CN113850488A - Multi-variety small-batch multi-resource scheduling system and method based on digital twin - Google Patents

Multi-variety small-batch multi-resource scheduling system and method based on digital twin Download PDF

Info

Publication number
CN113850488A
CN113850488A CN202111073463.7A CN202111073463A CN113850488A CN 113850488 A CN113850488 A CN 113850488A CN 202111073463 A CN202111073463 A CN 202111073463A CN 113850488 A CN113850488 A CN 113850488A
Authority
CN
China
Prior art keywords
scheduling
equipment
processing
workshop
products
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111073463.7A
Other languages
Chinese (zh)
Inventor
娄平
曾宇航
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan University of Technology WUT
Original Assignee
Wuhan University of Technology WUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuhan University of Technology WUT filed Critical Wuhan University of Technology WUT
Priority to CN202111073463.7A priority Critical patent/CN113850488A/en
Publication of CN113850488A publication Critical patent/CN113850488A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The invention discloses a multi-variety small-batch multi-resource scheduling system and method based on a digital twin. The order optimization module splits the order, performs similar batch combination according to process similarity, and transmits the order as a basic task of production to the scheduling optimization module; the workshop data perception and disturbance event detection module is used for realizing the real-time acquisition of the workshop 'man-machine-object-method-ring' state information based on a workshop disturbance detection method of perception data fusion, transmitting the acquired information to the disturbance event processing module for disturbance factor judgment, and transmitting the disturbance event to the scheduling optimization module for realizing the dynamic evolution of production; and the scheduling optimization module receives and processes the information data transmitted by other modules to calculate a target scheduling scheme. The problems of multiple product varieties and small batch in the order are solved, the problem of multi-target multi-resource scheduling and distribution in the workshop is solved, and the flexibility and robustness of the workshop to sudden accidents and the effectiveness of scheduling plans are improved.

Description

Multi-variety small-batch multi-resource scheduling system and method based on digital twin
Technical Field
The invention belongs to the technical field of digital twins, and particularly relates to a multi-variety small-batch multi-resource scheduling method based on digital twins.
Background
In the face of the wave of economic globalization, the manufacturing industry has changed significantly in recent years, and the service mode of 'customizing and ordering' for customers becomes the development direction of manufacturing enterprises. To survive in such a competitive environment, enterprises must quickly adapt to the rapid changes in market demand by increasing operational flexibility, increasing survival rates, shortening product cycles, and enhancing fault tolerance. Because the production equipment and the production route are frequently switched due to the fact that the products produced in a workshop are various and the production batch is small due to the 'customization and order-based' manner, the processing preparation time is increased, the stability of the workshop is reduced, the frequency of equipment faults is increased, the yield is reduced, and the like, the current situation of 'multi-variety small-batch' production needs to be effectively solved urgently.
The scheduling is a key and effective means in production and manufacturing, and is an optimization mode for efficiently producing products by effectively utilizing resources such as existing equipment, personnel, materials and the like in a workshop. Conventional dispatch plans consider only a single resource (process tool), or consider dual resources (process tool and operator). However, in the current workshop, a plurality of resources coexist in processing equipment, operators and carrying trolleys. The comprehensive allocation of these resources to minimize production cost and maximize production efficiency is an urgent problem to be solved at present.
Secondly, the service mode of 'customizing and ordering' enables the dynamic property of the workshop production process to become more complex and diversified, under the dynamic environment, the original scheduling plan may be degraded or even not feasible, and the cost is increased due to the problems of missing proper date and deadline, idle resources, higher work-in-process inventory, frequent re-planning and the like. How to obtain the disturbance event in the dynamic environment in real time and carry out effective evolution scheduling on the production process is also a pain point in a workshop production plan.
The digital twin is a simulation process integrating multidisciplinary, multi-physical quantity, multi-scale and multi-probability by fully utilizing data such as a physical model, sensor updating, operation history and the like, and mapping is completed in a virtual space, so that the full life cycle process of corresponding entity equipment is reflected. Meanwhile, the system also has a virtual-real interaction function, and can update and dynamically evolve in real time, so that evolution control and optimization of the physical world are realized.
At present, aiming at the related technology, an effective solution is not provided for applying the digital twin technology to the multi-resource scheduling aspect of multi-variety and small-batch.
For example, chinese patent CN 201911135963.1: an order scheduling and distribution scheduling method and system considers the order problem that how to realize continuous production of products with the same order and the same model can be realized under reverse scheduling, and the production is not interrupted. The production line balancing algorithm with the weight as the core is realized, the basic requirement of reverse production scheduling according to the delivery period is kept, and the continuity requirement required by production is met. However, how to convert the "small lots of multiple varieties" into "large lots of less varieties" cannot be solved. The method can not realize different orders, but has the problem of uniform mass production of products with similar processes.
Chinese patent CN 202011337391.8: a digital twin intelligent cloud scheduling method meeting personalized customized production is characterized in that order weight is calculated according to pre-production processing and formulating rules of orders before order execution, order indexes are definitely influenced, and a dynamic scheduling model based on two dynamic events of 'new personalized order arrival' and 'emergency important customer order insertion' is adopted. Order priority production issues are considered. Is rescheduling under "personalized order arrival" and "urgent important customer insertion". Is passively scheduled upon the generation of a perturbation event and periodically rescheduled without an emergency order. And the active evolution scheduling accurate prediction aiming at the generated disturbance factors cannot be realized.
Disclosure of Invention
In order to solve the technical problems in the related technologies to a certain extent, the invention aims to provide a multi-variety and small-batch multi-resource scheduling method based on a digital twin, which solves the problems of multiple product varieties and small batches in an order and the problem of multi-target and multi-resource scheduling and distribution in a workshop.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-variety and small-batch multi-resource scheduling system based on digital twin is characterized in that:
the system at least comprises an order optimization processing module, a workshop data perception and disturbance event detection module and a scheduling optimization processing module:
the order optimization processing module is used for splitting the customer orders according to the industry standard classification based on an order process similarity classification method of fuzzy clustering analysis to obtain a total set of different products with the minimum unit, carrying out fuzzy clustering classification calculation on the products in the total set according to process similarity, merging the products with the same similarity into similar batches, and taking all the similar batch products as the basic production target of batch scheduling;
the workshop data perception and disturbance event detection module is used for realizing the real-time acquisition of workshop 'man-machine-object-method-ring' state information based on a workshop disturbance event detection method of perception data fusion, transmitting the acquired information to a disturbance event processing module for disturbance factor judgment, processing the workshop state data in real time, and judging and classifying workshop equipment abnormity, personnel off post, material defect, processing method error and safety accident, and transmitting the disturbance event to scheduling optimization processing to realize the dynamic evolution scheduling of production;
the scheduling optimization processing module comprises an initialization pre-scheduling module and a dynamic evolution module, wherein the initialization pre-scheduling module is used for realizing multi-target multi-resource scheduling distribution, minimizing the maximum completion time and the production cost as processing targets, comprehensively considering dependence conditions of processing equipment, personnel and a carrying trolley, determining the processing sequence of N similar batches of workpieces on M equipment, the processing distribution condition of S workers on the M equipment and the distribution condition of W carrying vehicles on the M equipment, wherein W and S are less than M, and solving the scheduling problem by using a wolf optimization algorithm; and the dynamic evolution module realizes effective evolution scheduling optimization on different disturbance events.
A multi-variety small-batch multi-resource scheduling method based on digital twin is characterized by comprising the steps of order optimization processing, workshop data perception and disturbance event detection and scheduling optimization processing:
s1: performing order optimization processing, namely splitting a customer order according to an industry standard classification by using an order process similarity classification method based on fuzzy clustering analysis to obtain a total set of different products with minimum units, performing fuzzy clustering classification calculation on the products in the total set according to process similarity, merging the products with the same similarity into similar batches, and taking all the similar batches as basic production targets for batch scheduling;
s2: workshop data perception and disturbance event detection, wherein a workshop disturbance event detection method based on perception data fusion realizes real-time acquisition of workshop 'man-machine-object-method-ring' state information, transmits the acquired information to a disturbance event processing module for disturbance factor judgment, processes workshop state data in real time, and judges and classifies workshop equipment abnormity, personnel off duty, material defect, processing method error and safety accident, and transmits a disturbance event to scheduling optimization processing to realize production dynamic evolution scheduling;
s3: scheduling optimization processing, including an initialization pre-scheduling stage and a dynamic evolution stage; initializing a pre-dispatching model stage for realizing multi-target multi-resource dispatching distribution, taking minimized maximum completion time and minimized production cost as processing targets, comprehensively considering dependence conditions of processing equipment, personnel and a carrying trolley, determining the processing sequence of N similar batch workpieces on M equipment, the processing distribution conditions of S workers on M equipment and the distribution conditions of W carrying vehicles on M equipment, wherein W and S are less than M, and solving the dispatching problem by using a wolf optimization algorithm; and the dynamic evolution stage realizes effective evolution scheduling optimization on different disturbance events.
In the above technical solution, in the step of optimizing the order at S1, the order is split into B customer orders according to the industry standard categories of the product, and the product is obtainedn total sets of products P, where PiIs the product of the ith category.
In the above technical solution, in the step of optimizing the order in S1, the order is split by using a fuzzy analysis method, the product process similarity analysis and calculation is performed on the products in the total set P containing n types of products, the products with the same process similarity are combined into similar batches, and a product set EP containing K types of similar batches is formed, where the EP iskThe method is characterized in that similar batch products of the kth batch are obtained, wherein K is less than or equal to n, and the fuzzy clustering analysis step comprises the following steps:
s11: forming basic data matrix by product process
Figure BDA0003261311240000041
m is the number of product processes;
s12: normalization using the maximum value: for the characteristic index matrix X*The j-th column element of (1) calculates the maximum value Mj＝max{x1j，x2j，...，xnjJ is 1, 2.. times.m, then transformed
Figure BDA0003261311240000042
i＝1，2，...，n；
S13: normalized data, using the Max-Min method
Figure BDA0003261311240000043
xil∈[0，1]1, 2, ·, n; i 1, 2.. m, a similar construction is performed such that the similarity matrix is R ═ R (R ═ R)ij)n×n，rij∈[0，1]；
S14: calculating a transfer packet of the fuzzy similarity matrix R when A is Ra＝R2aWhen (1 is more than or equal to l and less than or equal to n) is established for the first time, obtaining a fuzzy equivalent matrix A, and selecting a proper interception level lambda belonging to [0, 1 ]]And find the lambda intercept matrix A of Aλ(ii) a According to AλPerforming similar classification on the products to obtain a similar batch product set EP;
s15: similar batches of EPkAll product set P '═ { P'1，p′2,., b } average processing time TavgAsEPkThe processing time T of the batch scheduling is,
Figure BDA0003261311240000051
c＝1，2，3...，b，tp′cis the processing time of the c product in P ', sp'cThe amount of the c-th product in P'.
In the technical scheme, in the step S2, in the sensing of the workshop data and the detection of the disturbance event, the workshop data sensing senses the real-time operation information of the workshop equipment including the processing speed, the processing time, the processing temperature, the pressure and the energy consumption of the equipment through a sensor, acquires the on-duty information of workers through a camera, acquires the type and the quantity of materials through a bar code/RFID (radio frequency identification) device, senses the state of a processing method through a special quality detection device, and senses the state of the field environment through an alarm device.
In the above technical solution, in the step S2 of sensing the workshop data and detecting the disturbance event, the workshop disturbance detection includes the following steps:
s21: training historical data and states of equipment operation process information, constructing an equipment fault classification model, extracting face features of employees, and constructing an employee face feature library;
s22: checking the marks of all the devices, and dividing the devices into a normal group and an abnormal group according to the marks;
s23: for normal group equipment, equipment fault prediction is carried out on equipment operation process data sensed in real time, whether equipment m faults exist is predicted, if the prediction result is that the equipment m is about to generate faults, a scheduling optimization processing module is informed, equipment fault evolution scheduling is carried out, constraint of the equipment m in scheduling is set to be 0, and the equipment m is marked to be abnormal;
s24: for the abnormal group equipment, equipment failure prediction is carried out on equipment operation process data sensed in real time, whether equipment m is recovered to be normal or not is predicted, if the prediction result is that the equipment m is recovered to be normal, a scheduling optimization module is informed, equipment failure recovery evolution scheduling is carried out, constraint of the equipment m in scheduling is set to be 1, and the equipment m is marked to be normal;
s25: checking all employee marks, and dividing the employees into on-duty groups and off-duty groups according to the marks;
s26: for the on-duty group, the face photos of the employees sensed in real time are subjected to feature FcExtracting and calculating feature data F in a feature librarypreAnd FcurIf V is greater than or equal to Vmax，Vmax→ 0, informing the scheduling optimization module to carry out staff off-Shift evolution scheduling, setting the staff S constraint in scheduling to be 0, and marking the staff S as off-Shift;
s27: for off-post group, performing feature F on employee face photos sensed in real timecExtracting and calculating feature data F in a feature librarypreAnd FcurIf V < VmaxValue VmaxAnd → 0, informing the scheduling optimization module to carry out the staff off duty and restore the evolution scheduling, setting the staff S constraint in the scheduling to be 1, and marking the staff S as on duty.
S28: checking the marks of all the materials, and dividing the materials into material defect and material defect recovery groups according to the marks;
s29: for the material defect group, the real-time perception of the quantity of the raw materials in the stock and the quantity of the products needing to be processed, such as the occurrence of the raw material PsiLess than the processed product PiThe number of the products is marked as material defect, the scheduling optimization module is informed of the material defect evolution scheduling, and the P in the scheduling task is reducediNumber of (value is Δ ═ P)i-Psi) And recording the defect value delta;
s210: for the material defect recovery group, the real-time perception of the quantity of the raw materials in the stock and the quantity of the products to be processed, such as the occurrence of the raw material PsiNot less than the number of the processed product PiThe number of the products is marked as material defect recovery, the scheduling optimization module is informed, the material defect recovery evolution scheduling is carried out, and the P in the scheduling task is increasediThe number of (value is Δ), the clear record defect value (Δ ═ 0);
s211: sensing and acquiring quality inspection equipment information in real time, and acquiring P when P is acquirediWhen the information of the product is abnormal,find out the corresponding processing equipment m and operator s, and mark the product PiInforming a scheduling optimization module of the error of the processing method, carrying out error evolution scheduling of the processing method, and removing the mark after scheduling processing is finished by the equipment m and the operator s;
s212: sensing and acquiring alarm equipment information of a field environment in real time, and finding out processing equipment m, operators s and processed products P corresponding to safety accidents when the alarm equipment information is acquirediAnd marking the product PiAnd the equipment m and the operator s are safety accidents, inform the scheduling optimization module to carry out safety accident evolution scheduling, and remove the mark after the scheduling processing is finished.
The invention has the beneficial effects that:
the method improves the flexibility and robustness of workshop scheduling, can improve the production efficiency, reduces the production cost and the personnel cost, meets the change requirement of the intelligent workshop at the present stage, and has wide application prospect. The method specifically comprises the following steps:
1. by collecting, merging and classifying the state data and the historical data in the processing process of personnel, equipment, materials and tasks, the effective prediction of disturbance events is realized.
2. And constructing a disturbance event evolution scheduling strategy taking data as a drive, and realizing efficient scheduling optimization processing on the disturbance event.
3. And calculating the multi-target multi-resource batch scheduling problem by using a wolf optimization algorithm, and efficiently generating a scheduling distribution scheme.
4. The products are classified by adopting a fuzzy clustering method, so that the grouping speed of the products is increased, the product varieties are reduced, the product batch is increased, the problem of 'multi-variety small-batch' in a workshop on production is optimized, and the response speed of the workshop to orders is improved.
5. The invention solves the problem of how to convert 'multi-variety small batch' (namely, the order is shown to be more in terms of orders, but the quantity of products in the order is less) into 'small variety large batch' (in a batch production task order, the products in each order are similar products), and the problem of how to realize the unified batch production of different orders but similar products is highlighted by the order optimization composition. At the same time, the maximum completion time and the maximum cost (personnel cost, equipment energy consumption cost and transport vehicle energy consumption cost) can be minimized, so that the product can be delivered in the delivery date and the cost in the production process is low.
6. The problems of multiple product varieties and small batch in an order are solved, the problem of multi-target multi-resource scheduling distribution of a workshop is solved, the workshop state is sensed in real time by combining a digital twin technology, the disturbance event is subjected to evolution scheduling, the management and control of an actual workshop are enhanced, and the flexibility and robustness of the workshop to sudden accidents and the effectiveness of a scheduling plan are improved.
Drawings
The accompanying drawings are included to provide a further understanding of the technology or prior art of the present application and are incorporated in and constitute a part of this specification.
FIG. 1 is a diagram showing the overall structure of the method of the present invention.
FIG. 2 is an overall flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the accompanying drawings.
The invention aims to provide a multi-variety and small-batch multi-resource scheduling method based on a digital twin, which solves the problems of multiple product varieties and small batches in an order and the problem of multi-target and multi-resource scheduling and distribution in a workshop, and combines a digital twin technology, senses the state of the workshop in real time, performs evolution scheduling on a disturbance event, enhances the management and control on the actual workshop, and improves the flexibility and robustness of the workshop to sudden accidents and the effectiveness of a scheduling plan.
The overall structure diagram of the invention is shown in figure 1, and comprises order optimization processing, workshop data perception and disturbance event detection and scheduling optimization processing, the overall flow diagram of the invention is shown in figure 2, orders are taken as guidance in a workshop, order optimization is performed on the orders of 'multiple varieties and small batches' to obtain a production target of 'less varieties and large batches', the state information of 'man-machine-object-method-ring' of the current workshop is taken as constraint, a scheduling model which takes the minimum maximum completion time and the total processing cost as the target is constructed, a grey wolf optimization algorithm is adopted to calculate a scheduling scheme, the scheduling scheme is implemented in the actual workshop, meanwhile, the disturbance events of 'man-machine-object-method-ring' in the workshop are perceived in real time, and evolution scheduling optimization is performed according to disturbance types.
For the order optimization in fig. 1, the specific contents are as follows: the order optimization processing comprises the splitting of orders and the aggregation of orders, wherein the splitting of orders is realized by splitting B customer orders according to the industry standard categories of products to obtain a total set P containing n products, wherein P isiIs the product of the ith category. Performing product process similarity analysis calculation on products in the total set P containing n products by adopting a fuzzy analysis method, merging products with the same process similarity into similar batches to form a product set EP containing K similar batches, wherein the EPkThe method is characterized in that similar batch products of the kth batch are obtained, wherein K is less than or equal to n, and the fuzzy clustering analysis step comprises the following steps:
a) forming basic data matrix by product process
Figure BDA0003261311240000081
m is the number of product processes;
b) normalization using the maximum value: for the characteristic index matrix X*The j-th column element of (1) calculates the maximum value Mj＝max{x1j，x2j，...，xnjJ is 1, 2.. times.m, then transformed
Figure BDA0003261311240000082
i＝1，2，...，n；
c) Normalized data, using the Max-Min method
Figure BDA0003261311240000083
xil∈[0，1]1, 2, ·, n; i 1, 2.. m, a similar construction is performed such that the similarity matrix is R ═ R (R ═ R)ij)n×n，rij∈[0，1]；
d) Calculating a transfer packet of the fuzzy similarity matrix R when A is Ra＝R2aWhen (1 is more than or equal to l and less than or equal to n) is established for the first time, obtaining a fuzzy equivalent matrix A, and selecting a proper interception level lambda belonging to [0, 1 ]]And find the intercept matrix A of Aλ(ii) a According to AλAnd performing similar classification on the products to obtain a similar batch product set EP.
e) Similar batches of EPkAll product set P '═ { P'1，p′2,., b } average processing time TavgAs EPkThe processing time T of the batch scheduling is,
Figure BDA0003261311240000091
c＝1，2，3...，b，tp′cis the processing time of the c product in P ', sp'cThe amount of the c-th product in P'.
For the sensing and transmission of the workshop data state in fig. 1, the real-time operation information of the workshop equipment, including the processing speed, the processing time, the processing temperature, the pressure and the energy consumption of the equipment, is sensed through the sensor, the on-duty information of workers is acquired through the camera, the types and the quantity of materials are acquired through the bar code/RFID equipment, the processing method state is sensed through the special quality detection equipment, and the field environment state is sensed through the alarm equipment.
For inter-vehicle disturbance detection in fig. 1, the following is specifically implemented:
(1) training historical data and states of equipment operation process information, constructing an equipment fault classification model, extracting face features of employees, and constructing an employee face feature library;
(2) checking the marks of all the devices, and dividing the devices into a normal group and an abnormal group according to the marks;
(3) for normal group equipment, equipment fault prediction is carried out on equipment operation process data sensed in real time, whether equipment m faults exist is predicted, if the prediction result is that the equipment m is about to generate faults, a scheduling optimization module is informed, equipment fault evolution scheduling is carried out, constraint of the equipment m in scheduling is set to be 0, and the equipment m is marked to be abnormal;
(4) for the abnormal group equipment, equipment failure prediction is carried out on equipment operation process data sensed in real time, whether equipment m is recovered to be normal or not is predicted, if the prediction result is that the equipment m is recovered to be normal, a scheduling optimization module is informed, equipment failure recovery evolution scheduling is carried out, constraint of the equipment m in scheduling is set to be 1, and the equipment m is marked to be normal;
(5) checking all employee marks, and dividing the employees into on-duty groups and off-duty groups according to the marks;
(6) for the on-duty group, the face photos of the employees sensed in real time are subjected to feature FcExtracting and calculating feature data F in a feature librarypreAnd FcurIf V is greater than or equal to Vmax，Vmax→ 0, informing the scheduling optimization module to carry out the staff off-Shift evolution scheduling, setting the staff S constraint in the scheduling to be 0, and marking the staff S as off-Shift;
(7) for off-post group, performing feature F on employee face photos sensed in real timecExtracting and calculating feature data F in a feature librarypreAnd FcurIf V < VmaxValue VmaxAnd → 0, informing the scheduling optimization module to carry out the staff off duty and restore the evolution scheduling, setting the staff S constraint in the scheduling to be 1, and marking the staff S as on duty.
(8) Checking the marks of all the materials, and dividing the materials into material defect and material defect recovery groups according to the marks;
(9) for the material defect group, the real-time perception of the quantity of the raw materials in the stock and the quantity of the products needing to be processed, such as the occurrence of the raw material PsiLess than the processed product PiThe number of the products is marked as material defect, the scheduling optimization module is informed of the material defect evolution scheduling, and the P in the scheduling task is reducediNumber of (value is Δ ═ P)i-Psi) And recording the defect value delta;
(10) for the material defect recovery group, the real-time perception of the quantity of the raw materials in the stock and the quantity of the products to be processed, such as the occurrence of the raw material PsiThe number of which is not less than the number of the processed products PiThe product is marked as material defect recovery, informs a scheduling optimization module, implements material defect recovery evolution scheduling, and increases P in a scheduling taskiThe number of (value is Δ), the clear record defect value (Δ ═ 0);
(11) sensing and acquiring quality inspection equipment information in real time, and acquiring P when P is acquirediWhen the product is abnormal, finding out the corresponding processing equipment m and operator s, and marking the product PiInforming a scheduling optimization module of the error of the processing method, carrying out error evolution scheduling of the processing method, and removing the mark after scheduling processing is finished by the equipment m and the operator s;
(12) sensing and acquiring alarm equipment information of a field environment in real time, and finding out processing equipment m, operators s and processed products P corresponding to safety accidents when the alarm equipment information is acquirediAnd marking the product PiThe equipment m and the operator s are safety accidents, inform the scheduling optimization module to carry out safety accident evolution scheduling, and remove the mark after the scheduling processing is finished;
for the mathematical model of the scheduling optimization process in fig. 1, a mathematical programming method is used for modeling, and the specific implementation method is as follows:
(1) constructing an objective function
F＝min{αF1+(1-α)F2In which F1＝max(Ci) It is indicated that the maximum time-out,
Figure BDA0003261311240000111
Figure BDA0003261311240000112
wherein f is2Representing personnel and equipment energy costs, f3Indicating the cost of energy consumption of the carrier, F2Represents the total cost of the production process;
(2) adding constraints
Figure BDA0003261311240000113
For the same product, only after the previous working procedure task is finished, the next working procedure can be carried outWorking;
Figure BDA0003261311240000114
in the same process, only one device can be selected for processing operation, and only one worker and one carrier can be selected for operation;
Cij-Cgh+H(1-cijk)+H(1-xghk)+H×yijghk≥tijk，
Cgh-Cij+H(1-cijk)+H(1-xghk)+H×(1-yijghk)≥tghkthe method indicates that the machining operations of the same equipment have a precedence relationship and cannot be performed simultaneously;
wherein x isijk∈{0，1}，xijks∈{0，1}，yijghk∈{0，1}，i，g＝1，2，...，N，j＝1，2，...ri，h＝1，2，...rgK is 1, 2.. the M, S is 1, 2.. the S, a ∈ (0, 1) random coefficient, CiRepresents the final processing completion time of the similar batch i, N represents the total batch number of workpieces of the similar batch to be processed in the production workshop, M represents the total number of processing equipment, S represents the total number of operators, riRepresents the total number of steps, O, of the workpiece iijJ-th working process, t, representing workpiece iijkRepresents the process OijMachining time in the machining apparatus k, CijksRepresents the process OijCost of working by workers s on the equipment k, EijksRepresents the process OijEnergy consumption for working by workers s on the equipment k, EijkwRepresents the process OijEnergy consumption, x, of transport by AGVw on equipment kijksRepresents the process OijMachining on the device k and by the operating worker s the value of the variable is 1, otherwise 0, xijkwRepresents the process OijProcessed on equipment k and carried by carrier w and having a variable value of 1, otherwise 0, yijghkRepresents the process OijWhether or not to take precedence over OghExecuting on the equipment k, wherein if the variable value is 1, otherwise, the variable value is 0;
for the dynamic scheduling policy of the scheduling optimization process in fig. 1, the specific implementation manner is as follows:
machine fault evolution scheduling: acquiring all processing procedures and processing time of unprocessed workpieces assigned on the machine and information of unfinished procedures and processing time of workpieces to be processed as scheduling targets, taking a last scheduling scheme as an initial solution of a wolf optimization algorithm, and recording a scheduling scheme calculated by the algorithm;
and (3) personnel off-post evolution scheduling: searching all employee record distribution schemes with the same operation capacity as the employee from the historical distribution schemes, selecting the employee with the least processing amount as a substitute of the employee, and recording the current distribution scheme.
And (3) material defect evolution scheduling: all products related to defective raw materials are obtained, the number of unprocessed products is reduced, and the processing sequence of products following such products is advanced.
And (3) error evolution scheduling of a processing method: and (4) emergently reminding an operator, correcting the processing method and the equipment processing parameters, obtaining raw materials corresponding to the processing defective products, and temporarily inserting the raw materials into production equipment for processing according to the original scheduling scheme.
Safety accident evolution scheduling: and judging the health state of the staff involved in the safety accident, if the staff cannot stay on duty continuously, performing staff off duty scheduling, judging the running state of the related equipment, if a fault occurs, performing equipment fault scheduling, acquiring raw materials corresponding to the product involved in the product, and temporarily plugging the raw materials into production equipment for processing according to the original scheduling scheme.
Those skilled in the art will appreciate that the specific embodiments of the invention described herein are merely exemplary of the invention, which is not described in detail in this disclosure.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.

Claims (8)

1. A multi-variety small-batch multi-resource scheduling system based on digital twin is characterized by at least comprising an order optimization module, a workshop data perception and disturbance event detection module and a scheduling optimization module; the order optimization module divides the customer orders according to the industry standard classification, performs similar batch combination according to the process similarity of the products, and transmits the combination as the basic task of production to the scheduling optimization module; the workshop data perception and disturbance event detection module is used for realizing the real-time acquisition of workshop 'man-machine-object-method-ring' state information based on a workshop disturbance detection method of perception data fusion, transmitting the acquired information to a disturbance event processing module therein for disturbance factor judgment, processing the workshop state data in real time, and transmitting the determined disturbance event to a scheduling optimization module to realize the dynamic evolution scheduling of production; and the scheduling optimization module receives and processes the information data transmitted by other modules and performs iterative optimization to obtain an excellent scheduling scheme.
2. The digital twin-based multi-variety small-lot multi-resource scheduling system of claim 1, wherein:
(1) the order optimization processing module is used for splitting the customer orders according to the industry standard classification based on an order process similarity classification method of fuzzy clustering analysis to obtain a total set of different products with the minimum unit, carrying out fuzzy clustering classification calculation on the products in the total set according to process similarity, merging the products with the same similarity into similar batches, and taking all the similar batch products as the basic production target of batch scheduling;
(2) the workshop data perception and disturbance event detection module is used for realizing the real-time acquisition of workshop 'man-machine-object-method-ring' state information based on a workshop disturbance event detection method of perception data fusion, transmitting the acquired information to a disturbance event processing module therein for disturbance factor judgment, processing the workshop state data in real time, and judging and classifying workshop equipment abnormity, personnel off duty, material defect, processing method error and safety accident, and transmitting the disturbance event to scheduling optimization processing to realize production dynamic evolution scheduling;
(3) the scheduling optimization processing module comprises an initialization pre-scheduling module and a dynamic evolution module, wherein the initialization pre-scheduling module is used for realizing multi-target multi-resource scheduling distribution, the maximum completion time and the minimum production cost are processing targets, the dependence conditions of processing equipment, personnel and a carrying trolley are comprehensively considered, the processing sequence of N similar batches of workpieces on M equipment, the processing distribution conditions of S workers on the M equipment and the distribution conditions of W carrying vehicles on the M equipment are determined, W and S are less than M, and a grey wolf optimization algorithm is used for solving the scheduling problem; and the dynamic evolution module realizes effective evolution scheduling optimization on different disturbance events.
3. A multi-variety small-batch multi-resource scheduling method based on digital twin is characterized by comprising the steps of order optimization processing, workshop data perception and disturbance event detection and scheduling optimization processing:
s1: performing order optimization processing, namely splitting a customer order according to an industry standard classification by using an order process similarity classification method based on fuzzy clustering analysis to obtain a total set of different products with minimum units, performing fuzzy clustering classification calculation on the products in the total set according to process similarity, merging the products with the same similarity into similar batches, and taking all the similar batches as basic production targets for batch scheduling;
s2: workshop data perception and disturbance event detection, wherein a workshop disturbance event detection method based on perception data fusion realizes real-time acquisition of workshop 'man-machine-object-method-ring' state information, transmits the acquired information to a disturbance event processing module for disturbance factor judgment, processes workshop state data in real time, and judges and classifies workshop equipment abnormity, personnel off duty, material defect, processing method error and safety accident, and transmits a disturbance event to scheduling optimization processing to realize production dynamic evolution scheduling;
s3: scheduling optimization processing, including an initialization pre-scheduling stage and a dynamic evolution stage; initializing a pre-dispatching model stage for realizing multi-target multi-resource dispatching distribution, taking minimized maximum completion time and minimized production cost as processing targets, comprehensively considering dependence conditions of processing equipment, personnel and a carrying trolley, determining the processing sequence of N similar batch workpieces on M equipment, the processing distribution conditions of S workers on M equipment and the distribution conditions of W carrying vehicles on M equipment, wherein W and S are less than M, and solving the dispatching problem by using a wolf optimization algorithm; and the dynamic evolution stage realizes effective evolution scheduling optimization on different disturbance events.
4. The order optimization process of claim 3, wherein: splitting the order into B customer orders according to the industry standard categories of the products to obtain a total set P containing n products, wherein PiIs the product of the ith category.
5. The order optimization process of claim 3, wherein: adopting a fuzzy analysis method to split orders, carrying out product process similarity analysis and calculation on products in a total set P containing n products, merging products with the same process similarity into similar batches to form a product set EP containing K similar batches, wherein the EPkThe method is characterized in that similar batch products of the kth batch are obtained, wherein K is less than or equal to n, and the fuzzy clustering analysis step comprises the following steps:
s11: forming basic data matrix by product process
Figure FDA0003261311230000031
m is the number of product processes;
s12: normalization using the maximum value: for the characteristic index matrix X*The j-th column element of (1) calculates the maximum value Mj＝max{x1j，x2j，...，cnjJ is 1, 2.. times.m, then transformed
Figure FDA0003261311230000032
S13: normalized data, using the Max-Min method
Figure FDA0003261311230000033
xil∈[0，1]1, 2, ·, n; i 1, 2.. m, a similar construction is performed such that the similarity matrix is R ═ R (R ═ R)ij)n×n，rij∈[0，1]；
S14: calculating a transfer packet of the fuzzy similarity matrix R when A is Ra＝R2aWhen (1 is more than or equal to l and less than or equal to n) is established for the first time, obtaining a fuzzy equivalent matrix A, and selecting a proper interception level lambda belonging to [0, 1 ]]And find the lambda intercept matrix A of Aλ(ii) a According to AλPerforming similar classification on the products to obtain a similar batch product set EP;
s15: similar batches of EPkAll product set P '═ { P'1，p′2,., b } average processing time TavgAs EPkThe processing time T of the batch scheduling is,
Figure FDA0003261311230000034
tp′cis the processing time of the c product in P ', sp'cThe amount of the c-th product in P'.
6. The method for plant data awareness and disturbance event detection according to claim 3, characterized in that the method comprises the following steps:
s21: training historical data and states of equipment operation process information, constructing an equipment fault classification model, extracting face features of employees, and constructing an employee face feature library;
s22: checking the marks of all the devices, and dividing the devices into a normal group and an abnormal group according to the marks;
s23: for normal group equipment, equipment fault prediction is carried out on equipment operation process data sensed in real time, whether equipment m faults exist is predicted, if the prediction result is that the equipment m is about to generate faults, a scheduling optimization processing module is informed, equipment fault evolution scheduling is carried out, constraint of the equipment m in scheduling is set to be 0, and the equipment m is marked to be abnormal;
s24: for the abnormal group equipment, equipment failure prediction is carried out on equipment operation process data sensed in real time, whether equipment m is recovered to be normal or not is predicted, if the prediction result is that the equipment m is recovered to be normal, a scheduling optimization module is informed, equipment failure recovery evolution scheduling is carried out, constraint of the equipment m in scheduling is set to be 1, and the equipment m is marked to be normal;
s25: checking all employee marks, and dividing the employees into on-duty groups and off-duty groups according to the marks;
s26: for the on-duty group, the face photos of the employees sensed in real time are subjected to feature FcExtracting and calculating feature data F in a feature librarypreAnd FcurIf V is greater than or equal to Vmax，Vmax→ 0, informing the scheduling optimization module to carry out staff off-Shift evolution scheduling, setting the staff S constraint in scheduling to be 0, and marking the staff S as off-Shift;
s27: for off-post group, performing feature F on employee face photos sensed in real timecExtracting and calculating feature data F in a feature librarypreAnd FcurIf V < VmaxValue VmaxAnd → 0, informing the scheduling optimization module to carry out the staff off duty and restore the evolution scheduling, setting the staff S constraint in the scheduling to be 1, and marking the staff S as on duty.
S28: checking the marks of all the materials, and dividing the materials into material defect and material defect recovery groups according to the marks;
s29: for the material defect group, the real-time perception of the quantity of the raw materials in the stock and the quantity of the products needing to be processed, such as the occurrence of the raw material PsiLess than the processed product PiThe number of the products is marked as material defect, the scheduling optimization module is informed of the material defect evolution scheduling, and the P in the scheduling task is reducediNumber of (value is Δ ═ P)i-Psi) And recording the defect value delta;
s210: for the material defect recovery group, the real-time perception of the quantity of the raw materials in the stock and the quantity of the products to be processed, such as the occurrence of the raw material PsiNot less than the number of the processed product PiThe number of the product is marked as material defect recovery, the scheduling optimization module is informed, and the material defect recovery is carried outRehearsal scheduling and increase P in scheduling taskiThe number of (value is Δ), the clear record defect value (Δ ═ 0);
s211: sensing and acquiring quality inspection equipment information in real time, and acquiring P when P is acquirediWhen the product is abnormal, finding out the corresponding processing equipment m and operator s, and marking the product PiInforming a scheduling optimization module of the error of the processing method, carrying out error evolution scheduling of the processing method, and removing the mark after scheduling processing is finished by the equipment m and the operator s;
s212: sensing and acquiring alarm equipment information of a field environment in real time, and finding out processing equipment m, operators s and processed products P corresponding to safety accidents when the alarm equipment information is acquirediAnd marking the product PiAnd the equipment m and the operator s are safety accidents, inform the scheduling optimization module to carry out safety accident evolution scheduling, and remove the mark after the scheduling processing is finished.
7. The mathematical model of the scheduling optimization process of claim 3, which is modeled by a mathematical programming method, and the specific implementation manner is as follows:
(1) constructing an objective function:
F＝min{αF1+(1-α)F2}，
wherein, F1＝max(Ci) It is indicated that the maximum time-out,
Figure FDA0003261311230000051
Figure FDA0003261311230000052
f2representing personnel and equipment energy costs, f3Indicating the cost of energy consumption of the carrier, F2Represents the total cost of the production process;
(2) adding a constraint condition:
Figure FDA0003261311230000053
only the previous process is carried out on the same productAfter the task processing is finished, the next procedure processing can be carried out;
Figure FDA0003261311230000054
in the same process, only one device can be selected for processing operation, and only one worker and one carrier can be selected for operation;
Cij-Cgh+H(1-cijk)+H(1-xghk)+H×yijghk≥tijk，
Cgh-Cij+H(1-cijk)+H(1-xghk)+H×(1-yijghk)≥tghkthe method indicates that the machining operations of the same equipment have a precedence relationship and cannot be performed simultaneously;
wherein, cijk∈{0，1}，cijks∈{0，1}，yijghk∈{0，1}，i，g＝1，2，...，N，j＝1，2，...ri，h＝1，2，...rgK is 1, 2.. the M, S is 1, 2.. the S, a ∈ (0, 1) random coefficient, CiRepresents the final processing completion time of the similar batch i, N represents the total batch number of workpieces of the similar batch to be processed in the production workshop, M represents the total number of processing equipment, S represents the total number of operators, riRepresents the total number of steps, O, of the workpiece iijJ-th working process, t, representing workpiece iijkRepresents the process OijMachining time in the machining apparatus k, CijksRepresents the process OijCost of working by workers s on the equipment k, EijksRepresents the process OijEnergy consumption for working by workers s on the equipment k, EijkwRepresents the process OijEnergy consumption, x, of transport by AGVw on equipment kijksRepresents the process OijMachining on the device k and by the operating worker s the value of the variable is 1, otherwise 0, xijkwRepresents the process OijProcessed on equipment k and carried by carrier w and having a variable value of 1, otherwise 0, yijghkRepresents the process OijWhether or not to take precedence over OghAnd executing on the device k, wherein if the variable value is 1, otherwise, the variable value is 0.
8. The dynamic scheduling policy for scheduling optimization processing according to claim 3, wherein the specific implementation manner is as follows:
machine fault evolution scheduling: acquiring all processing procedures and processing time of unprocessed workpieces assigned on the machine and information of unfinished procedures and processing time of workpieces to be processed as scheduling targets, taking a last scheduling scheme as an initial solution of a wolf optimization algorithm, and recording a scheduling scheme calculated by the algorithm;
and (3) personnel off-post evolution scheduling: searching all employee record distribution schemes with the same operation capacity as the employee from the historical distribution schemes, selecting the employee with the least processing amount as a substitute of the employee, and recording the current distribution scheme.
And (3) material defect evolution scheduling: all products related to defective raw materials are obtained, the number of unprocessed products is reduced, and the processing sequence of products following such products is advanced.
And (3) error evolution scheduling of a processing method: an operator is reminded urgently, processing parameters of a processing method and equipment are corrected, raw materials corresponding to the processing defective products are obtained, and the raw materials are inserted into production equipment temporarily for processing according to an original scheduling scheme;
safety accident evolution scheduling: and judging the health state of the staff involved in the safety accident, if the staff cannot stay on duty continuously, performing staff off duty scheduling, judging the running state of the related equipment, if a fault occurs, performing equipment fault scheduling, acquiring raw materials corresponding to the product involved in the product, and temporarily plugging the raw materials into production equipment for processing according to the original scheduling scheme.
CN202111073463.7A 2021-09-14 2021-09-14 Multi-variety small-batch multi-resource scheduling system and method based on digital twin Pending CN113850488A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111073463.7A CN113850488A (en) 2021-09-14 2021-09-14 Multi-variety small-batch multi-resource scheduling system and method based on digital twin

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111073463.7A CN113850488A (en) 2021-09-14 2021-09-14 Multi-variety small-batch multi-resource scheduling system and method based on digital twin

Publications (1)

Publication Number Publication Date
CN113850488A true CN113850488A (en) 2021-12-28

Family

ID=78974060

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111073463.7A Pending CN113850488A (en) 2021-09-14 2021-09-14 Multi-variety small-batch multi-resource scheduling system and method based on digital twin

Country Status (1)

Country Link
CN (1) CN113850488A (en)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114862283A (en) * 2022-07-05 2022-08-05 和尘自仪(嘉兴)科技有限公司 Quality inspection method and quality inspection device for machine tool machining
CN115208927A (en) * 2022-08-24 2022-10-18 成都秦川物联网科技股份有限公司 Industrial Internet of things for correcting and regulating defective products and control method thereof
CN115755822A (en) * 2023-01-05 2023-03-07 南京理工大学 Hydraulic pump assembly process self-adaptive control method based on process mark configuration
CN115857447A (en) * 2022-11-28 2023-03-28 安徽宝信信息科技有限公司 Complex industrial system operation monitoring method and system based on digital twins
CN115936377A (en) * 2022-12-13 2023-04-07 北京腾华宇航智能制造有限公司 Flexible job shop scheduling system
CN116300720A (en) * 2023-02-06 2023-06-23 广州辰创科技发展有限公司 Intelligent flexible scheduling advanced planning and scheduling system for production line
CN117047870A (en) * 2023-10-12 2023-11-14 四川省致链数字科技有限公司 Furniture punching procedure flexible matching system and method based on industrial Internet

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114862283A (en) * 2022-07-05 2022-08-05 和尘自仪(嘉兴)科技有限公司 Quality inspection method and quality inspection device for machine tool machining
CN115208927A (en) * 2022-08-24 2022-10-18 成都秦川物联网科技股份有限公司 Industrial Internet of things for correcting and regulating defective products and control method thereof
CN115208927B (en) * 2022-08-24 2022-12-06 成都秦川物联网科技股份有限公司 Industrial Internet of things for correcting, regulating and controlling defective products and control method thereof
US11921498B2 (en) 2022-08-24 2024-03-05 Chengdu Qinchuan Iot Technology Co., Ltd. Industrial internet of things (IoT) for correction and regulation of defective product, control methods and storage medium thereof
CN115857447A (en) * 2022-11-28 2023-03-28 安徽宝信信息科技有限公司 Complex industrial system operation monitoring method and system based on digital twins
CN115936377A (en) * 2022-12-13 2023-04-07 北京腾华宇航智能制造有限公司 Flexible job shop scheduling system
CN115755822A (en) * 2023-01-05 2023-03-07 南京理工大学 Hydraulic pump assembly process self-adaptive control method based on process mark configuration
CN116300720A (en) * 2023-02-06 2023-06-23 广州辰创科技发展有限公司 Intelligent flexible scheduling advanced planning and scheduling system for production line
CN117047870A (en) * 2023-10-12 2023-11-14 四川省致链数字科技有限公司 Furniture punching procedure flexible matching system and method based on industrial Internet
CN117047870B (en) * 2023-10-12 2023-12-22 四川省致链数字科技有限公司 Furniture punching procedure flexible matching system and method based on industrial Internet

Similar Documents

Publication Publication Date Title
CN113850488A (en) Multi-variety small-batch multi-resource scheduling system and method based on digital twin
Ding et al. RFID-based production data analysis in an IoT-enabled smart job-shop
CN106527373B (en) Workshop Autonomous Scheduling system and method based on multiple agent
CN101706886A (en) Order-driven Single-piece small-batch combined flow production method for processing workshop
CN112053082A (en) Material management system based on cloud computing
CN104781740A (en) Modular system for real-time evaluation and monitoring of machining production-line overall performances calculated from each given workpiece, tool and machine
CN113177761B (en) E-commerce storage intelligent scheduling early warning system considering timeliness
CN104657835A (en) Machining workshop scheduling and monitoring system and method
CN101364108B (en) Dynamic dispatching method for workshop working based on matter drive in complex large system environment
CN101830256B (en) Method and system for realizing balanced production of whole-automobile general assembly shop
CN111950939A (en) Material management method based on intelligent manufacturing
KR102419053B1 (en) System and method for integrated manufacturing operations
CN116843156A (en) Supply chain management and control method for assembled building components
CN112149866A (en) Intelligent manufacturing workshop anomaly prediction and control method based on edge cloud cooperation
CN115946132A (en) Intelligent dense warehouse and logistics robot system and fault monitoring method thereof
Yıldız et al. Integrating preventive and predictive maintenance policies with system dynamics: A decision table approach
Grabot et al. A decision support system for production activity control
CN116993052A (en) Intelligent factory production on-line monitoring analysis system based on digital twinning
CN101963804A (en) Management-control integrated flexible assembly line for mechanical products
CN114862122B (en) Workshop scheduling method, system and equipment based on APS
Mosavi et al. Intelligent energy management using data mining techniques at Bosch Car Multimedia Portugal facilities
CN114612005A (en) Glass factory digital intelligent system based on software as a service (SaaS) implementation
Zhang Revolutionizing Digital Marketing Industrial Internet of Things-enabled Supply Chain Management in Smart Manufacturing
CN111176174A (en) Multifunctional supervision system for stamping workshop
CN111160682A (en) Intelligent logistics transportation monitoring system for automobile parts

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