CN116224926A - Dynamic scheduling optimization method and device for single-piece small-batch flexible manufacturing workshops - Google Patents
Dynamic scheduling optimization method and device for single-piece small-batch flexible manufacturing workshops Download PDFInfo
- Publication number
- CN116224926A CN116224926A CN202211633961.7A CN202211633961A CN116224926A CN 116224926 A CN116224926 A CN 116224926A CN 202211633961 A CN202211633961 A CN 202211633961A CN 116224926 A CN116224926 A CN 116224926A
- Authority
- CN
- China
- Prior art keywords
- cell
- station
- state
- dynamic scheduling
- order
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 104
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 86
- 238000005457 optimization Methods 0.000 title claims abstract description 67
- 238000012545 processing Methods 0.000 claims abstract description 76
- 230000008569 process Effects 0.000 claims abstract description 41
- 230000008859 change Effects 0.000 claims abstract description 24
- 230000009471 action Effects 0.000 claims abstract description 19
- 210000004027 cell Anatomy 0.000 claims description 136
- 230000006399 behavior Effects 0.000 claims description 32
- 230000001413 cellular effect Effects 0.000 claims description 28
- 239000002245 particle Substances 0.000 claims description 24
- 238000003860 storage Methods 0.000 claims description 19
- 230000002068 genetic effect Effects 0.000 claims description 11
- 238000012544 monitoring process Methods 0.000 claims description 9
- 210000000352 storage cell Anatomy 0.000 claims description 9
- 238000009826 distribution Methods 0.000 claims description 8
- 239000002994 raw material Substances 0.000 claims description 8
- 230000000737 periodic effect Effects 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 4
- 238000005304 joining Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 abstract description 17
- 230000009467 reduction Effects 0.000 abstract 1
- 238000012384 transportation and delivery Methods 0.000 description 11
- 230000003068 static effect Effects 0.000 description 9
- 238000003754 machining Methods 0.000 description 7
- 238000004088 simulation Methods 0.000 description 5
- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Chemical compound OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 description 4
- 238000007726 management method Methods 0.000 description 4
- 238000012163 sequencing technique Methods 0.000 description 4
- 239000006185 dispersion Substances 0.000 description 3
- 230000002787 reinforcement Effects 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000011144 upstream manufacturing Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000010923 batch production Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 238000011143 downstream manufacturing Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- General Factory Administration (AREA)
Abstract
The invention relates to a dynamic scheduling optimization method and a device for a single-piece small batch flexible manufacturing shop, wherein the method comprises the following steps: according to the process route of the actual processing workpiece, a cell machine network model is established, and the initial state of each cell is obtained; according to the initial workable order selection of each station, traversing all possible first-step actions, and executing with different first-step actions: judging whether all orders are completed or not, if yes, ending, and obtaining a dispatching final solution; judging whether the production state is changed, if so, executing corresponding processing based on the state change type, and if not, acquiring next work by each station according to the current situation; updating the states of all cells based on a basic cell state evolution function; and obtaining scheduling final solutions corresponding to all the first-step actions, selecting an optimal solution, and controlling workshop production processes based on the optimal solution. Compared with the prior art, the method has the advantages of good robustness, reduction of the influence of dynamic events on workshop scheduling, fitting of actual production requirements of factories and the like.
Description
Technical Field
The invention relates to the field of scheduling of manufacturing workshops, in particular to a dynamic scheduling optimization method and device for a single-piece small-batch flexible manufacturing workshop.
Background
Along with the aggravation of market competition and the diversification of products, the traditional production mode is difficult to adapt to the modern production demands, so that the multi-variety and small-batch production mode becomes the mainstream production mode, and meanwhile, the flexible production mode which is different from the traditional large-scale quantitative production is continuously developed. For the scheduling method of the manufacturing shop, most of the existing researches start from ideal static production, the lowest cost and the fastest speed are pursued independently, the production and logistics scheduling are cut off, and dynamic uncertain events in actual production, such as abnormal operation of the shop, machine faults, emergency orders and the like, which bring a large number of disturbance events are ignored, so that even if the production plan with perfect data and excellent targets cannot be matched with actual production activities, and finally the normal implementation cannot be realized. In addition, centralized scheduling is found to be difficult to consider when actual project implementation, multiple constraints such as order delay, order punctual system and worker capacity, and thus the resulting scheduling results are not only difficult to respond in real time, but even not executable.
In the face of frequent production disturbance, students mostly consider centralized scheduling from two aspects of static scheduling and dynamic scheduling. The anti-interference capability of a scheduling scheme is improved by methods such as multipurpose predictive scheduling and robust scheduling in the aspect of static scheduling, but the scheduling mode is at the cost of losing certain production efficiency, and the real-time performance of scheduling and decision making is not well solved in a customization workshop with flexible scheduling and changeable environment, so that the application effect is not obvious; the multi-purpose rescheduling method in the aspect of dynamic scheduling takes events or periods and the like as driving events, but the method has the problems of slow abnormal response, poor decision real-time performance, low system stability and the like. Especially, aiming at the scheduling problem of flexible manufacturing workshops under the characteristics of multiple varieties and small batches, the scheduling method has the characteristics of multiple targets, multiple disturbance and the like, the problem is more complex, and the algorithm can not simultaneously meet the rapidity and the optimality.
Chinese application CN113761732a discloses a method for modeling and optimizing flexible scheduling in a multi-disturbance workshop based on reinforcement learning, which comprises the following steps: analyzing and inducing disturbance factors of production scheduling of a multi-disturbance workshop; abstracting a scheduling problem of a type of multi-disturbance workshops based on the idea of cellular machine modeling, summarizing abstract characteristics and operation mechanisms of a model, completing establishment of a double-layer cellular machine scheduling model, and constructing a double-layer cellular space of the cellular machine scheduling model; optimizing evolution rules of a multi-disturbance workshop cell machine scheduling model based on the thought of a reinforcement learning algorithm; and finally, establishing a simulation model system. The main scheduling targets of the method only consider the minimum work piece finishing time and the maximum average utilization rate of the same group of equipment, and the method can realize dynamic monitoring and has good robustness, but the method cuts off the production and logistics scheduling, is only applicable to the manufacturing scene with single function and fixed process route of the manufacturing unit, and is not applicable to the flexible workshop scheduling scene with various functions of the manufacturing unit.
Chinese application CN113570134a discloses a co-scheduling method for cell machines of large-scale equipment production and driving systems, the method comprising the steps of: constructing a grid model of a production scheduling cellular machine, and setting evolution rules; optimizing the evolution rule of the production scheduling cellular machine model; constructing a grid model of a travelling crane scheduling cellular machine according to actual travelling crane running conditions, and setting evolution rules; optimizing the driving dispatching evolution rule by means of a genetic algorithm; and designing a large-scale equipment manufacturing production and driving collaborative scheduling simulation system interface. According to the invention, the workpiece particles on the station required by the driving task are triggered after finishing the modification task, and return to the appointed home position to wait for the next task when other task instructions are not temporarily needed after the driving is finished and transported, so that the driving has strong working passivity and low working efficiency.
The literature 'large part flexible job shop scheduling algorithm based on a cell machine and an improved GA' aims at the problem of large part flexible job shop scheduling, adopts an improved genetic algorithm to optimize the local evolution rule of the cell machine, and provides a hybrid scheduling algorithm combining the cell machine and the improved genetic algorithm. According to the optimization targets of shortest total processing time, high load rate of each station and high load balance rate of each station of the same station group, a genetic algorithm optimization model of a single discretized static scheduling unit is established, and an optimization process is specifically described by combining a cost-effective example. But articles employ periodic rescheduling when designing the model rescheduling mechanism. Although periodic rescheduling can ensure that production has certain stability, it is not fast and sensitive to handle emergencies.
The prior art generally does not consider the complex processing route condition and the factory overall scheduling condition under the condition of multiple varieties and small batches. If the dynamic scheduling method adopting multi-main body scattered decision is considered, the total scheduling problem is subdivided into each production main body, and each main body autonomously selects a scheduling strategy according to the own operation characteristics, the response is sensitive, and the difference between different main bodies can be better expressed, but how to construct a production multi-main body autonomous decision frame and to cooperatively optimize the total strategy by utilizing the loose coupling relation between the main bodies is a difficulty of the scheduling method. In summary, the existing main technical difficulties of the dynamic scheduling dispersion decision-making method for the single-piece small batch flexible manufacturing workshops include: 1) Constructing a multi-main-body simulation model in a complex factory environment; 2) Constructing a multi-main-body dynamic scheduling decision relation; 3) Fast optimization of the scheduling scheme.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the dynamic scheduling optimization method and the device for the single-piece small-batch flexible manufacturing workshops, which have good robustness, reduce the influence of dynamic events on workshop scheduling results and meet the actual production requirements of factories.
The aim of the invention can be achieved by the following technical scheme:
the dynamic scheduling optimization method for the single-piece small-batch flexible manufacturing workshops is characterized by comprising the following steps of:
according to a process route of actually processing a workpiece, a cell machine network model is established, and the initial state of each cell is obtained, wherein the cells of the cell machine network model comprise cache cells, station cells, AGV logistics car cells and workpiece cells;
according to the initial workable order selection of each station, traversing all possible first-step actions, and executing the following steps with different first-step actions:
1) Judging whether all orders are completed, if yes, ending, obtaining a dispatching final solution, and if not, executing the step 2);
2) Judging whether the production state is changed, if so, executing corresponding processing based on the state change type, otherwise, executing the step 3), wherein the state change type comprises process interruption, emergency order joining and process order with process processing completion;
3) Each station obtains the next work according to the current situation;
4) Updating the states of all cells based on a basic cell state evolution function, and returning to the step 1);
and obtaining scheduling final solutions corresponding to all the first-step actions, selecting an optimal solution, and controlling workshop production processes based on the optimal solution.
Further, the cellular machine network model is a complex cellular machine network with mobile particles constructed by referring to the Petri network, and the processing order processed by each cellular is the mobile particles.
Further, when the state change type is a process interrupt, the corresponding process includes:
redefining all relevant cell states, and increasing the priority of the unprocessed order to the highest;
setting the current station state as a fault;
when the status change type is an emergency order entry, the corresponding processing includes:
the emergency order priority is raised to the highest level;
when the state change type is that the processing order has the working procedure, the corresponding processing is to start the logistics distribution flow, and the method specifically comprises the following steps:
and carrying out the behavior selection optimization of the AGV logistics trolley based on the loading condition of the AGV logistics trolley, the optimization target and the pre-constructed behavior candidate set until the transportation of all workpieces is completed.
Further, in the logistics distribution flow, the transport route of the AGV logistics car workshop related to multi-order carrying transport is obtained through optimization based on a genetic algorithm.
Further, each station obtains the next step of work according to the current situation specifically as follows:
and each workpiece performs behavior selection optimization through an optimization algorithm according to the optimization target and a pre-constructed behavior candidate set.
Further, the basic cell state evolution function is used for defining a law of time variation of the cell state, and is determined based on the current cell state, the relevant cell state and the decision behavior.
Further, the cell state is obtained based on various parameter constructions of each type of cell under different processing, behaviors and time.
Further, the cache cell comprises a raw material storage cell, a station line side storage cell, a workshop storage cell and a finished product storage cell.
Further, a periodic order polling mode is used to determine whether the production status has changed.
The invention also provides a dynamic scheduling optimization device for the single-piece small batch flexible manufacturing shop, which comprises the following steps:
the dynamic monitoring module is used for collecting real-time state data of the workshop;
and the dynamic scheduling optimization module is used for controlling the workshop production process by adopting the dynamic scheduling optimization method according to the workshop real-time state data.
Compared with the prior art, the invention has the following beneficial effects:
1) According to the invention, a complex cell network with mobile particles is defined for a large-scale flexible manufacturing workshop, the complex processing environment can be effectively abstracted and modeled, AGV logistics vehicles used for logistics are defined as logistics cells, and the overall scheduling optimization of workshop production and logistics can be realized by considering the scheduling scheme of the logistics in the workshop while the scheduling optimization is carried out.
2) The invention uses a multi-main body dispersion decision method to carry out workshop scheduling, subdivides the total scheduling problem into each production main body, and each production main body autonomously selects a scheduling strategy according to the own operation characteristics, has sensitive response and can better express the difference between different main bodies.
3) The invention can quickly carry out the production scheduling decision-making when facing to changeable production environments, effectively solves the influence of dynamic events on workshop scheduling results, has good robustness and has very strong resistance to external factors.
4) The invention considers various evolution rules and can select the optimal rule according to the actual factory to meet the actual production requirement of the factory.
Drawings
FIG. 1 is a schematic diagram of the overall architecture of the present invention;
FIG. 2 is a flow chart of the steps of designing the dynamic scheduling optimization module of the present invention;
FIG. 3 is a schematic diagram of a logical relationship among cells in the dynamic scheduling optimization module according to the present invention;
FIG. 4 is a flow chart of overall behavior selection of the dynamic scheduling optimization module of the present invention;
FIG. 5 is a flowchart illustrating a Step202 of the present invention for performing various emergency processes;
FIG. 6 is a flow chart of a logistics distribution behavior selection in accordance with the present invention;
fig. 7 is a station Gantt chart of an embodiment of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
The embodiment provides a dynamic scheduling optimization method for a single-piece small batch flexible manufacturing workshop, which comprises the following steps:
according to a process route of actually processing a workpiece, a cell machine network model is established, and the initial state of each cell is obtained, wherein the cells of the cell machine network model comprise cache cells, station cells, AGV logistics car cells and workpiece cells;
according to the initial workable order selection of each station, traversing all possible first-step actions, and executing the following steps with different first-step actions: 1) Judging whether all orders are completed, if yes, ending, obtaining a dispatching final solution, and if not, executing the step 2); 2) Judging whether the production state is changed or not by adopting a periodic order polling mode, if so, executing corresponding processing based on a state change type, and if not, executing a step 3), wherein the state change type comprises process interruption, emergency order joining and process order with process finishing; 3) Each station obtains the next work according to the current situation; 4) Updating the states of all cells based on a basic cell state evolution function, and returning to the step 1);
and obtaining scheduling final solutions corresponding to all the first-step actions, selecting an optimal solution, and controlling workshop production processes based on the optimal solution.
In the method, the cellular machine network model is a complex cellular machine network with mobile particles constructed by referring to the Petri network. The complex cellular network models stations and transport tools (such as AGVs) in workshops as "cells", and takes orders to be processed as a type of "moving particles", so that the orders processed by the stations and transport tools can be simulated through the particles contained in the cells.
In the above method, when the state change type is a process interruption, the corresponding process includes: redefining all relevant cell states, and increasing the priority of the unprocessed order to the highest; and setting the current station state as a fault. When the status change type is an emergency order entry, the corresponding processing includes: and the emergency order priority is raised to the highest. When the state change type is that the processing order has the working procedure, the corresponding processing is to start the logistics distribution flow, and the method specifically comprises the following steps: and carrying out the behavior selection optimization of the AGV logistics trolley based on the loading condition of the AGV logistics trolley, the optimization target and the pre-constructed behavior candidate set until the transportation of all workpieces is completed.
In the logistics distribution flow of the method, the workshop transportation route of the AGV logistics vehicle related to multi-order transportation is obtained through optimization based on a genetic algorithm.
In the method, each station acquires the next working step according to the current situation specifically as follows: and each workpiece performs behavior selection optimization through an optimization algorithm according to the optimization target and a pre-constructed behavior candidate set.
In the above method, the basic cell state evolution function is used to define a law of time variation of the cell state, and the basic cell state evolution function is determined based on the current cell state, the relevant cell state and the decision behavior. The cell state is obtained based on various parameter construction of each type of cell under different processing, behaviors and time.
In the method, the cache cells comprise raw material storage, station line side stock cells, workshop storage cells and finished product storage cells.
The above-described method, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Based on the dynamic scheduling optimization method, the embodiment also provides an optimization scheduling device for a single-piece small-batch flexible manufacturing workshop, which comprises a dynamic scheduling optimization module A and a dynamic monitoring module B, as shown in fig. 1, and the device is connected with a factory workshop system.
1) The dynamic scheduling optimization module comprises: a warehousing module A1, a manufacturing module A2, a transportation module A3, a periodic order polling module A4 and a global observer A5.
The storage module A1 comprises four types of raw material storage A1-1, station line side storage A1-2, workshop storage A1-3 and finished product storage A1-4. The raw material storage is mainly responsible for controlling the entry of orders, and making decisions such as entering factory processing of orders with urgent delivery period, delaying processing of orders with later delivery period and the like; the station line side stock is used for storing orders which can be processed at the current station and orders which are processed and not yet carried by AGV logistics vehicles; the workshop stores the order used for storing the wire side stock and not placing or the workable order of the wireless side stock station; the finished product warehouse is used for storing finished product orders which are processed and completed. The four storages all have a certain storage space, so that the storage capacity and the cost of the workshop need to be considered.
Each cell in the manufacturing module A2 corresponds to each station in actual machining, mainly completes various machining tasks of orders, and has extremely high independent selection capability. One station includes 1 or more devices and associated ancillary equipment and operators. Because factors which are extremely easy to ignore in the optimization operation of the process skills of personnel, the change time of processing centers with different processing capacities, the deviation of the processing personnel to the processing of the same type of orders and the like are found to be considered in the production of the actual factory inspection. Optimization indexes such as the capacity utilization rate, the order delay rate and the like are comprehensively considered in the device, and the production constraint mentioned above is considered, so that the obtained result is suitable for actual production requirements.
The transportation module A3 is a logistics transportation tool and is responsible for selecting and transporting processed orders in a workshop to finish the circulation of workpieces. The transport module A3 comprises a plurality of AGV logistics vehicles, each having a fixed carrying volume, capable of carrying a plurality of orders at a time, thus involving a path planning problem for the trolley and an autonomous decision whether to carry the completed order.
The periodic order polling module A4 filters all orders which are not processed in the workshop according to the actual needs of the factory and sets a specific time, and selects orders which meet the processing requirements according to the conditions of raw materials in the warehouse, the stock conditions of workshop products, the emergency degree of the order delivery period and the like, and enters the workshop processing flow.
The global observer A5 is equivalent to workshop overall management, and is used for sorting all hardware availability, real-time positions of the trolley, processing states of all orders, storage states of storage units and residual available processing time of stations according to sensor signals acquired by the dynamic monitoring module B.
2) The dynamic monitoring module B comprises a sensor module B1, a UWB positioning module B2 and a production real-time management module B3.
The sensor module B1 is used for monitoring the states of the manufacturing station and the AGV logistics car, and generally comprises sensors such as temperature, humidity and vibration, and the availability of the station and the logistics car is determined by monitoring signals.
The UWB positioning module B2 is used for positioning the products, the products and the AGV logistics car and comprises a positioning base station and a positioning tag. Each AGV logistics vehicle is provided with 1 positioning tag, each tray of the large part or the small part is provided with a positioning tag, the tag emits pulses according to a certain frequency, and the distance measurement is carried out with a base station with a known position, so that the position of each object is determined.
The production real-time management module B3 is used for feeding back the processing condition of workers and the temporary condition of workshops in real time.
The dynamic scheduling optimization module in the device utilizes the dynamic scheduling optimization method to realize the dynamic scheduling dispersion decision optimization for the single-piece small-batch flexible manufacturing workshops.
The dynamic scheduling optimization method is realized based on a pre-established cellular machine network model, and the construction and evolution rule design process of the cellular machine network model is shown in fig. 2, and comprises the following steps:
step101: establishing a network model of a cellular machine
Aiming at the complex customization order production condition of workshops, the workshop scheduling optimization module structure needs to be designed. The embodiment designs a complex cellular machine network with mobile particles by referring to the form of the Petri network. The logical relationship of cells as shown in fig. 3 is formed by combining the modules in fig. 1 according to the process route definition of the actual processing workpiece.
Taking the order No. 1 as an example, the initial processing station is correspondingly a station cell C, the process route is a station C- > a station B- > a station D ", and the processing is finally completed, so in the complex cellular network, for the case of the order No. 1, the station B is a downstream machine of the station C; in contrast, station C is the machine upstream of station B. The order firstly meets the condition that the processing can be started at the current moment, is sent out from the raw material warehouse A1-1, and then enters the line side stock C corresponding to the corresponding first procedure to wait; after the station cell C finishes processing, putting the station cell C into a corresponding line side library again for temporary storage, and temporarily storing the station cell C at a workshop storage A1-3 after the line Bian Ku is fully filled; the AGV logistics vehicle polls in a workshop, and autonomously selects proper order parts to transport to a downstream station according to the part positions given by the UWB positioning system B2 and the order part states given by the B3 production management system under the condition that the self-transport capacity limit is not exceeded, and the AGV logistics vehicle can support vehicle multi-order use path planning to select proper routes; and finishing all processing tasks of the order number 1 by reciprocating, and finally sending to the 'finished product stock A1-4'.
It should be noted that, according to the different product process routes in different orders, the logical relationship between the cells is also different. Multiple orders are processed simultaneously by one shop, so multiple logic relations are formed in the dynamic scheduling optimization module A simultaneously, and another logic relation is shown by an order number 2 (shown as 2) in FIG. 3.
Step102: setting basic state evolution function of cell
The workshop scheduling optimization module consists of various cell machines in each sub-module, and the states and evolution rules of different cells are different due to the different functionalities of the cell machines, but the overall implementation logic is as shown in figure 3 and all are based on cell state functions
Based on>
Representing the state of cell i at time t, < >>
Representing the state of all cell groups N associated with cell i at time t. The evolution function defines the law of time change of the cell state, including the current state and the relevant cell state and decision behavior, and describesThe relationship of cells and other cells in the cellular machine network designed in step1 is described.
1) Any cache cell (bit line Bian Ku cell) Y 1 State at time t+ 1
F is a local state transition rule, namely a job scheduling rule; cache cell
State at time t+1
By cache cells->
Upstream all station cells->
And its corresponding cache cell>
State set at time t and all downstream station cells->
And its corresponding cache cell>
The state set at time t is determined.
2) At time t, any station cell Y 2 The state at time t+1 is expressed as:
station cell
State at time t+1->
Cache cell for its upstream process +.>
State at time t->
Cache cell of the downstream process>
State at time t->
And station cell->
And its cache cell->
The state at time t is determined.
3) Arbitrary AGV logistics vehicle logistics cell Y at time t 3 The state at time t+1 is expressed as:
AGV logistics vehicle logistics cell Y 3 State at time t+1
By a set of cache cells in the workshop>
State at time t->
And Y is equal to 3 State at time t->
And (3) determining.
Step103: setting basic state function of cell
The state function of each type of cell is to determine and describe various parameters of a certain type of cell under different processing, behavior and time states according to its service object and function, and is essentially a mathematical expression of the cell corresponding to the actual processing element, and is defined after analyzing its functional elements, namely each Step102
Is defined in (a).
1)
For caching cell Y 1 The state at time t can be expressed as:
2)
Station cell Y 2 The state at time t can be expressed as:
t is the total time available for processing in one day, namely shift; s is(s) s In the busy and idle state of the station, the dynamic attribute s s E {0,1,2},0 free, 1 busy, 2 failed; s is(s) co The total processing capacity of the station, namely the load capacity of the station, the dynamic attribute, is equal to the sum of the processing capacity values required by the processed and processed workpieces in the queue of corresponding buffer cells and the workpieces to be processed.
s cl Residual processing capability, dynamic property, s of station cells cl ＝T-s co 。
3)
AGV logistics vehicle cell Y 3 The state at time t can be expressed as:
v is the running speed of the AGV logistics vehicle cell; s is(s) as For AGV logistics vehicle busy and idle state, dynamic attribute, s as E {0,1,2},0 free, 1 busy, 2 failed; s is(s) an The current position of the AGV logistics vehicle is provided; s is(s) as The position to be reached for the next action of the AGV logistics vehicle; s is(s) av The carrying volume of the AGV logistics vehicle is provided.
4)
Workpiece particles Y 4 The state at time t can be expressed as:
p t the total number of working procedures required for processing the particles is static attribute; p is p f The number of procedures completed by the particle is the dynamic attribute; np is the next process number of the particle, the dynamic attribute; s is(s) n Space occupied by the particles, static attribute, corresponding to buffer area
S of AGV logistics vehicle cell av The method comprises the steps of carrying out a first treatment on the surface of the dp is the processing priority of the workpiece, and the static attribute is determined by the delivery period of the workpiece and the importance degree of the order user, and the closer the delivery period is, the higher the priority is; endt is the time of the particle exchange period and the static attribute; t is t a The time for reaching the cells is divided into time for reaching the cache cells, namely time for starting queuing, and time for reaching the station cells, namely time for starting processing, and dynamic properties.
Step104: determining cellular machine behavior
According to the definition of the current state decision behavior, various heuristic methods can be defined for the station order production and the transportation of AGV logistics vehicles as different actions owned by a cell machine, and the following methods are determined in the embodiment without losing generality:
station cell machine behavior candidate set:
(1) Act a1, selecting a part for machining according to First Come First Served (FCFS);
(2) A2, selecting a part to process according to the shortest processing time first (SPF);
(3) Act a3, selecting the part to process according to the longest processing time first processing (LPF);
(4) Act a4, selecting a part for machining according to a minimum delivery date (EPF);
(5) Act a5, selecting a part for machining according to a Priority (PF);
(6) Act a6, not selecting any job.
AGV logistics car behavior candidate set:
(1) Act a1, selecting a part according to First Come First Served (FCFS);
(2) Act a2 of selecting parts based on shortest shipping time first delivery (SPF);
(3) Act a3, firstly distributing (LPF) the selected parts according to the longest transportation time;
(4) Act a4, selecting a part according to minimum delivery date (EPF);
(5) Act a5, selecting a part according to a Priority (PF);
(6) Act a6, not selecting any part delivery.
Step105: determining model self-organizing evolution rules
This Step defines how to perform the selection of the defined behavior and the evolution rules of the model, which together determine the evolution function f described in Step 102.
1.5.1 model evolution rule set
The self-organizing evolution rules of the model are summarized into five according to the dynamic scheduling characteristics of the flexible job shop, and are shown in table 1.
TABLE 1 model evolution rules
The evolution process specifically comprises:
order selection: order selection rule R sb→bs : the part particles to be processed are selected by the raw material storage cell machine according to the priority of the stored part particles, the time required for the shortest processing, and the time of the exchange period. However, since the factory is set to send special logistics personnel for the situation, the logistics situation of the situation does not need to be considered.
Selecting a workpiece: rule R for selecting workpieces bs→s : determining the next selection of a station by combining station cells with greedy algorithm and processing priority of workpiecesFirstly, sequencing according to the priority of the workpieces, wherein the higher the priority is, the more front the priority is; then to t a Sequencing, t a The larger the row is, the earlier, i.e. the processing is performed first; and finally, aiming at the processing condition of the current station, if the selected order can be inserted into the current scheduling plan to increase the productivity of the station, the selected order is lifted to the first position of the queue for processing, otherwise, the first workpiece of the current queue is selected for processing by combining with the FCFS (first come first get) principle.
Transport selection: transport particle selection rule R bs→a : the particles to be transported are selected by the transport particles in combination with the FCFS (first come) principle, first according to t a Sequencing, t a The larger the row is in front; the priority of the workpieces is then ordered for t a The same priority is higher in the front; and finally, sequencing according to the intersection time of the workpieces.
Triggering a processing task: machining task trigger rule R ls Two conditions need to be satisfied: 1) Target station cell idle s s =0; 2) When the condition is met, the station enters a target station, and the target station is busy s s =1, the buffer cell queue length lq=lq-1, generating a start processing time, while updating t q After the processing is finished, the workpiece is placed in the current working procedure to be processed, and is placed in an online side stock to wait for AGV logistics vehicle transportation.
Triggering a transportation task: transportation task trigger rule R la Three price adjustments need to be satisfied: 1) Target AGV logistics vehicle idle s os =0; 2) The residual transport space of the target AGV logistics vehicle is larger than the space required by the workpiece; 3) When the condition is met, the AGV logistics vehicle starts to transport, and if the AGV logistics vehicle has no transport space, the AGV logistics vehicle is placed in busy s as =1, the space occupied by the current transport order is subtracted from the remaining transport space, and the transport is carried to the next processing station line side library of the workpiece particles, and the particles t are updated at the same time a Line Bian Ku lq=lq+1; and if the machining is completed completely, transporting the workpiece to a finished product warehouse.
1.5.2 cellular machine behavior selection
In the aspect of behavior selection, various optimization targets can be selected for optimization according to actual problems, such as linear programming, genetic algorithm, particle swarm algorithm and the like. Taking order waiting time and station changing times as optimization targets, the patent introduces an example of algorithm solving by using a greedy algorithm. The present example considers two optimization objectives, namely order waiting time and minimizing the number of station changes. The two objectives are specifically described as follows:
(1) Order waiting time
The goal is to take into account the work in process inventory situation, measured in part waiting times.
First define a define state function μ for part state k (t), i.e
r k Representing order wait time at the kth decision time:
t k and t k-1 Represents k and k-1 decision moments, r k Indicated at time t k The length of time for which part i waits for processing.
(2) Minimizing station change times
The goal is to consider the worker's work load, measured in terms of the number of station changes.
Firstly, defining a transformation state theta of two decisions before and after station cell k (t), i.e
Whether the shape of the machined part is changed needs to be judged whether the shape of the machined part is similar or not according to the decision made at the time t and the time t-1, and if not, the shape needs to be changed.
r k Representing the kth decision timeIs a total number of times of the transformation:
wherein n represents the number of parts, t k And t k-1 Represents k and k-1 decision moments, r k Indicating that the system is at time t k Total number of changes.
After the cellular machine network model and evolution regularization thereof are established, the dynamic scheduling optimization module can realize decision optimization of dynamic scheduling according to the real-time sensing signals, and the whole flow is shown in fig. 4, and comprises the following steps:
step201: and writing an initial state of a cell machine according to actual conditions of a factory, selecting according to initial processable orders of each station, and traversing all possible first-step actions.
Step202: judging whether all orders in the system are completed or not, if yes, judging whether the current state is changed or not; the state change includes three cases, namely, a process interruption Step31, an emergency order addition Step32, and a process order process completion Step33, which are caused by a station failure or a sudden request of a worker, as shown in fig. 5.
The process interruption Step31 specifically includes:
step311: notifying the system of interrupt reasons, redefining all relevant cell states, and then, raising the priority of an unprocessed order to the highest waiting position to process the order normally, and then, processing the order first;
step312: and setting the current station state as a fault, and suspending processing of all orders in the line side library.
The emergency order entry Step32 specifically includes:
step321: and feeding the workpiece into a station line edge area corresponding to the first working procedure and placing the workpiece at the highest position of a waiting queue.
As shown in fig. 6, the processing order has a process processing completion Step33 specifically including:
step331: entering a logistics distribution flow;
step3311: judging whether the logistics trolley has a transportation space or not, and if no workpiece is kept in the current state, entering into waiting;
step3312: when the order processing is completed in the line side library and waiting for transportation, carrying out order selection transportation according to a greedy strategy and the self loading condition;
step3313: for multi-order delivery, the selection of workshop delivery routes is involved, and the genetic algorithm is selected for route optimization;
step3314: judging whether an undelivered order exists in the current workshop, if so, entering Step2-3011, and if not, entering Step3315;
step3315: the trolley enters a waiting state.
Step203: and according to the selection result of Step202, different emergency situations are treated.
Step204: each station performs the next step of work according to the current situation and according to the behavior selection optimization, and various optimization strategies such as genetic algorithm, reinforcement learning and the like can be selected in the next step, and the greedy algorithm is taken as an example only as one method of the behavior selection in the patent.
Step205: and (3) pushing simulation at the time t+ 1, determining a system state St+1 according to the action selection result, and updating all cell states in the system.
Step206: and selecting an optimal solution according to the final solutions caused by different first-step selections.
The dynamic scheduling optimization method and the device for the single-piece small-batch flexible manufacturing workshops can realize the optimization of dynamic scheduling decentralized decision, a software-hardware combination mode is adopted, a hardware module dynamically acquires real-time environment changes under the condition of real-time monitoring of a factory, and the software module analyzes real-time data of the factory to obtain a dynamic scheduling result. Wherein the software module implementation comprises the steps of: 1) Constructing a complex cellular network model aiming at a single small batch of flexible manufacturing workshops; 2) Establishing basic state evolution functions and basic state functions of each cell by combining actual requirements of a factory; 3) And determining the behavior and evolution method of the cellular machine. Finally, the production scheduling decision can be rapidly carried out when facing to changeable production environments, the robustness is good, and the dynamic scheduling is realized.
Examples
Taking 8 orders and 8 stations of an oil cylinder manufacturer and 1 AGV logistics trolley as examples, the feasibility and the effectiveness of the algorithm are verified. Table 2 is the parameter index of the order, and table 3 is the parameter index of each station.
Table 2 processing parameter index for each order
Table 3 parameter index of each station
| |
|
|
|
|
|
|
|
|
| |
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| |
0 | 1 | 2 | 3 | 4 | 5 | 6 | |
| |
0 | 1 | 2 | 3 | 4 | 5 | ||
| |
0 | 1 | 2 | 3 | 4 | |||
| |
0 | 1 | 2 | 3 | ||||
| |
0 | 1 | 2 | |||||
| |
0 | 1 | ||||||
| |
0 |
Table 4 shift table for each station
| |
|
|
|
|
|
|
|
|
| |
1 | 3 | 2 | 3 | 2 | 3 | 2 | 2 |
The state attributes of each station cell are constructed based on steps 101 to 105, and written as table tags as shown in table 5. Dynamic scheduling optimization is then achieved based on the following steps:
s201: first, the initial state of each cell unit is generated according to the production order. As the case may be, the state table is as follows, where T represents the total length of time each station cell can process per day, s s Representing the current cell state, s co Representing the length of time the current cell has been processed, s cl And (3) representing the order sequence processed or being processed by the current cell, wherein Cs represents the buffer cell buffer queue corresponding to the current cell, namely the order sequence can be processed.
TABLE 5 initial State Properties of each cellular machine
S202: for initialization state selection, the first step action has 6 choices, taking the following two examples:
TABLE 6 State Properties after the first step of decision for each cell machine case 1
TABLE 7 State Properties after the first step of decision for each cell machine case 2
The subsequent step takes case 2 with the optimal final result as an example to continue the expansion.
S203: after 50 minutes there is a process completion, the cell status is shown in the table below, with the bar indicating that the order has been processed, and S204 is entered, but there is no AGV to transport it to the next process.
Table 8 first State change State Properties of each cell machine
S204 and S205 are performed here simultaneously:
in S205, each station performs action selection according to a greedy algorithm, and picks the next processing order for processing.
In S204, the current AGV logistics trolley selects the workpiece 6 and the workpiece 5 for transportation according to its own idle condition and the total time required for workpiece processing. The transport path should be from station 1 to station 5 to station 7, based on genetic algorithm. Assuming that the trolley travel speed is 1m/min, the moment when the workpiece 5 arrives at the station 5 should be 54 th minute of starting operation, and the moment when the workpiece 6 arrives at the station 7 should be 56 th minute of starting operation.
S206: and (3) pushing simulation at the time t+1 according to the action selection, and updating all cell states in the system.
TABLE 9 first State change State Properties for each cell machine
And continuing to simulate the propulsion system according to the flow, and when the state of the second system changes, the state of each cell is shown by the following index.
Table 10 second time State change State Properties of each cell machine
In the illustrated example, an AGV is used for transport.
S207: after all orders are processed, a plurality of solutions exist, and the optimal solution is selected to obtain a Gantt chart of the system as shown in FIG. 7.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (10)
1. The dynamic scheduling optimization method for the single-piece small-batch flexible manufacturing workshops is characterized by comprising the following steps of:
according to a process route of actually processing a workpiece, a cell machine network model is established, and the initial state of each cell is obtained, wherein the cells of the cell machine network model comprise cache cells, station cells, AGV logistics car cells and workpiece cells;
according to the initial workable order selection of each station, traversing all possible first-step actions, and executing the following steps with different first-step actions:
1) Judging whether all orders are completed, if yes, ending, obtaining a dispatching final solution, and if not, executing the step 2);
2) Judging whether the production state is changed, if so, executing corresponding processing based on the state change type, otherwise, executing the step 3), wherein the state change type comprises process interruption, emergency order joining and process order with process processing completion;
3) Each station obtains the next work according to the current situation;
4) Updating the states of all cells based on a basic cell state evolution function, and returning to the step 1);
and obtaining scheduling final solutions corresponding to all the first-step actions, selecting an optimal solution, and controlling workshop production processes based on the optimal solution.
2. The dynamic scheduling optimization method for a single-piece small batch flexible manufacturing shop according to claim 1, wherein the cellular machine network model is a complex cellular machine network with mobile particles constructed by referring to the Petri net shape, and the processing order processed by each cellular is the mobile particles.
3. The method for optimizing dynamic scheduling for a single-piece small lot flexible manufacturing plant according to claim 1, wherein when the state change type is a process interrupt, the corresponding process includes:
redefining all relevant cell states, and increasing the priority of the unprocessed order to the highest;
setting the current station state as a fault;
when the status change type is an emergency order entry, the corresponding processing includes:
the emergency order priority is raised to the highest level;
when the state change type is that the processing order has the working procedure, the corresponding processing is to start the logistics distribution flow, and the method specifically comprises the following steps:
and carrying out the behavior selection optimization of the AGV logistics trolley based on the loading condition of the AGV logistics trolley, the optimization target and the pre-constructed behavior candidate set until the transportation of all workpieces is completed.
4. The method for optimizing dynamic scheduling of single-piece small-lot flexible manufacturing workshops according to claim 3, wherein in the logistics distribution flow, a workshop transportation route of the AGV logistics vehicle related to multi-order transportation is optimized and obtained based on a genetic algorithm.
5. The dynamic scheduling optimization method for a single-piece small batch flexible manufacturing shop according to claim 1, wherein the steps of each station obtaining the next step of work according to the current situation are specifically as follows:
and each workpiece performs behavior selection optimization through an optimization algorithm according to the optimization target and a pre-constructed behavior candidate set.
6. The method for optimizing dynamic scheduling in a single-piece small lot flexible manufacturing shop according to claim 1, wherein the basic cell state evolution function is used for defining a law of change of a cell state with time, and the basic cell state evolution function is determined based on a current cell state, related cell states and decision behaviors.
7. The method for optimizing dynamic scheduling of a single-piece small batch flexible manufacturing shop according to claim 6, wherein the cell state is obtained based on multiple parameter constructions of each type of cell under different processing, behaviors and times.
8. The method for optimizing dynamic scheduling for a single-piece small lot flexible manufacturing plant according to claim 1, wherein the cache cells comprise raw material storage, station line side stock cells, plant storage cells and finished product storage cells.
9. The method for optimizing dynamic scheduling for a single-piece small lot flexible manufacturing plant according to claim 1, wherein a periodic order polling mode is adopted to judge whether the production state is changed.
10. A dynamic scheduling optimization device for a single-piece small-lot flexible manufacturing shop, comprising:
the dynamic monitoring module is used for collecting real-time state data of the workshop;
and the dynamic scheduling optimization module is used for controlling the workshop production process by adopting the dynamic scheduling optimization method according to any one of claims 1-9 according to the workshop real-time state data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211633961.7A CN116224926A (en) | 2022-12-19 | 2022-12-19 | Dynamic scheduling optimization method and device for single-piece small-batch flexible manufacturing workshops |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211633961.7A CN116224926A (en) | 2022-12-19 | 2022-12-19 | Dynamic scheduling optimization method and device for single-piece small-batch flexible manufacturing workshops |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116224926A true CN116224926A (en) | 2023-06-06 |
Family
ID=86583272
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211633961.7A Pending CN116224926A (en) | 2022-12-19 | 2022-12-19 | Dynamic scheduling optimization method and device for single-piece small-batch flexible manufacturing workshops |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116224926A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116820060A (en) * | 2023-08-31 | 2023-09-29 | 超网实业(成都)股份有限公司 | Energy-saving strategy optimization design method and system for factory workshop |
| CN117891224A (en) * | 2024-03-15 | 2024-04-16 | 山东国泰民安玻璃科技有限公司 | Production control method, equipment and medium for oral liquid bottle |
| CN117950381A (en) * | 2024-03-26 | 2024-04-30 | 深圳市永迦电子科技有限公司 | Digital production and manufacturing management system |
| CN117891224B (en) * | 2024-03-15 | 2024-05-24 | 山东国泰民安玻璃科技有限公司 | Production control method, equipment and medium for oral liquid bottle |
-
2022
- 2022-12-19 CN CN202211633961.7A patent/CN116224926A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116820060A (en) * | 2023-08-31 | 2023-09-29 | 超网实业(成都)股份有限公司 | Energy-saving strategy optimization design method and system for factory workshop |
| CN116820060B (en) * | 2023-08-31 | 2023-12-05 | 超网实业(成都)股份有限公司 | Energy-saving strategy optimization design method and system for factory workshop |
| CN117891224A (en) * | 2024-03-15 | 2024-04-16 | 山东国泰民安玻璃科技有限公司 | Production control method, equipment and medium for oral liquid bottle |
| CN117891224B (en) * | 2024-03-15 | 2024-05-24 | 山东国泰民安玻璃科技有限公司 | Production control method, equipment and medium for oral liquid bottle |
| CN117950381A (en) * | 2024-03-26 | 2024-04-30 | 深圳市永迦电子科技有限公司 | Digital production and manufacturing management system |
| CN117950381B (en) * | 2024-03-26 | 2024-05-28 | 深圳市永迦电子科技有限公司 | Digital production and manufacturing management system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107036618A (en) | A kind of AGV paths planning methods based on shortest path depth optimization algorithm | |
| Xie et al. | Simulation and experimental design methods for job shop scheduling with material handling: a survey | |
| Fazlollahtabar et al. | Autonomous guided vehicles | |
| CN111932002B (en) | Manufacturing workshop production material predictive distribution method based on edge intelligence | |
| Bolu et al. | Path planning for multiple mobile robots in smart warehouse | |
| CN113359702B (en) | Intelligent warehouse AGV operation optimization scheduling method based on water wave optimization-tabu search | |
| Yang et al. | Modelling and production configuration optimization for an assembly shop | |
| Xu et al. | A dynamic scheduling method for logistics tasks oriented to intelligent manufacturing workshop | |
| Tehrani Nik Nejad et al. | Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system | |
| Heger et al. | Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules | |
| CN116224926A (en) | Dynamic scheduling optimization method and device for single-piece small-batch flexible manufacturing workshops | |
| Wang et al. | Multi-objective optimisation of automated guided dispatching and vehicle routing system | |
| Zhou et al. | Multi-objective optimization of greening scheduling problems of part feeding for mixed model assembly lines based on the robotic mobile fulfillment system | |
| Herps et al. | A simulation-based approach to design an automated high-mix low-volume manufacturing system | |
| Wang et al. | Digital-twin based real-time resource allocation for hull parts picking and processing | |
| Qu et al. | Internet-of-Things-based just-in-time milk-run logistics routing system | |
| Liu et al. | A new knowledge-guided multi-objective optimisation for the multi-AGV dispatching problem in dynamic production environments | |
| Saboor et al. | Flexible cell formation and scheduling of robotics coordinated dynamic cellular manufacturing system: A gateway to Industry 4.0 | |
| CN117611029A (en) | Process simulation method and system for warehouse logistics system | |
| Rudnik | Transport trolley control in a manufacturing system using simulation with the FSAW, FWASPAS and FTOPSIS methods | |
| Mehrsai et al. | Toward learning autonomous pallets by using fuzzy rules, applied in a Conwip system | |
| WO2019155589A1 (en) | Transport operation control device, transport system, transport operation control method, and recording medium | |
| Shakeri et al. | An efficient incremental evaluation function for optimizing truck scheduling in a resource-constrained crossdock using metaheuristics | |
| Zhou et al. | Dynamic cyclic kitting part-feeding scheduling for mixed-model assembly line by a hybrid quantum-behaved particle swarm optimization | |
| Iwamura et al. | A study on real-time scheduling for autonomous distributed manufacturing systems |
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 |