CN108647917A - Intelligent repository runs simulation optimization method - Google Patents
Intelligent repository runs simulation optimization method Download PDFInfo
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- CN108647917A CN108647917A CN201810355914.8A CN201810355914A CN108647917A CN 108647917 A CN108647917 A CN 108647917A CN 201810355914 A CN201810355914 A CN 201810355914A CN 108647917 A CN108647917 A CN 108647917A
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Abstract
The present invention relates to a kind of intelligent repositories to run simulation optimization method, belong to intelligent repository technical field, it provides a kind of by the way of analogue simulation, simulation run is carried out to intelligent repository in conjunction with actual conditions, it pinpoints the problems in time, the intelligent repository for improving working efficiency runs simulation optimization method, used technical solution is to use to model existing intelligent repository, then in conjunction with actual operating condition, manual intelligent warehouse is further optimized using analogue simulation software, for products storage circulation system, overcome the defect of existing research, realize the intelligent dispatching system with more AGV, allow to close very much navigation mode, break traditional chained job pattern, the efficiency of existing AGV and intelligent storage equipment is set to reach maximal efficiency optimization.
Description
Technical field
The present invention relates to a kind of intelligent repositories to run simulation optimization method, belongs to intelligent repository technical field.
Background technology
Currently, with the continuous development of logistic industry, intelligent storage has been to solve logistic industry cargo storage, Transportation Efficiency
The most critical part of rate.In order to realize that intelligent storage, miscellaneous large-scale intelligent warehouse are constantly set up, from the storage of cargo
Deposit, transport, transporting etc. links all realize it is intelligent.But due to that when intelligent repository designs, can not know intelligent storehouse
The overall operation situation in library, and when considering is transported using RGV transports or AGV, cannot effectively be judged, be caused
Expected effect is often not achieved in the intelligent repository of foundation of spending huge sums, and the working efficiency of intelligent storage equipment is also unable to reach
It maximizes.
Invention content
To solve technical problem of the existing technology, the present invention provides a kind of by the way of analogue simulation, in conjunction with
Actual conditions carry out simulation run to intelligent repository, pinpoint the problems in time, and the intelligent repository operation emulation for improving working efficiency is excellent
Change method.
To achieve the above object, the technical solution adopted in the present invention is intelligent repository operation simulation optimization method, including
Following steps,
A, the collection and arrangement of data carries out network analysis to the flow aspect and functions of the equipments data of intelligent repository；
B, data model is established, and the characteristics of according to the drawing of intelligent repository and intelligent repository each working position, gradually carries out mould
Block is established and connection；First, it is established according to each working region of intelligent repository, then, according to each area of intelligent repository
The characteristics of domain, carries out data input and debugging；Then further each region of intelligent repository is attached and entirety is debugged；
RGV and AGV models finally are added simultaneously again, form the Whole Data Model of intelligent repository；
C, the importing and operation of model imports the Whole Data Model of intelligent repository in DOSIMIS-3 analogue simulation softwares, root
Simulation run is carried out to the Whole Data Model of the intelligent repository of foundation according to the operating parameter of actual design, and is recorded in real time each
The operating status of working region, operational efficiency, waiting list, into operation datas such as shipping Qty.s；
D, by each problem in simulation run, generated in intelligent repository operational process, analyzing processing is carried out one by one, and real
When adjustment simulation work regional model position and into operation datas such as shipping Qty., times；
E, and then in the operation data for being set according to actual conditions RGV and AGV models, including running route and the speed of service and fortune
The practical operation situation of RGV and AGV two kinds of means of transportation of model is analyzed in the initial position of defeated cargo, exports RGV and AGV models
Working condition block diagram and receive order waiting list comparison diagram；
F, the disengaging quantity and haulage time and speed of cargo in the model of working region are finally adjusted, then analyzes RGV and AGV models
The practical operation situation of two kinds of means of transportation exports the working condition block diagram of RGV and AGV models and receives order waiting list
Comparison diagram；
G, by above-mentioned operating analysis, the best of two kinds of means of transportation of optimal operating parameter and RGV and AGV of intelligent repository is exported
Usable condition.
Preferably, in the step b, when being modeled to entire intelligent repository, equipment in each intelligent repository is applied
Brush different colors.
Compared with prior art, the present invention has the following technical effects：The present invention is by the way of analog simulation, to entire
Intelligent repository is modeled, and the actual conditions and operating parameter then in conjunction with intelligent repository are soft by DOSIMIS-3 analogue simulations
Part carries out simulation run, find in the process of running intelligent repository there are the problem of, and settle one by one, then by adjusting intelligence
The practical disengaging car loading in warehouse, analysis is using the working condition block diagram of two kinds of means of transportation of RGV and AGV and reception order etc.
Wait for queue comparison diagram；Which kind of so that it is determined that in the case of, transported using RGV or AGV, working efficiency higher, cost is lower；This
Sample effectively can carry out management and control to intelligent repository, and the operating status in Intelligent Optimal warehouse improves the operational efficiency of intelligent repository.
Specific implementation mode
In order to make technical problems, technical solutions and advantages to be solved be more clearly understood, tie below
Embodiment is closed, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only used to solve
The present invention is released, is not intended to limit the present invention.
Intelligent repository runs simulation optimization method, includes the following steps,
A, the collection and arrangement of data carries out network analysis to the flow aspect and functions of the equipments data of intelligent repository；In order to more
Well all critical functions of entire intelligent repository being simulated to come, the first stage is conceived to the processing and analysis of data direction,
Firstly the need of all data and functions of the equipments are listed, the analysis of a system is done for these data, after analysis, for
It is likely encountered problem in modeling, data are further processed, find and calculate most suitable data and carries out system point
Analysis.In addition, for the drawing of intelligent repository, the module further to optimization system itself is also needed to be matched and matched,
It is that intelligent repository is modeled with most suitable system module（That is, drawing is added in most suitable system module）.Most
Afterwards, it after foundation, also needs to carry out further adaptability debugging and comparison to data and selected module.Make model next
It is more smooth and accurate after step operation.For in terms of data processing, it is necessary first to arrange the data for being possible to use and make be
System analysis, such as：Different furniture items are carried out with the analysis in terms of different assembling flow paths, and obtains its issuable probability etc.
Problem, for example, averagely built-up time be 5 minutes every, it is necessary to found according to the characteristics of furniture itself and be suitble to the statistics of itself
Method, for example, the problems such as assembling deviation, and the problems such as assembling distribution（Normal distribution is uniformly distributed still Erlangian distribution etc.）,
It is required for verifying one by one.
B, data model is established, the characteristics of according to the drawing of intelligent repository and intelligent repository each working position, gradually into
Row module is established and connection；First, it is established according to each working region of intelligent repository, it is then, each according to intelligent repository
The characteristics of a region, carries out data input and debugging；Then further each region of intelligent repository is attached and entirety is adjusted
Examination；RGV and AGV models finally are added simultaneously again, form the Whole Data Model of intelligent repository, and to entire intelligent repository into
When row modeling, the color different to equipment brushing in each intelligent repository；
C, the importing and operation of model imports the Whole Data Model of intelligent repository in DOSIMIS-3 analogue simulation softwares, root
Simulation run is carried out to the Whole Data Model of the intelligent repository of foundation according to the operating parameter of actual design, and is recorded in real time each
The operating status of working region, operational efficiency, waiting list, into operation datas such as shipping Qty.s；
D, by each problem in simulation run, generated in intelligent repository operational process, analyzing processing is carried out one by one, and real
When adjustment simulation work regional model position and into operation datas such as shipping Qty., times；
E, and then in the operation data for being set according to actual conditions RGV and AGV models, including running route and the speed of service and fortune
The practical operation situation of RGV and AGV two kinds of means of transportation of model is analyzed in the initial position of defeated cargo, exports RGV and AGV models
Working condition block diagram and receive order waiting list comparison diagram；Working condition block diagram is able to record using two kinds of transporters
The information such as loading haulage time, standby time, stand-by period, discharge time, loading time and the empty race time of formula；Pass through reception
Order waiting list comparison diagram can reflect that the variation of production efficiency, the variation of output cargo are picked up to what RGV and AGV was transported
Take the influence that the stand-by period brings with dispatching efficiency；
F, the disengaging quantity and haulage time and speed of cargo in the model of working region are finally adjusted, then analyzes RGV and AGV models
The practical operation situation of two kinds of means of transportation exports the working condition block diagram of RGV and AGV models and receives order waiting list
Comparison diagram；By adjusting the quantity of production efficiency and cargo, the working efficiency between two kinds of means of transportation can be really reacted
Gap enables a customer to more actual conditions and is selected.
G, by above-mentioned operating analysis, two kinds of means of transportation of optimal operating parameter and RGV and AGV of intelligent repository are exported
Best usable condition.
Simultaneously by above method, the AGV comparison optimizations of different brand efficiency can also be realized, and can also be directed to pair
For example piler, shelf, production line, processing line, storage facilities transporting equipment carry out global optimization.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement etc., should all wrap within the scope of the present invention made by within refreshing and principle.
Claims (2)
1. intelligent repository runs simulation optimization method, feature is just：Include the following steps,
A, the collection and arrangement of data carries out network analysis to the flow aspect and functions of the equipments data of intelligent repository；
B, data model is established, and the characteristics of according to the drawing of intelligent repository and intelligent repository each working position, gradually carries out mould
Block is established and connection；First, it is established according to each working region of intelligent repository, then, according to each area of intelligent repository
The characteristics of domain, carries out data input and debugging；Then further each region of intelligent repository is attached and entirety is debugged；
RGV and AGV models finally are added simultaneously again, form the Whole Data Model of intelligent repository；
C, the importing and operation of model imports the Whole Data Model of intelligent repository in DOSIMIS-3 analogue simulation softwares, root
Simulation run is carried out to the Whole Data Model of the intelligent repository of foundation according to the operating parameter of actual design, and is recorded in real time each
The operating status of working region, operational efficiency, waiting list, into operation datas such as shipping Qty.s；
D, by each problem in simulation run, generated in intelligent repository operational process, analyzing processing is carried out one by one, and real
When adjustment simulation work regional model position and into operation datas such as shipping Qty., times；
E, and then in the operation data for being set according to actual conditions RGV and AGV models, including running route and the speed of service and fortune
The practical operation situation of RGV and AGV two kinds of means of transportation of model is analyzed in the initial position of defeated cargo, exports RGV and AGV models
Working condition block diagram and receive order waiting list comparison diagram；
F, the disengaging quantity and haulage time and speed of cargo in the model of working region are finally adjusted, then analyzes RGV and AGV models
The practical operation situation of two kinds of means of transportation exports the working condition block diagram of RGV and AGV models and receives order waiting list
Comparison diagram；
G, by above-mentioned operating analysis, the best of two kinds of means of transportation of optimal operating parameter and RGV and AGV of intelligent repository is exported
Usable condition.
2. intelligent repository according to claim 1 runs simulation optimization method, feature is just：In the step b,
When being modeled to entire intelligent repository, the color different to equipment brushing in each intelligent repository.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109656249A (en) * | 2018-12-17 | 2019-04-19 | 厦门大学嘉庚学院 | A kind of more AGV real-time schedulings based on unidirectional track |
| CN109885005A (en) * | 2019-03-14 | 2019-06-14 | 北京达特集成技术有限责任公司 | A kind of flexible chip production logistics caching system |
| CN111142490A (en) * | 2019-12-31 | 2020-05-12 | 江苏苏宁物流有限公司 | AGV intelligent storage simulation method, system and device and computer readable storage medium |
| CN111444599A (en) * | 2020-03-23 | 2020-07-24 | 兰剑智能科技股份有限公司 | AGV project simulation and monitoring method and system |
| CN112084708A (en) * | 2020-09-04 | 2020-12-15 | 西南交通大学 | AGV system optimization configuration method based on response surface and genetic algorithm |
| CN112836775A (en) * | 2021-02-04 | 2021-05-25 | 浙江科技学院 | Different goods warehouse-in and warehouse-out information input system |
| CN112906081A (en) * | 2019-12-04 | 2021-06-04 | 北京京东乾石科技有限公司 | Method and device for planning warehouse layout |
| CN113793080A (en) * | 2020-06-18 | 2021-12-14 | 北京京东乾石科技有限公司 | Real-time simulation method and device for warehouse operation state |
| CN115009755A (en) * | 2022-04-13 | 2022-09-06 | 北京京东振世信息技术有限公司 | Control method and device for storage system equipment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105836356A (en) * | 2016-05-27 | 2016-08-10 | 陕西科技大学 | Hybrid optimal scheduling method for dense warehousing system |
| CN106843124A (en) * | 2016-12-26 | 2017-06-13 | 北京起重运输机械设计研究院 | A kind of Automatic Warehouse three-dimensional real-time monitoring method and system |
-
2018
- 2018-04-19 CN CN201810355914.8A patent/CN108647917A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105836356A (en) * | 2016-05-27 | 2016-08-10 | 陕西科技大学 | Hybrid optimal scheduling method for dense warehousing system |
| CN106843124A (en) * | 2016-12-26 | 2017-06-13 | 北京起重运输机械设计研究院 | A kind of Automatic Warehouse three-dimensional real-time monitoring method and system |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109656249A (en) * | 2018-12-17 | 2019-04-19 | 厦门大学嘉庚学院 | A kind of more AGV real-time schedulings based on unidirectional track |
| CN109885005A (en) * | 2019-03-14 | 2019-06-14 | 北京达特集成技术有限责任公司 | A kind of flexible chip production logistics caching system |
| CN109885005B (en) * | 2019-03-14 | 2020-07-14 | 北京达特集成技术有限责任公司 | Flexible chip production logistics cache system |
| CN112906081A (en) * | 2019-12-04 | 2021-06-04 | 北京京东乾石科技有限公司 | Method and device for planning warehouse layout |
| CN111142490A (en) * | 2019-12-31 | 2020-05-12 | 江苏苏宁物流有限公司 | AGV intelligent storage simulation method, system and device and computer readable storage medium |
| CN111444599A (en) * | 2020-03-23 | 2020-07-24 | 兰剑智能科技股份有限公司 | AGV project simulation and monitoring method and system |
| CN113793080A (en) * | 2020-06-18 | 2021-12-14 | 北京京东乾石科技有限公司 | Real-time simulation method and device for warehouse operation state |
| CN112084708B (en) * | 2020-09-04 | 2022-08-19 | 西南交通大学 | AGV system optimization configuration method based on response surface and genetic algorithm |
| CN112084708A (en) * | 2020-09-04 | 2020-12-15 | 西南交通大学 | AGV system optimization configuration method based on response surface and genetic algorithm |
| CN112836775A (en) * | 2021-02-04 | 2021-05-25 | 浙江科技学院 | Different goods warehouse-in and warehouse-out information input system |
| CN112836775B (en) * | 2021-02-04 | 2022-07-29 | 浙江科技学院 | Different goods warehouse-in and warehouse-out information input system |
| CN115009755A (en) * | 2022-04-13 | 2022-09-06 | 北京京东振世信息技术有限公司 | Control method and device for storage system equipment |
| CN115009755B (en) * | 2022-04-13 | 2024-04-05 | 北京京东振世信息技术有限公司 | Control method and device for warehouse system equipment |
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