CN111798140A publication Critical patent/CN111798140A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

The invention discloses an intelligent arrangement method for warehoused goods, which is used for collecting goods information of goods to be warehoused; constructing a real-time goods allocation model; solving a goods space distribution model; and according to the goods location distribution model solving result, distributing the goods to be warehoused to the corresponding positions. Through optimizing the goods distribution model, plan the cargo handling device route, not only can improve the rate of utilization of goods shelves, avoid the large tracts of land of goods to pile up, can also practice thrift goods access time to improve the efficiency of commodity circulation.

Description

Intelligent arrangement method for stored goods
Technical Field
The invention belongs to the technical field of intelligent warehousing, and particularly relates to an intelligent arrangement method for warehoused goods.
Background
At present, intelligent warehousing and big data, cloud computing and other new-generation internet technologies are deeply integrated in China, and the whole industry is advanced towards the direction of high-efficiency operation and rapid circulation.
In recent years, warehousing equipment represented by shelves, pallets, and forklifts and a warehousing management information system are well applied to large and medium-sized warehousing enterprises. According to GGII data measurement and calculation, the mechanized operation rate of national warehousing industry is more than 35%, and the warehousing management informatization reaches more than 50%. From the informatization level, the informatization of the warehousing industry of China is developing towards the intelligent warehousing and internet platform. The application proportion of automatic identification technologies such as bar codes, intelligent labels and radio frequency identification, visualization and goods tracking systems and automatic or quick sorting technologies to professional storage enterprises such as medicine, tobacco, electronics and electronic commerce is improved to some extent in large-scale enterprises.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide an intelligent arrangement method for warehoused goods, aiming at the defects of the prior art.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows:
an intelligent arrangement method for warehoused goods comprises the following steps:
step 1: collecting goods information of goods to be warehoused;
step 2: constructing a real-time goods allocation model;
and step 3: solving a goods space distribution model;
and 4, step 4: and according to the goods location distribution model solving result, distributing the goods to be warehoused to the corresponding positions.
In order to optimize the technical scheme, the specific measures adopted further comprise:
the step 1 of collecting the cargo information of the cargo to be warehoused is as follows: the delivery place, the destination, the cargo weight, the cargo volume, the cargo type, the cargo turnover rate and the cargo property of the cargo to be warehoused are collected.
The step 2 is to construct a real-time cargo space allocation model, wherein the real-time cargo space allocation model is as follows:
Figure BDA0002574280280000011
wherein f is1For the target optimization function of the cargo turnover rate, f2Optimizing a function for a cargo-dependency objective, f3Optimizing a function for a shelf stability objective, f4Is goods anda matching degree target optimization function of the goods space;
λ1、λ2、λ3、λ4are all function parameters.
The target optimization function of the cargo turnover rate is as follows:
Figure BDA0002574280280000021
in the formula, vx、vyAnd vzThe moving speed of the stacker in the x, y and z directions is shown; l is the unit cargo space length; xi, yi and zi are coordinates of the goods space i in the x, y and z directions; a. b and c are the total number of the goods positions in the x, y and z directions; and P is the cargo turnover rate.
The above-mentioned cargo correlation objective optimization function is:
Figure BDA0002574280280000022
in the formula, xi,yi,ziThe coordinates of the cargo space i in the x direction, the y direction and the z direction are shown; a. b and c are the total number of the goods positions in the x, y and z directions, (a)k，bk，ck) The coordinates of the central cargo space of the kth cargo.
The shelf stability objective optimization function described above is:
Figure BDA0002574280280000023
wherein L is the length of the unit cargo space, mkIs the weight of cargo of the kth class, xi,yi,ziThe coordinates of a cargo space i in the x, y and z directions are shown, n represents the storage quantity of the k-th cargo, and k belongs to [1, n ]]Is an integer of (1).
The matching degree target optimization function of the goods and the goods positions is as follows:
Figure BDA0002574280280000024
in the formula, V*Representing the degree of compatibility, M, between the volume of goods to be warehoused and the volume of the free goods space i*Representing the compatibility between the weight of goods to be warehoused and the bearing weight of the space goods position i; n is a radical of1、N2The total number of the goods shelves in the x direction and the total number of the goods shelves in the y direction are obtained;
V*and M*Respectively, as follows:
Figure BDA0002574280280000031
wherein ViRepresenting the carrying volume, M, of the cargo space iiIs the bearing weight of a cargo space i, VjRepresenting the volume of goods to be warehoused, MjIndicating the weight of the goods to be warehoused.
The step 3 adopts a genetic algorithm to solve the goods allocation model, and adopts the reciprocal of a goods allocation model function f as a fitness function of the genetic algorithm; the value of the parameter lambda is determined by means of an analytic hierarchy process.
The invention has the following beneficial effects:
optimize the goods distribution model, plan the cargo handling device route, not only can improve the rate of utilization of goods shelves, avoid the large tracts of land of goods to pile up, can also practice thrift goods access time to improve the efficiency of commodity circulation.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic view of a container;
fig. 3 is a schematic view of a shelf.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, the intelligent arranging method for the warehoused goods of the present invention includes:
step 1: collecting goods information of goods to be warehoused;
step 2: constructing a real-time goods allocation model;
and step 3: solving a goods space distribution model;
and 4, step 4: and according to the goods location distribution model solving result, distributing the goods to be warehoused to the corresponding positions.
Namely, the solving result is sent to the warehouse handling equipment, and the warehouse handling equipment carries the goods to the specified place.
In the embodiment, the arrangement of the stored goods is according to the principle of entering and exiting the warehouse nearby, the principle of shelf stability, the principle of shelf relevance and the principle of goods identity.
According to the warehouse-in and warehouse-out nearby principle, the distribution place of the goods is marked, and the goods needing to be delivered out of the warehouse are placed on the goods shelf close to the warehouse-in and warehouse-out as soon as possible;
the shelf stability principle is that larger and heavier goods are placed below the shelf, and small and light goods are placed above the shelf;
the goods correlation principle is that a batch of goods with similar goods characteristics are placed at the same place;
according to the principle of cargo uniformity, the size of the cargo should be uniform with that of the goods shelf, and the utilization rate of the goods shelf is improved.
Referring to fig. 2 and fig. 3, assuming that the shelf is a unit shelf, a placing mode of x rows, y rows and z layers is adopted, and the position of the warehousing entrance is the 1 st row, 1 st row and 1 st layer; the goods shelves are equidistant, the sizes of the goods positions are the same, and the bearing capacity of the goods positions is the same; the time required by the mechanical arm at each cargo space is the same; the cargo space is numbered from 1,2,3, … to the last cargo space using natural numbers, where the cargo space No. 1 is located at row 1, column 1, level 1, the cargo space No. 2 is located at row 1, column 2, level 1, and so on. The coordinates of the containers to be placed are calculated in the model according to the serial numbers, so that the corresponding containers can be conveniently found.
In an embodiment, the step 1 of collecting the cargo information of the cargo to be warehoused is as follows: when the goods arrive, the delivery place, the destination, the weight, the volume, the type, the turnover rate, the property and the like of the goods to be warehoused are collected, wherein the turnover rate P of the goods needs to be determined according to the warehousing and ex-warehouse time of the goods.
In an embodiment, the step 2 of constructing the real-time cargo space allocation model includes:
Figure BDA0002574280280000041
wherein f is1For the target optimization function of the cargo turnover rate, f2Optimizing a function for a cargo-dependency objective, f3Optimizing a function for a shelf stability objective, f4Optimizing a function for the matching degree target of the goods and the goods positions;
λ1、λ2、λ3、λ4are all function parameters.
In an embodiment, the target optimization of the cargo turnover rate includes that the cargo with higher frequency of entering and exiting the warehouse is required to have shorter entering and exiting time, and the cargo with lower frequency of entering and exiting the warehouse can have relatively longer entering and exiting time.
The target optimization function of the cargo turnover rate is as follows:
Figure BDA0002574280280000042
in the formula, vx、vyAnd vzThe moving speed of the stacker in the x, y and z directions is shown; l is the unit cargo space length; xi, yi and zi are coordinates of the goods space i in the x, y and z directions; a. b and c are the total number of the goods positions in the x, y and z directions; p is the turnover rate of the goods, i.e. the number of turnover of the goods in the warehouse for a certain period of time, i.e. the frequency of the goods entering or leaving the warehouse in general. Thus, the formula indicates a coordinate of (x)i,yi,zi) The moving distance of the kth cargo on the cargo space is summed.
In the embodiment, the goods correlation target optimization considers the difference of the delivery attributes of the goods, stores the goods with the same delivery time as much as possible, and avoids the missing of the optimal delivery time of the next-day objects under the conditions of fully considering the space utilization rate and reasonably storing the goods with the same attribute.
Carrying out cargo correlation analysis through the properties of the cargo, wherein the cargo correlation target optimization function is as follows:
Figure BDA0002574280280000051
in the formula, xi,yi,ziThe coordinates of the cargo space i in the x direction, the y direction and the z direction are shown; a. b and c are the total number of the goods positions in the x, y and z directions, (a)k，bk，ck) The specific value of the central goods position coordinate of the kth goods is determined according to the actual condition of the warehouse.
In the embodiment, the shelf stability target optimizes and considers the stress of the shelf to reasonably store the goods. The stability of the goods shelf is related to the gravity center of the goods shelf, and according to the principle of the stability of the goods shelf with a top light and a bottom heavy, the gravity center of the goods shelf can be lowered, and the stability of the goods shelf can be enhanced.
Due to the diversity of goods, the size and weight of the goods may be different, and due to the fact that different goods are stored on the same shelf, the shelf stability needs to be ensured. The shelf stability objective optimization function is:
Figure BDA0002574280280000052
wherein L is the length of the unit cargo space, mkIs the weight of cargo of the kth class, xi,yi,ziThe coordinates of a cargo space i in the x, y and z directions are shown, n represents the storage quantity of the k-th cargo, and k belongs to [1, n ]]Is an integer of (1). Thus, the above formula represents the center of gravity of all shelves of the warehouse, and the smaller its value, the better the shelf stability.
In the embodiment, the matching degree target optimization of the goods and the goods positions aims to improve the utilization rate of the warehouse and increase the utilization rate of the warehouse space, and the goods are allocated to the best matching goods positions as much as possible in the goods position allocation process.
Substituting the volume of the goods and the compatibility degree with the goods position into a matching degree target optimization function of the goods and the goods position to analyze the matching degree of the goods and the goods shelf, wherein the matching degree target optimization function of the goods and the goods position is as follows:
Figure BDA0002574280280000053
in the formula, V*Representing the degree of compatibility, M, between the volume of goods to be warehoused and the volume of the free goods space i*Representing the compatibility between the weight of goods to be warehoused and the bearing weight of the space goods position i; n is a radical of1、N2The total number of the goods shelves in the x direction and the total number of the goods shelves in the y direction are obtained;
V*and M*Respectively, as follows:
Figure BDA0002574280280000061
wherein ViRepresenting the carrying volume, M, of the cargo space iiIs the bearing weight of a cargo space i, VjRepresenting the volume of goods to be warehoused, MjIndicating the weight of the goods to be warehoused.
In an embodiment, in step 3, a genetic algorithm is used to solve the cargo space allocation model, the reciprocal of the cargo space allocation model function f is used as a fitness function of the genetic algorithm, and the smaller the value of f, the larger the fitness function value, the more excellent the individual is in the genetic population. The value of the parameter lambda is determined by adopting an analytic hierarchy process, and the indexes of the turnover rate, the correlation, the stability of the goods shelf and the matching degree between the goods and the goods space are obtained according to a grade scale questionnaire and are shown in the following table 1:
TABLE 1
Turnover rate Correlation Stability of Degree of matching
Turnover rate 1 3 5 5
Correlation 1/3 1 3 5
Stability of 1/5 1/3 1 3
Degree of matching 1/5 1/5 1/3 1
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.

Claims (8)

1. An intelligent arrangement method for warehoused goods is characterized by comprising the following steps:
step 1: collecting goods information of goods to be warehoused;
step 2: constructing a real-time goods allocation model;
and step 3: solving a goods space distribution model;
and 4, step 4: and according to the goods location distribution model solving result, distributing the goods to be warehoused to the corresponding positions.
2. The intelligent arrangement method for the warehoused goods according to claim 1, wherein the step 1 of collecting the goods information of the goods to be warehoused is as follows: the delivery place, the destination, the cargo weight, the cargo volume, the cargo type, the cargo turnover rate and the cargo property of the cargo to be warehoused are collected.
3. The intelligent arrangement method for the warehoused goods according to claim 1, wherein the step 2 is implemented by constructing a real-time goods allocation model, and the constructed real-time goods allocation model is as follows:
Figure FDA0002574280270000011
wherein f is1For the target optimization function of the cargo turnover rate, f2Optimizing a function for a cargo-dependency objective, f3Optimizing a function for a shelf stability objective, f4Optimizing a function for the matching degree target of the goods and the goods positions;
λ1、λ2、λ3、λ4are all function parameters.
4. The intelligent arrangement method for the warehoused goods according to claim 3, wherein the goods turnover rate target optimization function is as follows:
Figure FDA0002574280270000012
in the formula, vx、vyAnd vzThe moving speed of the stacker in the x, y and z directions is shown; l is the unit cargo space length; xi, yi and zi are coordinates of the goods space i in the x, y and z directions; a. b and c are the total number of the goods positions in the x, y and z directions; and P is the cargo turnover rate.
5. The intelligent arrangement method for the warehoused goods according to claim 3, wherein the goods correlation objective optimization function is as follows:
Figure FDA0002574280270000013
in the formula, xi,yi,ziThe coordinates of the cargo space i in the x direction, the y direction and the z direction are shown; a. b and c are the total number of the goods positions in the x, y and z directions, (a)k，bk，ck) The coordinates of the central cargo space of the kth cargo.
6. The intelligent routing method for warehoused goods according to claim 3, wherein the shelf stability objective optimization function is:
Figure FDA0002574280270000021
wherein L is the length of the unit cargo space, mkIs the weight of cargo of the kth class, xi,yi,ziThe coordinates of a cargo space i in the x, y and z directions are shown, n represents the storage quantity of the k-th cargo, and k belongs to [1, n ]]A, b and c are the total number of the goods positions in the x, y and z directions.
7. The intelligent arrangement method for the warehoused goods according to claim 3, wherein the matching degree target optimization function of the goods and the goods space is as follows:
Figure FDA0002574280270000022
in the formula, V*Representing the degree of compatibility, M, between the volume of goods to be warehoused and the volume of the free goods space i*Representing the degree of compatibility between the weight of goods to be warehoused and the bearing weight of a space goods position i, N1、N2The total number of the goods shelves in the x direction and the total number of the goods shelves in the y direction are obtained;
V*and M*Respectively, as follows:
Figure FDA0002574280270000023
wherein ViRepresenting the carrying volume, M, of the cargo space iiIs the bearing weight of a cargo space i, VjRepresenting the volume of goods to be warehoused, MjIndicating the weight of the goods to be warehoused.
8. The intelligent arrangement method for the warehoused goods according to claim 3, wherein in the step 3, a genetic algorithm is adopted to solve a goods space distribution model, and the inverse number of a goods space distribution model function f is used as a fitness function of the genetic algorithm; the value of the parameter lambda is determined by means of an analytic hierarchy process.
CN202010649220.2A 2020-07-08 2020-07-08 Intelligent arrangement method for stored goods Pending CN111798140A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010649220.2A CN111798140A (en) 2020-07-08 2020-07-08 Intelligent arrangement method for stored goods

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010649220.2A CN111798140A (en) 2020-07-08 2020-07-08 Intelligent arrangement method for stored goods

Publications (1)

Publication Number Publication Date
CN111798140A true CN111798140A (en) 2020-10-20

Family

ID=72810430

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010649220.2A Pending CN111798140A (en) 2020-07-08 2020-07-08 Intelligent arrangement method for stored goods

Country Status (1)

Country Link
CN (1) CN111798140A (en)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112950109A (en) * 2021-01-28 2021-06-11 浙江大学 Complex network-based associated article storage location optimization method
CN113233070A (en) * 2021-06-01 2021-08-10 深圳市信立自动化有限公司 Online stacker control method and system based on Internet of things
CN113780922A (en) * 2021-01-15 2021-12-10 北京京东乾石科技有限公司 Goods flow direction and flow rate determining method and device
CN114417696A (en) * 2021-12-07 2022-04-29 长春工业大学 Automatic stereoscopic warehouse goods space allocation optimization method based on genetic algorithm
CN116090962A (en) * 2023-04-10 2023-05-09 江苏亚东朗升国际物流有限公司 Intelligent warehouse system
CN117575476A (en) * 2024-01-16 2024-02-20 陕西天霖瑞腾网络科技有限公司 Intelligent monitoring management system based on Internet of things
CN117575476B (en) * 2024-01-16 2024-04-26 西安拓达电子科技有限公司 Intelligent monitoring management system based on Internet of things

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN110991754A (en) * 2019-12-09 2020-04-10 西南交通大学 Multi-target goods location optimization method based on variable neighborhood NSGA-II algorithm

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN110991754A (en) * 2019-12-09 2020-04-10 西南交通大学 Multi-target goods location optimization method based on variable neighborhood NSGA-II algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张宇: "仓库管理系统中货位分配与多人作业拣选路径优化", 中国优秀硕士学位论文全文数据库信息科技辑 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113780922A (en) * 2021-01-15 2021-12-10 北京京东乾石科技有限公司 Goods flow direction and flow rate determining method and device
CN112950109A (en) * 2021-01-28 2021-06-11 浙江大学 Complex network-based associated article storage location optimization method
CN112950109B (en) * 2021-01-28 2022-05-17 浙江大学 Complex network-based associated article storage position optimization method
CN113233070A (en) * 2021-06-01 2021-08-10 深圳市信立自动化有限公司 Online stacker control method and system based on Internet of things
CN114417696A (en) * 2021-12-07 2022-04-29 长春工业大学 Automatic stereoscopic warehouse goods space allocation optimization method based on genetic algorithm
CN114417696B (en) * 2021-12-07 2023-05-26 长春工业大学 Automatic stereoscopic warehouse cargo space distribution optimization method based on genetic algorithm
CN116090962A (en) * 2023-04-10 2023-05-09 江苏亚东朗升国际物流有限公司 Intelligent warehouse system
CN116090962B (en) * 2023-04-10 2024-01-16 江苏亚东朗升国际物流有限公司 Intelligent warehouse system
CN117575476A (en) * 2024-01-16 2024-02-20 陕西天霖瑞腾网络科技有限公司 Intelligent monitoring management system based on Internet of things
CN117575476B (en) * 2024-01-16 2024-04-26 西安拓达电子科技有限公司 Intelligent monitoring management system based on Internet of things

Similar Documents

Publication Publication Date Title
CN111798140A (en) Intelligent arrangement method for stored goods
CN108550007B (en) Goods space optimization method and system for automatic stereoscopic warehouse of pharmaceutical enterprise
CN111178606B (en) Automatic warehouse storage position allocation optimization method based on NSGA-II
CN105354641B (en) Goods picking path optimization method and goods picking path optimization device
CN110980082A (en) Automatic stereoscopic warehouse position allocation method
CN105836356B (en) A kind of intensive warehousing system hybrid optimization dispatching method
CN108292381A (en) warehouse shelf space optimization
CN103559396B (en) Based on the automatic dispensary stock's allocation optimization method improving chaos particle cluster algorithm
CN109872107A (en) Picking method for optimizing route and system
CN114417696B (en) Automatic stereoscopic warehouse cargo space distribution optimization method based on genetic algorithm
CN105129308A (en) Intelligent warehouse system
CN113120487B (en) Inventory system and goods storing and taking method
CN109583660B (en) Method for realizing dynamic goods picking strategy
CN111861325A (en) Storage system and storage control method
CN115115256A (en) Medicine warehouse goods space distribution method
CN110751441A (en) Method and device for optimizing storage position in logistics storage system
CN114772132A (en) Flexible goods shelf warehousing system with multiple equipment combinations
CN113343570A (en) Dynamic picking method and system considering relevance of picking order
CN115578039B (en) Warehouse goods space allocation method, electronic equipment and computer storage medium
CN114476481A (en) Intelligent classification logistics storage system
CN111369055B (en) Method and device for determining position of goods container and electronic equipment
CN110619493B (en) AGV layout method and system, electronic device and storage medium
CN112478553A (en) Compact robot automatic storing and taking system cargo box closing method
CN115310917B (en) Warehousing management method
CN113682705B (en) Inventory distribution method, device, server and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination