CN108357848B - Modeling optimization method based on Multilayer shuttle car automated storage and retrieval system - Google Patents
Modeling optimization method based on Multilayer shuttle car automated storage and retrieval system Download PDFInfo
- Publication number
- CN108357848B CN108357848B CN201810213876.2A CN201810213876A CN108357848B CN 108357848 B CN108357848 B CN 108357848B CN 201810213876 A CN201810213876 A CN 201810213876A CN 108357848 B CN108357848 B CN 108357848B
- Authority
- CN
- China
- Prior art keywords
- elevator
- task
- layer
- outbound
- time
- 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.)
- Expired - Fee Related
Links
Classifications
-
- 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
-
- 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
- B65G1/0478—Storage devices mechanical for matrix-arrangements
-
- 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
- B65G1/0492—Storage devices mechanical with cars adapted to travel in storage aisles
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Warehouses Or Storage Devices (AREA)
Abstract
The invention discloses a kind of modeling optimization methods based on Multilayer shuttle car automated storage and retrieval system, including：Determine the characteristics of motion of elevator and shuttle and the respective services time of elevator and shuttle operation process；Extract respectively the number of plies of Multilayer shuttle car automated storage and retrieval system, the depth in tunnel, the height of single layer shelf, the width of single goods lattice, the velocity and acceleration of elevator, shuttle velocity and acceleration, single picks and places ETCD estimated time of commencing discharging and outbound task details parameter, establishes the mathematical model of Multilayer shuttle car automated storage and retrieval system；Building is to obtain shortest picking total time as the mixed-integer programming model of target；Mixed-integer programming model is solved using GUROBI linear programming for solution device, obtains the picking sequence of shortest total picking time and system optimal.The present invention can quickly estimate the performance of system under various shelf and device configuration, provide decision support for the careful design and raising capacity utilization of system, saving operating cost.
Description
Technical field
The present invention relates to automatic stereowarehouse technical fields, more particularly to one kind to be based on the automatic stored system of Multilayer shuttle car
The modeling optimization method of system.
Background technique
In traditional automatic stereowarehouse, piler is responsible for the access of cargo, therefore to conventional automated three-dimensional storehouse
The modeling in library need to only analyze piler single equipment.
As order is to small lot, multiple batches of development, Multilayer shuttle car automated storage and retrieval system gradually comes into operation.With tradition
Automatic stereowarehouse compare, Multilayer shuttle car automated storage and retrieval system picking, working efficiency with elevator and shuttle
It greatly promotes.As shown in Figure 1, Multilayer shuttle car automated storage and retrieval system at each access adit, is equipped with an elevator for bearing
Blame the movement of the vertical direction of the tunnel cargo, and by goods handling to I/O platform；It is negative that every layer of shelf are designed with a shuttle
Blame the movement of this layer of cargo horizontal direction.Obviously, the modeling method of traditional automatic stereo storage is set just for piler one kind
It is standby, be not suitable for the mode of this complexity of Multilayer shuttle car system.Before this, existing many for Multilayer shuttle car automated storage and retrieval system
Modeling method, but being all based on queueing theory scheduling theory establishes approximate appraising model greatly, models the result levels of precision of acquisition also
Have to be hoisted.
Therefore, it is necessary to which the modeling method of Multilayer shuttle car automated storage and retrieval system outbound task is furtherd investigate and is changed
Into finding the higher modeling method of levels of precision.How the modeling method of in the prior art conventional automated stereo garage is solved not
How the problem of modeling of outbound task suitable for Multilayer shuttle car automated storage and retrieval system solves multilayer shuttle in the prior art
How the not high problem of the modeling method accuracy of vehicle automated storage and retrieval system, assess Multilayer shuttle car automated storage and retrieval system performance
Problem becomes and needs technical problems to be solved at this stage.
Summary of the invention
The purpose of the present invention is to solve the above-mentioned problems, provides a kind of based on Multilayer shuttle car automated storage and retrieval system
Modeling optimization method, this method by establish mixed-integer programming model accurately describe based on Multilayer shuttle car it is automatic stored
The practical outbound task process of system rapidly calculates automatic stored based on Multilayer shuttle car under various shelf and device configuration
The performance of system, for based on Multilayer shuttle car automated storage and retrieval system careful design and improve capacity utilization, save operation at
This offer decision support.
To achieve the above object, concrete scheme of the invention is as follows：
The invention discloses a kind of modeling optimization methods based on Multilayer shuttle car automated storage and retrieval system, including following step
Suddenly：
(1) determine elevator and shuttle the characteristics of motion and elevator and shuttle operation process respective services when
Between；Respectively extract the number of plies of Multilayer shuttle car automated storage and retrieval system, the depth in tunnel, the height of single layer shelf, single goods lattice
Width, the velocity and acceleration of elevator, shuttle velocity and acceleration, single pick and place ETCD estimated time of commencing discharging and outbound task it is detailed
Information parameter establishes the mathematical model of Multilayer shuttle car automated storage and retrieval system；
(2) building is to obtain shortest picking total time as the mixed-integer programming model of target；
(3) mixed-integer programming model is solved using GUROBI linear programming for solution device, acquisition is shortest always to be taken
The value condition of ETCD estimated time of commencing discharging and each state variable, to judge the picking sequence of system optimal.
Further, the characteristics of motion of the determining elevator and shuttle, specially：
Assuming that current elevator determines elevator from I/O platform to this layer in xth layer according to the height of single layer shelf
Travel distance；
The acceleration and maximum speed for considering elevator, determine the travel time of elevator；
Assuming that current shuttle is in tunnel, first according to the width of single goods lattice determines that it reaches q-th of depth location
Travel distance；
The acceleration and maximum speed for considering shuttle, determine the travel time of shuttle.
Further, the respective services time of the determining elevator and shuttle operation process, specially：
Cargo is transported the service time to I/O platform and release by elevator from i-th layer；
Elevator discharges cargo from i-th layer of transport to I/O platform, is subsequently returning to the service time of jth layer picking；
Shuttle takes out the service time for being located at the outbound task of i-th layer of q-th of depth location；
Elevator is from initial conditions, i.e., to i-th layer of service time at I/O platform.
Further, the building is specific as the mixed-integer programming model of target to obtain shortest picking total time
For：
Wherein, tMIt is at the beginning of elevator executes the last one outbound task, N is the number collection of all shelf numbers of plies
It closing, M is the outbound task sum of system,Indicate elevator by cargo from when i-th layer of transport to the service of I/O platform and release
Between, yMiOutbound task that whether the last one elevator task is i-th layer is identified (if the last one elevator task is i-th layer
Outbound task, the value of the variable is 1；Otherwise, 0) value of the variable is
Further, the uniqueness foundation to guarantee the corresponding outbound layer of each outbound task, picking order, goods yard position
The constraint condition of mixed-integer programming model, specially：
1) the outbound task sum on i-th layer of shelf is equal to the elevator total task number of i-th layer of shelf；And guarantee any
The uniqueness of outbound layer where one elevator task；
It 2) is that the number of certain layer of n-th of outbound task is equal to the outbound task number of all shelf in all elevator tasks
Number greater than n；And guarantee any one elevator task in the uniqueness of its corresponding outbound layer outbound order；
3) guarantee any one outbound task in the uniqueness of its corresponding outbound layer outbound order；
And guarantee any one outbound task in the uniqueness of its corresponding outbound layer goods yard depth.
Further, the constraint between each elevator task, tool are determined according to the connection between each elevator task
Body is：
First elevator task reaches the time of any one layer of shelf greater than elevator at the time of beginning；
Also, the time difference that the continuous elevator task of any two is separated by, which is greater than between round-trip two layers of the shelf of elevator, to be needed
The time wanted.
Further, according in every layer of shelf to the connection between outbound task determine in every layer of shelf to outbound task it
Between constraint, specially：
First outbound task of any layer is later than goods yard where shuttle reaches it at the time of waiting elevator scheduling complete
The time required to being operated at picking；
Also, the time difference that the continuous outbound task of two of any layer completes the moment is greater than the when that shuttle walking needs
Between.
It further, is t at the beginning of elevator being executed m-th of outbound taskm, shuttle is completed i-th layer
N-th of outbound task is denoted as r at the time of waiting elevator responsein, according to tmAnd rinInner link, establish and constrain part such as
Under：
At the time of first elevator task is later than first cargo to outbound and is removed at the time of beginning；
And, it is assumed that m-th of elevator task is i-th layer of n-th of outbound task, what m-th of elevator task started
At the time of n-th of task that moment is later than i-th layer is completed；
And, it is assumed that m-th of elevator task is i-th layer of (n-1)th outbound task, and i-th layer of n-th of task is completed
At the time of be later than the sum of time and the shuttle travel time that m-th of elevator task starts；
And, it is assumed that m-th of elevator task is i-th layer of n-th of outbound task, and the m-1 elevator task is jth
The outbound task of layer, i-th layer of n-th of task are later than the time and promotion that the m-1 elevator task starts at the time of completion
The sum of machine travel time.
It further, is the nonnegativity for guaranteeing mixed-integer programming model, establishing a constraint part is specially：
Elevator is not less than zero at the beginning of executing m-th of outbound task；
Also, shuttle completes i-th layer of n-th of outbound task, is not less than zero at the time of waiting elevator response.
Further, the picking sequence of the acquisition system optimal is specially：
By the value for each state variable that solver calculates, picking sequence in mark system, specific picking is suitable
The method that sequence determines：
And if only if ymi=1, zmn=1, xinqIndicate that m-th of cargo being removed is located at i-th layer of q-th of depth when=1
It is n-th of cargo being removed on this layer of shelf on position, i.e. m-th of Delivery is i-th layer of n-th of Delivery, together
When the Delivery on q-th of depth location；
According to the above rule, every outbound task uniquely corresponding outbound layer, picking order, goods yard position are successively judged
Information further obtains the picking sequence of system optimal；
Wherein, ymiIdentify m-th of elevator task whether the outbound task for being i-th layer, zmnM-th of elevator is identified to appoint
Business whether be a certain layer n-th of outbound task, xinqWhether n-th of Delivery of i-th layer of mark be in q-th of depth location
On.
Beneficial effects of the present invention：
The integer programming model that the present invention establishes can the accurate practical outbound task process of the simulation system, can be quick
The performance for estimating system under various shelf and device configuration, be system careful design and improve capacity utilization, save
Operating cost provides decision support.
Modeling method provided by the invention, which solves the result obtained, can accurately position the sequence of each outbound task,
Compared with total picking time required for random picking sequence, the total duration of picking is greatly reduced.
Inner link between outbound task is abstracted as accurate mathematics part by the present invention, establishes Multilayer shuttle car system
Outbound task integer programming model and its derivation algorithm, overcoming the modeling in traditional library, can not to adapt to Multilayer shuttle car automatic
The characteristics of warehousing system multiple servers, the performance of system is fast and accurately calculated using tool.
Using model of the present invention and derivation algorithm, best shuttle, elevator configuration combination can be quickly and effectively found out,
System operation cost is not only saved, also can provide theoretical direction for logistic storage system designer.
Detailed description of the invention
Fig. 1 is Multilayer shuttle car automated storage and retrieval system schematic diagram；
Fig. 2 is outbound task flow chart.
Specific embodiment：
The present invention is described in detail with reference to the accompanying drawing：
In the outbound task of single, task requests the response of respective layer shuttle first.According to system call, shuttle
It is first moved horizontally at the outbound goods yard of system distribution, is taken out cargo using pallet fork, then shuttle runs to the head of this layer
Column, request the response of the tunnel elevator.Equally, elevator goes to respective layer and shuttle to complete cargo according to system call
Handover, outbound task process is as shown in Figure 2.
Based on this, the invention discloses a kind of modeling optimization methods based on Multilayer shuttle car automated storage and retrieval system, including
Following steps：
(1) to Multilayer shuttle car automated storage and retrieval system model, extract the number of plies, columns, elevator velocity and acceleration, wear
The velocity and acceleration of shuttle car picks and places the parameters such as ETCD estimated time of commencing discharging, and is abstracted as every input in mathematical model.
The basic input that (1-1) analysis model needs；It specifically includes：
The number of plies N of Multilayer shuttle car automated storage and retrieval system, the depth C in tunnel, single layer shelf height Dh, single goods lattice
Width Dw, shuttle maximum speed Vw, shuttle acceleration aw, elevator maximum speed Vh, elevator acceleration ah, shuttle list
Secondary pick-and-place ETCD estimated time of commencing discharging tw, elevator single pick and place ETCD estimated time of commencing discharging th, solve scale constant T, include all outbound task details
The dictionary Q of (layer, place depth location where task).
(1-2) analyzes the characteristics of motion of elevator and shuttle, specially：
Assuming that current elevator is in xth layer, travel distance of the elevator from I/O platform to this layer is
H=(x-1) × Dh
Consider that the acceleration and maximum speed of elevator, the travel time of elevator are：
Similarly, it is assumed that current shuttle tunnel first, the travel distance for reaching q-th of depth location is
W=q × Dw
Consider that the acceleration and maximum speed of shuttle, the travel time of shuttle are：
(1-3) calculates the respective services time of elevator and shuttle operation process, specially：
Cargo is transported the service time to I/O platform and release by calculating elevator from i-th layer
It calculates elevator and discharges cargo from i-th layer of transport to I/O platform, be subsequently returning to the service time of jth layer picking
It calculates shuttle and takes out the service time for being located at the outbound task of i-th layer of q-th of depth location
Elevator is calculated from initial conditions, i.e., to i-th layer of service time at I/O platform
(2) to the dynamic picking process model building of Multilayer shuttle car automated storage and retrieval system, to obtain shortest picking total time
For target, mixed-integer programming model is established.
The objective function of (2-1) model：
Wherein, tmIt is at the beginning of elevator executes m-th of outbound task, N is the number set of all shelf numbers of plies.
When objective function obtains minimum value, total picking time of Multilayer shuttle car automated storage and retrieval system is most short.
(2-2) be guarantee the corresponding outbound layer of each outbound task, picking order, goods yard position uniqueness establish about
Beam, specially：
∑i∈Nymi=1 i ∈ N, m ∈ 1,2 ..., M }；
Wherein, SiIdentify the outbound task quantity on i-th layer of shelf, variable ymiIdentify m-th of elevator task whether be
I-th layer of outbound task, ymiValue rule be：
Wherein, SmaxIndicate the maximum value of single layer shelf outbound task, ZnThe outbound task number for identifying all shelf is greater than n
A number, variable zmnIdentify m-th of elevator task whether be a certain layer n-th of outbound task, zmnValue rule
For：
Wherein, QiIndicate the set of the depth number composition of outbound task on i-th layer of shelf, variable xinqI-th layer of mark
Whether n-th of outbound task be on q depth location, xinqValue rule be：
(2-3) analyzes the connection between each elevator task, and it is as follows to establish constraint part：
First elevator task reaches the time of any one layer of shelf greater than elevator at the time of beginning, i.e.,
Assuming that the m-1 elevator task is located at i-th layer of shelf, m-th of elevator task is located at jth layer shelf, arbitrarily
The time difference that two continuous elevator tasks are separated by must be greater than the time needed between round-trip two layers of the shelf of elevator, i.e.,
(2-4) is analyzed in every layer of shelf to the connection between outbound task, and it is as follows to establish constraint part：
First outbound task of any layer must be later than shuttle at the time of waiting elevator scheduling and reach goods yard where it
The time required to completing picking operation, i.e.,
The time difference that the continuous outbound task of two of any layer completes the moment must be greater than shuttle and walkThe time wanted, i.e.,
Shuttle is completed the outbound task on i-th layer of n-th of depth location by (2-5), at the time of waiting elevator response
It is denoted as rin, analyze tmAnd rinInner link, establish that constrain part as follows：
At the time of first elevator task is later than first cargo to outbound and is removed at the time of beginning, i.e.,
t1≥ri1-T(3-y1,i-zm1-xi1q)；m∈{1,2,…,M},q∈{1,2,…,C}；
Assuming that m-th of elevator task is i-th layer of n-th of outbound task, evening at the time of m-th of elevator task starts
At the time of i-th layer of n-th of task is completed, i.e.,
tm≥rin-T(2-ymi-zmn), i ∈ N, n ∈ { 1,2 ..., Smax},m∈{1,2,…,M}；
Assuming that m-th of elevator task is i-th layer of (n-1)th outbound task, then i-th layer of n-th of task is completed
At the time of be later than the sum of time and the shuttle travel time that m-th of elevator task starts, i.e.,
i∈N,n∈{2,…,Smax},m∈{2,…,M}；
Assuming that m-th of elevator task is i-th layer of n-th of outbound task, the m-1 elevator task is jth layer
Outbound task, then i-th layer of n-th of task is later than the time and promotion that the m-1 elevator task starts at the time of completion
The sum of machine travel time, i.e.,
i,j∈N,n∈{1,…,Smax},m∈{2,…,M}。
(2-6) is the nonnegativity for guaranteeing model, adds other necessary Simple constraints, tm≥0,rin≥0。
(3) it is programmed using Python, designed model is generated into configuration using GUROBI linear programming for solution device
File solves the picking sequence of shortest total picking time and system optimal, and thus further statistics obtains various configuration items
Calculated result under part obtains the allocation optimum of Multilayer shuttle car automated storage and retrieval system convenient for analysis.
Obtain system optimal picking sequence be specially：
By the value for each state variable that solver calculates, picking sequence in mark system, specific picking is suitable
The method that sequence determines：
And if only if ymi=1, zmn=1, xinqIndicate that m-th of cargo being removed is located at i-th layer of q-th of depth when=1
It is n-th of cargo being removed on this layer of shelf on position, i.e. m-th of Delivery is i-th layer of n-th of Delivery, together
When the Delivery on q-th of depth location；
According to the above rule, every outbound task uniquely corresponding outbound layer, picking order, goods yard position are successively judged
Information further obtains the picking sequence of system optimal；
Wherein, ymiIdentify m-th of elevator task whether the outbound task for being i-th layer, zmnM-th of elevator is identified to appoint
Business whether be a certain layer n-th of outbound task, xinqWhether n-th of Delivery of i-th layer of mark be in q-th of depth location
On.
In order to verify the validity of model, the simulated scenario such as table 1 of 6 kinds of Multilayer shuttle car systems is set, using above-mentioned
The quick calculated result of model exact method.Under every group of scene, total picking time under 4 kinds of random picking sequences is recorded respectively,
It is compared with the calculated result of model, data record obtained is as shown in table 2.
1 scene setting table of table
The analysis of 2 the model calculation of table
Obviously, substantially reduced using model calculated total picking time than total picking time under random picking sequence,
The calculated picking sequence of model is optimal picking sequence.
Table 3 is the most common basic configuration of Multilayer shuttle car automated storage and retrieval system, and the above results are under the configuration condition
It is calculated.
3 Multilayer shuttle car automated storage and retrieval system basic configuration of table
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (9)
1. the modeling optimization method based on Multilayer shuttle car automated storage and retrieval system, which is characterized in that include the following steps：
(1) characteristics of motion of elevator and shuttle and the respective services time of elevator and shuttle operation process are determined；
The number of plies of Multilayer shuttle car automated storage and retrieval system, the width of the depth in tunnel, the height of single layer shelf, single goods lattice are extracted respectively
The velocity and acceleration of degree, the velocity and acceleration of elevator, shuttle, single picks and places ETCD estimated time of commencing discharging and outbound task is believed in detail
Parameter is ceased, the mathematical model of Multilayer shuttle car automated storage and retrieval system is established；
(2) building is to obtain shortest picking total time as the mixed-integer programming model of target；
The mixed-integer programming model is specially：
Wherein, tMIt is at the beginning of elevator executes the last one outbound task, N is the number set of all shelf numbers of plies, M
It is total for the outbound task of system,Indicate the service time that elevator transports cargo to I/O platform and release from i-th layer,
yMiIdentify the last one elevator task whether the outbound task for being i-th layer, if the last one elevator task be i-th layer go out
Library task, yMiValue be 1；Otherwise, yMiValue be 0；
(3) mixed-integer programming model is solved using GUROBI linear programming for solution device, when obtaining shortest total picking
Between and system optimal picking sequence.
2. as described in claim 1 based on the modeling optimization method of Multilayer shuttle car automated storage and retrieval system, which is characterized in that institute
The characteristics of motion of determining elevator and shuttle is stated, specially：
Assuming that current elevator determines walking of the elevator from I/O platform to this layer according to the height of single layer shelf in xth layer
Distance；
The acceleration and maximum speed for considering elevator, determine the travel time of elevator；
Assuming that current shuttle is in tunnel, first according to the width of single goods lattice determines that it reaches the walking of q-th of depth location
Distance；
The acceleration and maximum speed for considering shuttle, determine the travel time of shuttle.
3. as described in claim 1 based on the modeling optimization method of Multilayer shuttle car automated storage and retrieval system, which is characterized in that institute
The respective services time of determining elevator and shuttle operation process is stated, specially：
Cargo is transported the service time to I/O platform and release by elevator from i-th layer；
Elevator discharges cargo from i-th layer of transport to I/O platform, is subsequently returning to the service time of jth layer picking；
Shuttle takes out the service time for being located at the outbound task of i-th layer of q-th of depth location；
Elevator is from initial conditions, i.e., to i-th layer of service time at I/O platform.
4. as described in claim 1 based on the modeling optimization method of Multilayer shuttle car automated storage and retrieval system, which is characterized in that be
Guarantee the corresponding outbound layer of each outbound task, picking order, goods yard position uniqueness, establish mixed-integer programming model
Constraint condition, specially：
1) the outbound task sum on i-th layer of shelf is equal to the elevator total task number of i-th layer of shelf；And guarantee any one
The uniqueness of outbound layer where elevator task；
2) be in all elevator tasks certain layer of n-th of outbound task number be equal to all shelf outbound task number be greater than
N numbers；And guarantee any one elevator task in the uniqueness of its corresponding outbound layer outbound order；
3) guarantee any one outbound task in the uniqueness of its corresponding outbound layer outbound order；
And guarantee any one outbound task in the uniqueness of its corresponding outbound layer goods yard depth.
5. as described in claim 1 based on the modeling optimization method of Multilayer shuttle car automated storage and retrieval system, which is characterized in that root
The constraint between each elevator task is determined according to the connection between each elevator task, specially：
First elevator task reaches the time of any one layer of shelf greater than elevator at the time of beginning；
Also, the time difference that the continuous elevator task of any two is separated by, which is greater than between elevator round-trip two layers of shelf, to be needed
Time.
6. as described in claim 1 based on the modeling optimization method of Multilayer shuttle car automated storage and retrieval system, which is characterized in that root
It is determined in every layer of shelf to the constraint between outbound task, specially according in every layer of shelf to the connection between outbound task：
First outbound task of any layer is later than goods yard where shuttle reaches it and completes to take at the time of waiting elevator scheduling
The time required to goods operation；
Also, the time difference that the continuous outbound task of two of any layer completes the moment is greater than the time that shuttle walking needs.
7. as described in claim 1 based on the modeling optimization method of Multilayer shuttle car automated storage and retrieval system, which is characterized in that will
Elevator is t at the beginning of executing m-th of outbound taskm, shuttle is completed into i-th layer of n-th of outbound task, waiting mentions
R is denoted as at the time of the response of the machine of literin, according to tmAnd rinInner link, establish that constrain part as follows：
At the time of first elevator task is later than first cargo to outbound and is removed at the time of beginning；
And, it is assumed that m-th of elevator task is i-th layer of n-th of outbound task, at the time of m-th of elevator task starts
At the time of being later than i-th layer of n-th of task completion；
And, it is assumed that m-th of elevator task is i-th layer of (n-1)th outbound task, i-th layer of n-th of task complete when
Quarter is later than the sum of time and the shuttle travel time that m-th of elevator task starts；
And, it is assumed that m-th of elevator task is i-th layer of n-th of outbound task, and the m-1 elevator task is jth layer
Outbound task, i-th layer of n-th of task are later than the time and elevator row that the m-1 elevator task starts at the time of completion
Walk the sum of time.
8. as described in claim 1 based on the modeling optimization method of Multilayer shuttle car automated storage and retrieval system, which is characterized in that be
Guarantee the nonnegativity of mixed-integer programming model, establishing a constraint part is specially：
Elevator is not less than zero at the beginning of executing m-th of outbound task；
Also, shuttle completes the outbound task on i-th layer of q-th of depth location, is not less than at the time of waiting elevator response
Zero.
9. as described in claim 1 based on the modeling optimization method of Multilayer shuttle car automated storage and retrieval system, which is characterized in that institute
The acquisition system optimal stated picking sequence be specially：
By the value for each state variable that solver calculates, picking sequence in mark system, specific picking sequence is sentenced
Fixed method：
And if only if ymi=1, zmn=1, xinqIndicate that m-th of cargo being removed is located at i-th layer of q-th of depth location when=1
On, it is n-th of cargo being removed on this layer of shelf, i.e. m-th of Delivery is i-th layer of n-th of Delivery, simultaneously should
Delivery is on q-th of depth location；
According to the above rule, every outbound task uniquely corresponding outbound layer, picking order, goods yard position letter are successively judged
Breath further obtains the picking sequence of system optimal；
Wherein, ymiIdentify m-th of elevator task whether the outbound task for being i-th layer, zmnWhether identify m-th of elevator task
For n-th of outbound task of a certain layer, xinqWhether n-th of Delivery of i-th layer of mark be on q-th of depth location.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810213876.2A CN108357848B (en) | 2018-03-15 | 2018-03-15 | Modeling optimization method based on Multilayer shuttle car automated storage and retrieval system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810213876.2A CN108357848B (en) | 2018-03-15 | 2018-03-15 | Modeling optimization method based on Multilayer shuttle car automated storage and retrieval system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108357848A CN108357848A (en) | 2018-08-03 |
| CN108357848B true CN108357848B (en) | 2018-11-30 |
Family
ID=63000282
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810213876.2A Expired - Fee Related CN108357848B (en) | 2018-03-15 | 2018-03-15 | Modeling optimization method based on Multilayer shuttle car automated storage and retrieval system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108357848B (en) |
Families Citing this family (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7084257B2 (en) * | 2018-08-30 | 2022-06-14 | 株式会社日立製作所 | Picking support system and support method |
| CN109081030B (en) * | 2018-09-28 | 2021-04-13 | 陕西科技大学 | Configuration optimization method of primary and secondary shuttle type intensive warehousing system |
| CN109230142B (en) * | 2018-10-22 | 2020-12-25 | 陕西科技大学 | Scheduling path optimization method for composite operation of dense storage system |
| CN111275373B (en) * | 2018-12-05 | 2024-05-21 | 北京京东乾石科技有限公司 | Efficiency acquisition method, system and equipment of elevator and readable storage medium |
| CN111612202B (en) * | 2019-02-26 | 2024-04-19 | 北京京东乾石科技有限公司 | Method and system for transporting articles |
| CN109677830B (en) * | 2019-02-28 | 2021-04-16 | 陕西科技大学 | Resource allocation optimization method for four-way shuttle type dense warehousing system |
| CN109896217B (en) * | 2019-04-19 | 2024-05-03 | 广东豪坤供储智能装备科技有限公司 | Automatic logistics device of variable-speed double-channel steering shuttle |
| CN110422526B (en) * | 2019-08-13 | 2022-01-18 | 上海快仓自动化科技有限公司 | Warehousing system and logistics control method |
| CN111792249B (en) * | 2019-09-17 | 2022-08-12 | 北京京东乾石科技有限公司 | Warehouse management method, device, system and computer readable storage medium |
| CN111126770B (en) * | 2019-11-26 | 2022-06-21 | 浙江工业大学 | Task scheduling method of cross-layer shuttle storage system |
| CN111290382B (en) * | 2020-02-12 | 2022-11-29 | 上海驿放智能装备有限公司 | Multilayer shuttle warehouse equipment scheduling system |
| CN111573095B (en) * | 2020-04-16 | 2021-10-15 | 龚业明 | Bypassing collision avoidance optimization method of storage system based on self-climbing robot |
| CN111942795B (en) * | 2020-08-17 | 2021-11-12 | 东华大学 | Operation efficiency evaluation method for four-way vehicle dense warehousing system |
| CN112093359B (en) * | 2020-10-16 | 2021-11-26 | 永山窍档案管理有限公司 | Method for improving full-automatic retrieval efficiency of high-density three-dimensional library |
| CN113159611A (en) * | 2021-05-07 | 2021-07-23 | 湖北普罗劳格科技股份有限公司 | Elevator dispatching method, device and equipment based on prediction model and storage medium |
| CN113420951B (en) * | 2021-05-17 | 2023-07-07 | 西安电子科技大学 | Performance evaluation system of double-deep multi-layer intelligent warehouse |
| CN113200278B (en) * | 2021-05-28 | 2022-11-01 | 深圳市海柔创新科技有限公司 | Cargo transportation method, device, equipment, warehousing system and storage medium |
| CN113320872B (en) * | 2021-06-08 | 2022-12-06 | 深圳市海柔创新科技有限公司 | Article processing method, device, equipment, system and storage medium |
| CN113525990B (en) * | 2021-07-30 | 2023-03-24 | 广州佳帆计算机有限公司 | Method and device for warehousing commodities based on shuttle path |
| CN114418461B (en) * | 2022-03-28 | 2022-11-15 | 浙江凯乐士科技集团股份有限公司 | Task allocation method and device for shuttle vehicle and electronic equipment |
| CN114648278B (en) * | 2022-05-13 | 2022-08-05 | 泗水县瑞安建材有限公司 | Building material warehouse-in and warehouse-out management method based on intelligent warehousing |
| CN115432343B (en) * | 2022-07-26 | 2023-06-16 | 湖北凯乐仕通达科技有限公司 | Goods arrival person picking system and picking method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103971222B (en) * | 2014-05-27 | 2018-06-05 | 山东大学 | The goods yard distribution method of Multilayer shuttle car automated storage and retrieval system |
| CN105022862B (en) * | 2015-06-16 | 2018-06-12 | 北京科技大学 | A kind of automatic vehicle access system modular emulation and optimization system |
| CN107416400B (en) * | 2017-07-04 | 2018-03-23 | 山东大学 | Model and its optimization method based on the intensive automatic storage system of cross-layer shuttle |
-
2018
- 2018-03-15 CN CN201810213876.2A patent/CN108357848B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN108357848A (en) | 2018-08-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108357848B (en) | Modeling optimization method based on Multilayer shuttle car automated storage and retrieval system | |
| CN110322172B (en) | Inventory scheduling method, inventory scheduling device and computer-readable storage medium | |
| CN110443093B (en) | Intelligent-oriented infrared digital panoramic system and warehouse management method thereof | |
| Sari et al. | Travel-time models for flow-rack automated storage and retrieval systems | |
| EP3872009A1 (en) | Inventory region integrating goods preparation and picking, and inventory management system and method | |
| Lerher et al. | Simulation analysis of mini-load multi-shuttle automated storage and retrieval systems | |
| CN116502785B (en) | Warehouse logistics intelligent management method, device, equipment and storage medium | |
| CN110766194A (en) | Order processing method and device, warehousing system, computer equipment and storage medium | |
| CN111401801B (en) | Processing system and method for determining decentralized storage of target object warehouse | |
| CN112536794A (en) | Machine learning method, forklift control method and machine learning device | |
| CN109583660B (en) | Method for realizing dynamic goods picking strategy | |
| CN110751441A (en) | Method and device for optimizing storage position in logistics storage system | |
| CN105858044A (en) | Optimal dispatching method for warehousing systems combining rail guided vehicles and lifts | |
| CN116702454A (en) | Modeling method and device for logistics layout, electronic equipment and storage medium | |
| CN111747008B (en) | Ex-warehouse positioning method, device and system | |
| CN111709565B (en) | Energy efficiency optimization method and system based on multi-layer shuttle system | |
| Ouhoud et al. | Comparative study between continuous models and discrete models for single cycle time of a multi-aisles automated storage and retrieval system with class based storage | |
| CN118258406B (en) | Automatic guided vehicle navigation method and device based on visual language model | |
| CN113650997B (en) | Article warehouse-out positioning method and device | |
| CN113077070B (en) | Dynamic ABC classification storage strategy optimization method and realization system based on attribute prediction | |
| CN113435805B (en) | Method, device, equipment and storage medium for determining article storage information | |
| CN116664053B (en) | Commodity inventory management method | |
| CN113327284B (en) | Image recognition method and device, electronic equipment and storage medium | |
| CN113371383B (en) | Goods shelf scheduling method, device, equipment, warehousing system and storage medium | |
| CN111160831B (en) | Task generation method and device for intensive warehouse and electronic equipment |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20181130 Termination date: 20210315 |