CN112163819B - Logistics outfield simulation model construction method and application method thereof - Google Patents
Logistics outfield simulation model construction method and application method thereof Download PDFInfo
- Publication number
- CN112163819B CN112163819B CN202011138532.3A CN202011138532A CN112163819B CN 112163819 B CN112163819 B CN 112163819B CN 202011138532 A CN202011138532 A CN 202011138532A CN 112163819 B CN112163819 B CN 112163819B
- Authority
- CN
- China
- Prior art keywords
- outfield
- model
- data
- logistics
- parameters
- 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.)
- Active
Links
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
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- 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
- G06Q10/067—Enterprise or organisation modelling
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for constructing a logistics outfield simulation model, which comprises the following steps: obtaining basic data of a logistics outfield to construct a sample feature vector, wherein the basic data at least comprises equipment parameters, personnel configuration parameters and site parameters; training a pre-built nonlinear model based on the basic data and classification labels of the cargo flow data corresponding to the sample feature vectors to obtain a target model and an optimal I/O of the target model; acquiring a construction scheme of a logistics outfield according to the target model and the optimal I/O of the target model; according to the invention, the system modeling virtual configuration and the site configuration are mapped one by one, a simulation model is established by adopting a nonlinear model to fit the site nonlinear related situation, the reaction site situation is accurately simulated, the accurate data of equipment, personnel and sites required by logistics outfield construction is obtained, the logistics outfield is conveniently and reasonably planned and laid out, the sorting and transferring business requirements are met, and the construction cost is prevented from being wasted.
Description
Technical Field
The invention relates to the field of computers, in particular to a method for constructing a logistics outfield simulation model and an application method thereof.
Background
The construction investment of an external field is as small as tens of millions and as large as hundreds of millions, and particularly, the high matching fitting is required for the purchasing requirement of related external field equipment and the station cargo flow, so that the equipment is too much, the cost is wasted, and the field resources are occupied; the equipment is too few, and the business requirement of sorting and transferring cannot be met, and even the 'explosion bin' is caused during peak of a goods flow, so that the timeliness of goods dispatch is directly influenced, and even the enterprise image is influenced. Therefore, the reasonable planning of the related logistics equipment of the external field is effective, and the method has important significance for reducing the cost and improving the efficiency.
In the current outfield operation process, only relatively limited objective events such as the model, technical parameters, manual scheduling, historical sorting and transferring data of related equipment can be known, and the man-machine efficiency geometry of the whole outfield in the cargo transferring and sorting process and early warning of a plurality of risks caused by too much cargo quantity can not be judged, such as: whether the machine equipment meets the sorting requirements of the goods, whether the manual distribution can meet the optimized efficiency improvement, how much (the load carrying capacity) of the "explosion bin" threshold of the whole outfield, and the like.
For reasonable planning and layout of outfield equipment, the logistics industry generally simulates the field situation in a modeling simulation mode, but at present, simulation related parameters are mainly based on ideal data, and added noise is mainly white noise, so that the whole model is as follows: the weight, the size, the packaging and other data of the package are consistent, all the equipment is in an ideal running state, the manual work efficiency is unchanged and the like, and the simulation results of the parameter running cannot completely reflect the situation of the site and cannot well expose the problem, so that the simulation modeling has not great reference value to the logistics enterprises in practical application.
Disclosure of Invention
The invention aims at: a method for constructing a logistics outfield simulation model and an application method thereof are provided.
The technical scheme of the invention is as follows: in a first aspect, a method for constructing a logistic outfield simulation model is provided, including:
obtaining basic data of a logistics outfield to construct a sample feature vector, wherein the basic data at least comprises equipment parameters, personnel configuration parameters and site parameters;
training a pre-built nonlinear model based on the basic data and classification labels of the cargo flow data corresponding to the sample feature vectors to obtain a target model and an optimal I/O of the target model;
and acquiring a construction scheme of the logistics outfield according to the target model and the optimal I/O of the target model.
In some preferred embodiments, the training the pre-built nonlinear model based on the basic data and the classification label of the cargo flow data corresponding to the sample feature vector to obtain the target model and the optimal I/O of the target model specifically includes:
performing verification training on the nonlinear model to obtain the target model;
preprocessing the cargo flow data corresponding to the sample feature vector and filtering the preprocessed cargo flow data to obtain cleaning target data;
and importing the cleaning target data into the target model for dynamic simulation to obtain the optimal I/O of the target model.
In some preferred embodiments, the performing verification training on the nonlinear model to obtain the target model specifically includes:
and obtaining a transfer function of the linear model to be identified through decoupling and small disturbance linearization analysis so as to obtain the target model.
In some preferred embodiments, the preprocessing the cargo flow data corresponding to the sample feature vector specifically includes:
and identifying and eliminating the data outliers of the cargo flow data corresponding to the sample feature vectors by adopting a low-order polynomial sliding fitting method, and correcting the related outliers by using a Lagrange difference formula.
In some preferred embodiments, the filtering the preprocessed cargo flow data to obtain the cleaning target data specifically includes:
filtering the preprocessed cargo flow data by adopting a fourth-order low-pass digital filter with variable bandwidth and variable sampling frequency to obtain primary filtered cargo flow data;
and performing secondary filtering on the primary filtering cargo flow data according to the limiting value of the sample characteristic vector so as to acquire the cleaning target data.
In some preferred embodiments, the equipment parameters include at least a length, a width, and a speed range of the sort line conveyor belt;
the personnel configuration parameters at least comprise personnel quantity, carrying radius, average carrying speed and working period;
the site parameters at least comprise the number of truck loading acquisitions, the frequency of truck in and out, the number of trucks, the loading and unloading rate of the trucks and the number of platforms for loading and unloading.
In some preferred embodiments, the pre-built nonlinear model is built by:
acquiring model basic parameters based on the basic data;
the nonlinear model is constructed based on the model base parameters and adding random noise to the base data.
In some preferred embodiments, when the model basic parameters are obtained based on the basic data, non-quantitative data in the equipment parameters, personnel configuration parameters and site parameters are expressed in a non-linear manner, and quantitative data is kept unchanged.
In some preferred embodiments, the random noise is modeled as a normal distribution.
In a second aspect, a logistic outfield simulation modeling method based on the construction method of the logistic outfield simulation model provided in the first aspect is provided, which specifically includes:
receiving logistics outfield data to be simulated;
and outputting the logistics outfield simulation modeling and the basic parameter report form to be used for establishing the logistics outfield.
Compared with the prior art, the invention has the advantages that: the system modeling virtual configuration and the field configuration are mapped one by one, and a simulation model is established by adopting a nonlinear model to fit the field nonlinear related situation, so that the reaction field situation can be accurately simulated, the accurate data of equipment, personnel and fields required by logistics outfield construction can be obtained, the logistics outfield can be reasonably planned and laid out conveniently, the sorting and transferring business requirements can be met, and the construction cost can be prevented from being wasted.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for constructing a logistic outfield simulation model according to example 1;
FIG. 2 is a flow chart of the method for modeling logistic outfield simulation provided in example 2;
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: the embodiment provides a method for constructing a logistic outfield simulation model, which is shown by referring to fig. 1, and includes:
s1, acquiring basic data of a logistics external field to construct a sample feature vector, wherein the basic data at least comprises equipment parameters, personnel configuration parameters and site parameters.
In one embodiment, the equipment parameters include at least a length, a width, and a speed range of the sort line conveyor belt;
the personnel configuration parameters at least comprise personnel quantity, carrying radius, average carrying speed and working period;
the site parameters at least comprise the number of truck loading acquisitions, the frequency of truck in and out, the number of trucks, the loading and unloading rate of the trucks and the number of platforms for loading and unloading.
S2, training a pre-built nonlinear model based on the basic data and the classification labels of the cargo flow data corresponding to the sample feature vectors to obtain an optimal I/O of a target model and the target model. The method specifically comprises the following steps:
s2-1, performing verification training on the nonlinear model to obtain the target model.
Preferably, in this embodiment, the target model is obtained by decoupling and small disturbance linearization analysis to obtain a transfer function of the linear model to be identified.
Preferably, in this embodiment, the pre-built nonlinear model is built by:
s2-11, acquiring basic parameters of a model based on the basic data;
s2-12, constructing the nonlinear model based on the model basic parameters and adding random noise to the basic data.
Preferably, in this embodiment, when the model base parameter is obtained based on the base data, non-quantitative data in the equipment parameter, the personnel configuration parameter, and the site parameter is expressed in a non-linear manner, and the quantitative data remains unchanged.
As a preferred aspect, in this embodiment, the random noise is built by using a normal distribution as a model, and the random events generated by the fixed location, the fixed equipment, etc. in the industrial production and life are more accurately simulated by establishing functions related to noise randomness generation and regular presentation. The concrete expression is as follows:
wherein x is the corresponding variable of each parameter, mu is the mean value thereof, and sigma is the standard deviation thereof; μ and σ can be statistically derived from the historical data of the outfield counterpart.
Parameters related to wagon port entry:δm represents a load variable; δT1 represents a wagon arrival frequency variable per unit time; Δm1 is cargo noise; Δt1 is vehicle arrival frequency noise.
Fork truck related parameters:t represents the number of forklifts; delta s forklift truck efficiency variables are installed in unit time; t represents the number of stations; Δs is forklift efficiency noise.
Sorting line related parameters:s represents the length of the sorting line; δv represents the speed variable of the sort line; w represents the width of the sorting line; m represents the weighing threshold of the sorting line; deltav is the velocity noise.
The field personnel configures relevant parameters:p represents the number of site personnel; δr represents a transport radius variable of the site personnel; v represents the average carrying efficiency of the site personnel in unit time; δT represents the duty cycle of the field personnel; w0 represents the maximum carrier weight per person; Δr is the floor personnel's handling radius noise.
Related parameters of automated handling robot:p1 represents the number of automated handling robots; δr represents a conveyance radius variable of the automated conveyance robot; v1 represents average conveying efficiency per unit time of the automatic conveying robot; m1 represents the maximum weighing amount of the automated handling robot; v0 represents the maximum transport cargo volume of the automated transport robot.
Establishing model function to input goods input quantity in unit timeAmount of goods per unit time in sorting processCargo amount in unit time of loading process>
S2-2, preprocessing the cargo flow data corresponding to the sample feature vector and filtering the preprocessed cargo flow data to obtain cleaning target data.
Preferably, in this embodiment, the preprocessing the cargo flow data corresponding to the sample feature vector specifically includes:
s2-21, recognizing and eliminating data outliers of the cargo flow data corresponding to the sample feature vectors by adopting a low-order polynomial sliding fitting method, and correcting related outliers by using a Lagrange difference formula so as to smooth cargo flow data processing.
Preferably, in this embodiment, the filtering the pretreated cargo flow data to obtain the cleaning target data specifically includes:
s2-22, filtering the preprocessed cargo flow data by adopting a fourth-order low-pass digital filter with variable bandwidth and variable sampling frequency to obtain primary filtering cargo flow data, and filtering cargo data with volume which does not accord with a preset range value or weight exceeding or size exceeding in the cargo flow data to prevent the influence on the effect of subsequent simulation.
S2-23, performing secondary filtering on the primary filtering cargo flow data according to the limit value of the sample characteristic vector so as to obtain the cleaning target data. Specifically, the filtering is again determined for the initially filtered cargo flow data obtained in steps S2-22. Illustratively, the secondary filtering is performed according to the width of the sorting line conveyor belt, the maximum weight to be received, the maximum manual handling weight to be received, the automatic handling robot weight to be received, and the like.
S2-3, importing the cleaning target data into the target model for dynamic simulation so as to obtain the optimal I/O of the target model.
S3, acquiring a construction scheme of the logistics outfield according to the target model and the optimal I/O of the target model.
The embodiment provides a building method of a logistics outfield simulation model, which adopts a nonlinear model to build the simulation model to fit the related situation of on-site nonlinear, and can accurately simulate the real situation of the occurrence of a reactant logistics outfield, thereby acquiring accurate equipment, personnel and site data required by logistics outfield construction, acquiring an accurate logistics outfield construction scheme, being convenient for reasonably planning and laying out the logistics outfield, meeting the sorting and transportation service requirements and simultaneously preventing construction cost waste.
Furthermore, by adding nonlinear noise to the normal distribution function to match the complex condition of the real external field, the complex noise possibly occurring in the logistics external field is simulated more accurately, so that scheme data support is provided for the production operation of the real logistics external field.
Example 2: the embodiment provides a logistic outfield simulation modeling method based on the construction method of the logistic outfield simulation model provided in the first aspect, and referring to fig. 2, the method specifically includes:
s21, receiving logistics outfield data to be simulated.
S22, outputting logistics outfield simulation modeling and a basic parameter report form to be used for establishing a logistics outfield.
Specifically, the cargo flow data to be achieved in the logistic offsite to be built is imported into the logistic offsite simulation model provided in embodiment 1, and the logistic offsite simulation model outputs the equipment parameters, the personnel configuration parameters and the site parameters of the logistic offsite to be built and the fault noise model.
More preferably, the three-dimensional dynamic display is used for displaying equipment parameters, personnel configuration parameters and site parameters of the logistical outfield to be established and a fault noise model.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention. In addition, the device and method for constructing the client activity level prediction model provided in the foregoing embodiments belong to the same concept, and detailed implementation processes of the device and method are described in the method embodiments, which are not repeated herein.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. The method for constructing the logistics outfield simulation model is characterized by comprising the following steps of:
obtaining basic data of a logistics outfield to construct a sample feature vector, wherein the basic data at least comprises equipment parameters, personnel configuration parameters and site parameters;
training a pre-built nonlinear model based on the basic data and the classification labels of the cargo flow data corresponding to the sample feature vectors to obtain an optimal I/O of a target model and the target model, wherein the pre-built nonlinear model is built by the following steps:
acquiring model basic parameters based on the basic data;
constructing the nonlinear model based on the model base parameters and adding random noise to the base data;
acquiring a construction scheme of a logistics outfield according to the target model and the optimal I/O of the target model; the input of the target model comprises cargo flow data which needs to be achieved in the places outside the logistics which need to be established; the output of the target model comprises equipment parameters, personnel configuration parameters and site parameters of the logistic outfield to be established, and a fault noise model.
2. The method for constructing a logistic outfield simulation model according to claim 1, wherein training a nonlinear model constructed in advance based on the basic data and classification labels of the cargo flow data corresponding to the sample feature vectors to obtain an optimal I/O of a target model and the target model specifically comprises:
performing verification training on the nonlinear model to obtain the target model;
preprocessing the cargo flow data corresponding to the sample feature vector and filtering the preprocessed cargo flow data to obtain cleaning target data;
and importing the cleaning target data into the target model for dynamic simulation to obtain the optimal I/O of the target model.
3. The method for constructing a logistic outfield simulation model according to claim 2, wherein the performing verification training on the nonlinear model to obtain the target model specifically includes:
and obtaining a transfer function of the linear model to be identified through decoupling and small disturbance linearization analysis so as to obtain the target model.
4. The method for constructing a logistic outfield simulation model according to claim 2, wherein the preprocessing the cargo flow data corresponding to the sample feature vector specifically comprises:
and identifying and eliminating the data outliers of the cargo flow data corresponding to the sample feature vectors by adopting a low-order polynomial sliding fitting method, and correcting the related outliers by using a Lagrange difference formula.
5. The method for constructing a logistic outfield simulation model according to claim 2, wherein the filtering the preprocessed cargo flow data to obtain the cleaning target data specifically comprises:
filtering the preprocessed cargo flow data by adopting a fourth-order low-pass digital filter with variable bandwidth and variable sampling frequency to obtain primary filtered cargo flow data;
and performing secondary filtering on the primary filtering cargo flow data according to the limiting value of the sample characteristic vector so as to acquire the cleaning target data.
6. A method of constructing a logistic outfield simulation model according to any one of claims 1-5, wherein the equipment parameters include at least the length, width and speed range of the sorting line conveyor;
the personnel configuration parameters at least comprise personnel quantity, carrying radius, average carrying speed and working period;
the site parameters at least comprise the number of truck loading acquisitions, the frequency of truck in and out, the number of trucks, the loading and unloading rate of the trucks and the number of platforms for loading and unloading.
7. The method for constructing a logistic outfield simulation model according to claim 1, wherein when the basic parameters of the model are acquired based on the basic data, non-quantitative data in the equipment parameters, personnel configuration parameters and site parameters are expressed in a non-linear manner, and quantitative data are kept unchanged.
8. The method for constructing a logistic outfield simulation model according to claim 1, wherein the random noise is modeled in a normal distribution.
9. The logistic outfield simulation modeling method of the construction method of the logistic outfield simulation model according to any one of claims 1 to 5 and 7 to 8, characterized by comprising the following specific steps:
receiving logistics outfield data to be simulated;
and outputting the logistics outfield simulation modeling and the basic parameter report form to be used for establishing the logistics outfield.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011138532.3A CN112163819B (en) | 2020-10-22 | 2020-10-22 | Logistics outfield simulation model construction method and application method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011138532.3A CN112163819B (en) | 2020-10-22 | 2020-10-22 | Logistics outfield simulation model construction method and application method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112163819A CN112163819A (en) | 2021-01-01 |
| CN112163819B true CN112163819B (en) | 2023-08-25 |
Family
ID=73864562
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011138532.3A Active CN112163819B (en) | 2020-10-22 | 2020-10-22 | Logistics outfield simulation model construction method and application method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112163819B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112700200B (en) * | 2021-01-06 | 2024-01-16 | 上海燕汐软件信息科技有限公司 | Method, device and medium for delivering express terminal |
| CN112862161A (en) * | 2021-01-18 | 2021-05-28 | 上海燕汐软件信息科技有限公司 | Goods sorting management method and device, electronic equipment and storage medium |
| CN113968445B (en) * | 2021-11-18 | 2022-09-09 | 宝开(上海)智能物流科技有限公司 | Light and small piece high-speed sorting control method, system and device |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010017925A (en) * | 1999-08-16 | 2001-03-05 | 이정욱 | Method for preestimating a plan of a container terminal |
| CN103010485A (en) * | 2012-12-21 | 2013-04-03 | 南京航空航天大学 | Simulation modeling method for tilt-rotor unmanned plane and system thereof |
| CN105844360A (en) * | 2016-03-29 | 2016-08-10 | 中国农业大学 | Agricultural product cold chain storage transport fine control modeling and optimizing method |
| CN108364099A (en) * | 2018-02-11 | 2018-08-03 | 武汉科技大学 | A kind of logistics distribution system and allocator based on emulation |
| KR101993476B1 (en) * | 2018-01-22 | 2019-06-27 | 주식회사 지노스 | Ship block logistics simulation modeling method using logistics token and process-centric modeling method |
| CN111223001A (en) * | 2019-12-18 | 2020-06-02 | 中国科学院软件研究所 | Resource scheduling method and system based on multiple process models |
| CN111291959A (en) * | 2018-12-10 | 2020-06-16 | 顺丰科技有限公司 | Resource allocation method, device, equipment and storage medium |
| KR20200076497A (en) * | 2018-12-19 | 2020-06-29 | 대우조선해양 주식회사 | Method for realizing human resource modeling simulator with human resource demand predicting modeling mechanism in shipbuilding industry |
| CN111626650A (en) * | 2019-02-28 | 2020-09-04 | 顺丰科技有限公司 | Logistics site equipment efficiency optimization method and system |
| CN111625919A (en) * | 2019-02-28 | 2020-09-04 | 顺丰科技有限公司 | Design method and device of logistics simulation system |
-
2020
- 2020-10-22 CN CN202011138532.3A patent/CN112163819B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20010017925A (en) * | 1999-08-16 | 2001-03-05 | 이정욱 | Method for preestimating a plan of a container terminal |
| CN103010485A (en) * | 2012-12-21 | 2013-04-03 | 南京航空航天大学 | Simulation modeling method for tilt-rotor unmanned plane and system thereof |
| CN105844360A (en) * | 2016-03-29 | 2016-08-10 | 中国农业大学 | Agricultural product cold chain storage transport fine control modeling and optimizing method |
| KR101993476B1 (en) * | 2018-01-22 | 2019-06-27 | 주식회사 지노스 | Ship block logistics simulation modeling method using logistics token and process-centric modeling method |
| CN108364099A (en) * | 2018-02-11 | 2018-08-03 | 武汉科技大学 | A kind of logistics distribution system and allocator based on emulation |
| CN111291959A (en) * | 2018-12-10 | 2020-06-16 | 顺丰科技有限公司 | Resource allocation method, device, equipment and storage medium |
| KR20200076497A (en) * | 2018-12-19 | 2020-06-29 | 대우조선해양 주식회사 | Method for realizing human resource modeling simulator with human resource demand predicting modeling mechanism in shipbuilding industry |
| CN111626650A (en) * | 2019-02-28 | 2020-09-04 | 顺丰科技有限公司 | Logistics site equipment efficiency optimization method and system |
| CN111625919A (en) * | 2019-02-28 | 2020-09-04 | 顺丰科技有限公司 | Design method and device of logistics simulation system |
| CN111223001A (en) * | 2019-12-18 | 2020-06-02 | 中国科学院软件研究所 | Resource scheduling method and system based on multiple process models |
Non-Patent Citations (1)
| Title |
|---|
| 卷烟物流复合式分拣系统的设备和人员配置优化模型与方法研究;张华庆;中国优秀硕士学位论文全文数据库经济与管理科学辑(第07期);J145-276 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112163819A (en) | 2021-01-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112163819B (en) | Logistics outfield simulation model construction method and application method thereof | |
| Serrano et al. | Scheduling of truck arrivals, truck departures and shop-floor operation in a cross-dock platform, based on trucks loading plans | |
| WO2019187779A1 (en) | Warehouse system | |
| Aulin et al. | Increasing the functioning efficiency of the working warehouse of the “UVK Ukraine” company transport and logistics center | |
| CN103870889A (en) | Logistic center cargo delivery routing problem eM-Plant simulation method | |
| Yang et al. | Modelling and production configuration optimization for an assembly shop | |
| Reggelin et al. | A mesoscopic approach to modeling and simulation of logistics processes | |
| Kluska et al. | The use of simulation in the design of Milk-Run intralogistics systems | |
| Jeong et al. | Application of a concept development process to evaluate process layout designs using value stream mapping and simulation | |
| Mahmood et al. | Production intralogistics automation based on 3D simulation analysis | |
| EP3712833A1 (en) | Product placement system, product placement method, and product placement program | |
| Sadeghi et al. | Optimizing warehouse operations for environmental sustainability: a simulation study for reducing carbon emissions and maximizing space utilization | |
| CN110795601A (en) | System and method for classifying and intelligently storing industrial waste salt | |
| CN110963219A (en) | Intelligent production line for disassembling and assembling container freight yard station | |
| Rohrer | Simulation of manufacturing and material handling systems | |
| Greenwood et al. | A multi-level framework for simulating milk-run, in-plant logistics operations | |
| Lang et al. | Mesoscopic simulation models for logistics planning tasks in the automotive industry | |
| Golova et al. | Simulation based performance analysis of production intralogistics | |
| Bojovic et al. | A two‐level approach to the problem of rail freight car fleet composition | |
| Abedinzadeh et al. | ParisaTavafi. Improving the performance of warehouse loading and unloading system using simulation | |
| Romer et al. | Data-based services for smart load carriers: functional design and requirements analysis for internet of things technologies | |
| Zhou et al. | Conceptual simulation modeling of warehousing operations | |
| Tsaples et al. | Investigating the effects of introducing Automated Straddle Carriers in port operations with a System Dynamics model | |
| Wang et al. | An AGV scheduling algorithm for smart workshops with limited logistics capacity | |
| Lin et al. | A Digital Twin Simulation Framework for Smart Warehousing |
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 |