CN108647914A - Production scheduling method and device, computer equipment and storage medium - Google Patents
Production scheduling method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN108647914A CN108647914A CN201810288929.7A CN201810288929A CN108647914A CN 108647914 A CN108647914 A CN 108647914A CN 201810288929 A CN201810288929 A CN 201810288929A CN 108647914 A CN108647914 A CN 108647914A
- Authority
- CN
- China
- Prior art keywords
- factory
- solution
- global
- global solution
- production
- 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.)
- Granted
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 163
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000003860 storage Methods 0.000 title claims abstract description 11
- 238000010845 search algorithm Methods 0.000 claims abstract description 54
- 230000006870 function Effects 0.000 claims description 79
- 238000004590 computer program Methods 0.000 claims description 24
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 abstract description 8
- 230000000694 effects Effects 0.000 abstract description 5
- 239000002699 waste material Substances 0.000 abstract description 5
- 238000005457 optimization Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000003786 synthesis reaction Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000000996 additive effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 229910002056 binary alloy Inorganic materials 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 235000013406 prebiotics Nutrition 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06Q10/0633—Workflow analysis
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to a production scheduling method, a production scheduling device, computer equipment and a storage medium. The method comprises the following steps: acquiring customer order information; analyzing the customer order information through a preset tabu search algorithm to obtain a plurality of global solutions corresponding to the customer order information, wherein the number of target parameters of the global solutions is at least 2; searching a global solution with the minimum difference between target parameters in the global solution, and taking the global solution with the minimum difference between the target parameters as an optimal solution; and performing production scheduling according to the optimal solution. According to the production scheduling method, the global solution of the production scheduling corresponding to the customer order is solved by the preset tabu search algorithm, the target parameter of the global solution is not less than 2, then the global solution with the minimum target parameter difference in the global solution, namely the optimal solution, is obtained through the balancer, the production scheduling is carried out according to the optimal solution, waste generated in the production scheduling process is reduced, and the effect of carrying out the optimal production scheduling under the condition of comprehensively considering various production scheduling targets can be achieved.
Description
Technical field
This application involves supply chain production technical fields, are set more particularly to a kind of production scheduling method, apparatus, computer
Standby and storage medium.
Background technology
The make-to-order production that MTO (Make To Order, by single manufacture) refers in supply chain production field, that is to say, that
Enterprise is to carry out scheduling of production according to order, rather than according to the market demand, so there will be no extra inventory, how many
Order just arranges some factory specifically to produce.Production scheduling refers to then the distribution to order, and general enterprises have multiple factories and are
It is manufactured, and different factory cost valences, address, production capacity, producing line have differences, therefore, from entreprise cost and essence
For the angle of prebiotic production, be more desirable under the premise of the cooperation between maintaining each factory so that the production cost of enterprise and
Customer satisfaction highest.It is the specific business according to supply chain first in the production scheduling of current supply chain modes or system
Flow (such as MTO, MTR), design one enable the schedule scheme that production task is timely completed customer demand.Such as M factory
Current production status is uploaded respectively, and work is not being influenced according to actual sales order by the order management personnel of supply chain modes
New order is added in the production scheduling of factory under the premise of factory is planned, then new task is issued into each factory.This
Process can take into account cost or production capacity factor and factory to the production qualification of some concrete model, to one of them
Scheduling of production is carried out for target.
Most enterprises want to meet following several demands simultaneously：1, cost minimization；2, factory every producing line
Production capacity is all called as far as possible, as far as possible so that capacity loss is minimum；3, the geographical location of factory and factory's order is distributed to
Client place of receipt it is close as far as possible, as far as possible so that transportation cost it is low.But current program system can only meet 1 or 1
With 2, that is, there are problems that waste of resource.
Invention content
Based on this, it is necessary in view of the above technical problems, provide a kind of life that can meet a variety of production scheduling demands simultaneously
Produce scheduling method, device, computer equipment and storage medium.
A kind of production scheduling method, including step：
Obtain customer order information；
The customer order information is parsed by default tabu search algorithm, obtains the customer order information pair
The multiple global solutions answered, the number of the target component of the global solution are at least 2；
Search the global solution of difference minimum between target component in the global solution, and by difference between the target component
Minimum global solution is as optimal solution；
Production scheduling is carried out according to the optimal solution.
The global solution for searching difference minimum between target component in the global solution in one of the embodiments,
And the global solution of difference minimum between the target component is specifically included as the step of optimal solution：
Calculate the absolute value of difference between target component in the global solution；
The minimum corresponding global solution of the sum of absolute value of difference between target component is searched in each global solution, by the target
The global solution of the sum of absolute value of difference minimum is as optimal solution between parameter.
Described import the customer order information presets tabu search algorithm in one of the embodiments, obtains institute
Further include before the step of stating global solution of the tabu search algorithm to the order information：
Search the historical production data of each factory and each factory location information；
Determine that each factory completes order capacity loss and required production cost based on the historical production data, according to ordering
Customer Location and each factory location information determine range information of the client from factory in list；
Target is obtained according to the capacity loss, the required range information of production cost and the client from factory
Function；
Tabu search algorithm is built according to default edge function and the object function.
In one of the embodiments, it is described according to the capacity loss, it is described needed for production cost and the client
Range information from factory further includes step before obtaining object function：
Each factory is completed into the capacity loss of order, the production cost of each factory completion order and each factory from required visitor
The distance of family position is normalized.
The object function specifically includes in one of the embodiments,：
minΣiΣjΣk(Capability+Cost+Distance }=min ΣiΣjΣk{|PCj/Lj-PCijk·xijk|+
Cij·xijk+Dij
·xijk}
Wherein Capability indicates that capacity loss, Cost indicate that production cost, Distance indicate required Customer Location
With a distance from factory, PCjIndicate the average daily production capacities of factory j；LjIndicate the producing line number that factory j can be provided；PCijk, indicate that order i exists
The producing line k of factory j produces required production capacity supply；CijIndicate that order i produces required cost in factory j；DijExpression is ordered
Distance of single affiliated clients of i apart from factory j.And x thereinijkIt is decision variable, works as xijk=1 is, indicates that order i distributes to work
The producing line k, x of factory jijk=0, then it represents that order i is not yet assigned to the producing line k of factory j.
The tabu search algorithm includes presetting edge function and object function in one of the embodiments, described
The customer order information will be parsed by default tabu search algorithm, it is corresponding more to obtain the customer order information
The step of a global solution, specifically includes：
The customer order information is converted into order data according to the default edge function；
Global solution, the number of the target component of the global solution are obtained according to the object function and the order data
At least 2.
The customer order information is parsed by default tabu search algorithm in one of the embodiments, is obtained
Further include step before the corresponding multiple global solutions of the customer order information：
It is built by R language and presets tabu search algorithm.
A kind of production scheduling device, described device include：
Order information acquisition module, for obtaining customer order information；
Global solution acquisition module is obtained for being parsed to the customer order information by default tabu search algorithm
The corresponding multiple global solutions of the customer order information are obtained, the number of the target component of the global solution is at least 2；
Optimal solution acquisition module, the global solution for searching difference minimum between target component in the global solution, and will
The global solution of difference minimum is as optimal solution between the target component；
Production scheduling module, for carrying out production scheduling according to the optimal solution.
A kind of computer equipment, including memory and processor, the memory are stored with computer program, the processing
The step of device realizes above-mentioned any one method when executing the computer program.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
The step of above-mentioned any one method is realized when row.
Above-mentioned production scheduling method solves the overall situation of the corresponding production scheduling of customer order to preset tabu search algorithm
Solution, and the target component of global solution is not less than 2, then obtains the overall situation that target component difference is minimum in global solution by balancer
Solution, i.e. optimal solution, and production scheduling is carried out according to optimal solution, the waste that production scheduling process generates is reduced, synthesis can be reached and examined
Consider the effect that optimal production scheduling is carried out under a variety of production scheduling targets.
Description of the drawings
Fig. 1 is the flow diagram of production scheduling method in one embodiment；
Fig. 2 is the flow diagram of production scheduling method in one embodiment；
Fig. 3 is the structure diagram of production scheduling device in one embodiment；
Fig. 4 is the internal structure chart of one embodiment Computer equipment.
Specific implementation mode
It is with reference to the accompanying drawings and embodiments, right in order to make the object, technical solution and advantage of the application be more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
Production scheduling method provided by the present application, can be applied in the production environment of make-to-order production, i.e. manufacturing enterprise
Receive the order of client, production scheduling is then carried out according to the actual state of each production common property, by the Order splitting of receiving to each
Production plant is produced, this is because different factory cost valence, address, production capacity, producing line have differences, therefore, from
For entreprise cost and the angle of lean production, it is more desirable under the premise of the cooperation between maintaining each factory so that enterprise
Production cost and customer satisfaction highest.Assuming that manufacturing enterprise carries out production scheduling as unit of day, it will be under the order of yesterday
Plan to each factory；And every producing line of factory all checks pass through before manufacture, does not break down in process of production.
In one embodiment, as shown in Fig. 2, providing a kind of production scheduling method, including step：
S200 obtains customer order information.
Customer order information can specifically include：The life of Customer ID, customer address, product type, product needed for client
Produce the date etc. of batch number quantity, the neat set date of product, the beginning production schedule.Manufacturing enterprise receives client to the pre- of product
It is fixed, generate corresponding customer order information.
S400 parses customer order information by default tabu search algorithm, obtains customer order information and corresponds to
Multiple global solutions, the number of the target component of global solution is at least 2.
Tabu search algorithm, tabu search algorithm are a kind of meta-heuristic random search algorithms, it is initial feasible from one
Solution is set out, and selects a series of specific direction of search as souning out, and selection, which is realized, allows specific target function value to change at most
It is mobile.And traditional tabu search algorithm is to make it so needing modified Tabu search algorithm for the algorithm of single object optimization
It can realize multiple-objection optimization.Default tabu search algorithm target component in the application is at least 2, but due to being provided with multiple mesh
Mark parameter so that the quantity for the solution that default tabu search algorithm obtains is not limited to one.Target component refers to default taboo
The target component of searching algorithm optimization, in one embodiment, target component can specifically include capacity loss, required be produced into
This and client are with a distance from factory.
The order information of client is parsed by preset tabu search algorithm, obtain meet optimization aim with visitor
The corresponding global solution of family order.In one embodiment, parsing global solution can meet owning for the sum of target component minimum
Solution.
S600 searches the global solution of difference minimum between target component in global solution, and most by difference between target component
Small global solution is as optimal solution.
During multiple-objection optimization, so that each target is minimum simultaneously, this is simultaneously unrealistic, in fact, being pursued
Multi-objective optimization question, be to enable multiple targets being optimal " as far as possible " simultaneously, and " " be optimal as far as possible
Scheme also can there are many kinds of, so a variety of " as far as possible " in optimal scheme, it is required obtain target function value minimum
That solution.The value of integration objective object function is 10 in one of the embodiments, and (a1, a2, a3) indicates production capacity respectively
Loss, cost, distance, then one group of possible global solution is (10,0,0) or (Isosorbide-5-Nitrae, 5) or (8,1,1) or (3,4,3), this four
The general objective functional value of group global solution all meets minimum value, but single solution is not the optimal solution in single goal, such as (10,0,0)
The cost and distance of this group solution are 0, it is clear that can not possibly be existed in practical business, for another example the cost of (8,1,1) and apart from right and wrong
Often small value, and production capacity loss is larger, it is clear that there is solution combination better than this.(3,4,3) are targets in this 4 global solutions
The solution of the direct difference minimum of parameter, it is possible to as the global solution of this arrangement scheme.
Target component in different global solutions has differences, by comparing mesh in all global solutions after obtaining global solution
The difference for marking parameter obtains optimal solution.
S800 carries out production scheduling according to optimal solution.
After obtaining optimal solution, production scheduling is carried out according to optimal solution, can be to search taboo in one of the embodiments,
It is optimal the production scheduling plan of solution in searching algorithm, and production scheduling is carried out according to the plan.
Above-mentioned production scheduling method solves the overall situation of the corresponding production scheduling of customer order to preset tabu search algorithm
Solution, and the target component of global solution is not less than 2, then obtains the overall situation that target component difference is minimum in global solution by balancer
Solution, i.e. optimal solution, and production scheduling is carried out according to optimal solution, the waste that production scheduling process generates is reduced, synthesis can be reached and examined
Consider the effect that optimal production scheduling is carried out under a variety of production scheduling targets.
As shown in Fig. 2, difference is minimum between target component in step S600 lookups global solution in one of the embodiments,
Global solution, and the global solution of difference minimum between target component is specifically included as the step of optimal solution：
S620 calculates the absolute value of difference between target component in global solution.
S640 searches in each global solution the minimum corresponding global solution of the sum of absolute value of difference between target component, by mesh
The minimum global solution of the sum of absolute value of difference is as optimal solution between mark parameter.
After obtaining global solution, even if then target can be joined with the absolute value of difference between parameters in global solution
The global solution of the sum of absolute value of difference minimum is as optimal solution between number.Absolute difference is most between parameters in global solution
Small is the global solution of difference minimum between target component.Solving the process of optimal solution in one of the embodiments, can lead to
Overbalance device is realized, it is assumed that there are three the targets of tabu search algorithm, and the course of work of balancer specifically comprises the following steps：
1) three targets indicate d12 by obj1, obj2, obj3 respectively, and d13, d23 indicate the difference between three targets respectively
Absolute value；
2) any one in d12 or d13 or d23 is calculated, is deposited this value as a key-value pair in a list,
The length L of this list is configured by the parameter of preset tabu search algorithm, specifically can be according to preset TABU search
Multiple target distance parameter in algorithm is configured；
3) towards the slightly smaller value of the direction removal search of max (obj1, obj2, obj3), one group of new desired value is obtained, is returned to
Step 1), when calculating to the L+1 or when the value that step 2) is calculated 3 times (3 are target number) does not occur
Change, then return to the key of list intermediate value minimum, a solution as this wheel TABU search iteration returns to argument scalar functions.
When calculating the key-value pair in L every time in one of the embodiments, it can look for looking for according to the direction of a upper key-value pair
, it can comparatively fast find minimum value.
Step S400 in one of the embodiments,：Customer order information is solved by default tabu search algorithm
Analysis, obtaining the corresponding global solution of customer order information further includes before：
S320 searches the historical production data of each factory and each factory location information.
S340 determines that each factory completes order capacity loss and required production cost based on historical production data, according to
Customer Location and each factory location information determine range information of the client from factory in order.
S360, the range information according to capacity loss, required production cost and client from factory obtain object function.
S380 builds tabu search algorithm according to default edge function and object function.
There is the record of production of Related product in each production plant of manufacturing enterprise, by the historical record for analyzing production plant
It can determine that each factory completes the capacity loss of order and required production cost, it can be true according to the location information of each factory
Determine range information of the client from factory.Mesh is then built with a distance from factory according to capacity loss, required production cost and client
Scalar functions.Tabu search algorithm is built according to default edge function and object function.It is main by using default edge function
For being responsible for processing and conversion customer order information, object function then calculates according to edge function treated new order data excellent
Target involved by change problem, it is convenient and efficient.
Further include in one of the embodiments, step before step S360：
Each factory is completed into the capacity loss of order, the production cost of each factory completion order and each factory from required visitor
The distance of family position is normalized.
Normalized refers to that will carry the capacity loss of dimension, cost and client with a distance from factory these three carry dimension
Amount be converted into nondimensional amount, allow the end value of this 3 targets to be in the same level, if capacity loss is 10000, cost
10, client is 50 with a distance from factory, so that them is had additive property and not influence between each other, by they by mathematical way into
Row normalizing, that is, the number being transformed between 0~1.
Object function specifically includes in one of the embodiments,：
minΣiΣjΣk(Capability+Cost+Distance}
=min ΣiΣjΣk{|PCj/Lj-PCijk·xijk|+Cij·xijk+Dij
·xijk}
Wherein Capability indicates that capacity loss, Cost indicate that production cost, Distance indicate required Customer Location
With a distance from factory, PCjIndicate the average daily production capacities of factory j；LjIndicate the producing line number that factory j can be provided；PCijk, indicate that order i exists
The producing line k of factory j produces required production capacity supply；CijIndicate that order i produces required cost in factory j；DijExpression is ordered
Distance of single affiliated clients of i apart from factory j.And x thereinijkIt is decision variable, works as xijk=1 is, indicates that order i distributes to work
The producing line k, x of factory jijk=0, then it represents that order i is not yet assigned to the producing line k of factory j.
Object function indicates raw by designing production capacity, production cost and Customer Location this 3 variables with a distance from factory
The object form that production scheduling is pursued.By object function in conjunction with through presetting edge function treated customer order information,
Obtain global solution of the object function to customer order.
Tabu search algorithm includes default edge function and object function in one of the embodiments, by default
Tabu search algorithm parses customer order information, and the step of obtaining customer order information corresponding multiple global solutions is specific
Including：
Customer order information is converted to order data by S410 according to default edge function；
S430 obtains global solution according to object function and order data.
Default edge function is mainly used for converting customer order information, and is translated into and is ordered for what object function was handled
Forms data, after by presetting edge function to the collection of customer order information processing, by object function to order data into
Row processing obtains global solution of the object function to same day production scheduling
Customer order information is parsed by default tabu search algorithm in one of the embodiments, obtains visitor
Further include step before the corresponding multiple global solutions of family order information：
It is built by R language and presets tabu search algorithm.
The production scheduling method of the application can realize that traditional tabu search algorithm can only in R language using R language
Realize maximized target, and by the tabu search algorithm of the application redesign, it can realize multiple-objection optimization
It is completed at the same time minimum value search.The realization process of the production scheduling method of the application can also include for realizing production scheduling
Front-end interface.And R language has the front end Shiny of lightweight, and R language can be used to realize improved tabu search algorithm, and lead to
It crosses Shiny and realizes the application front end, this framework very light weight, and since R language is open source software, cost is relatively low.
In one embodiment, the production scheduling method of the application, includes the following steps：
S200 obtains customer order information.
S320 searches the historical production data of each factory and each factory location information.
S340 determines that each factory completes order capacity loss and required production cost based on historical production data, according to
Customer Location and each factory location information determine range information of the client from factory in order.
Each factory is completed into the capacity loss of order, the production cost of each factory completion order and each factory from required visitor
The distance of family position is normalized.
S360, the range information according to capacity loss, required production cost and client from factory obtain object function.
S380 builds tabu search algorithm according to default edge function and object function.
Customer order information is converted to order data by S410 according to default edge function；
S430 obtains global solution according to object function and order data,.
S620 calculates the absolute value of difference between target component in global solution.
S640 searches in each global solution the minimum corresponding global solution of the sum of absolute value of difference between target component, by mesh
The minimum global solution of the sum of absolute value of difference is as optimal solution between mark parameter.
S800 carries out production scheduling according to optimal solution.
Default edge function is mainly used for converting customer order information, and is translated into and is ordered for what object function was handled
Forms data, default edge function specifically include：
Function 1：Pm_orders is handled for the customer order information to the same day, is inputted as same day new order, defeated
Order data after going out for convergence particularly calculates the number and summation of each different product model.
Function 2：Idxbin, Binary Conversion are the function of integer value, and as distribution factory index, acquisition pair is indexed with this
Factory is answered, Binary Conversion is needed exist for, is an implementation wherein because input of TABU search can only be binary representation
Example in, can indicate factory using binary string, the digit of binary string depending on the quantity of factory, such as 3 two into
System can indicate 0~7, can be used for indicating most 8 factories；
Function 3：Dispatch, a string of binary numbers are converted into a string of factory's titles, concretely first initialize one to
F is measured, indicates the order number and summation that summarize within certain day each different product model of gained, the i.e. function of pm_orders functions, f
Length be the line number quantity on order of different product model (be) that pm_orders is returned, by a string of binary numbers, (number is
3 multiple, every 3 can indicate factory's index) it is converted into the title of a string of factories, mainly by traversing this binary system
String, has invoked idxbin functions and obtains the index value of factory, to obtain a string of factory's index values, and index value is assigned to f
I-th of vector, indicate the corresponding factory's index distributed of i-th product type.
Function 4：Caploss calculates often wheel scheduling and specifies factory's capacity loss summation；
Function 5：Cost calculates often wheel scheduling and specifies production cost；
Function 6：Distance, order client is at a distance from factory in calculating often wheel scheduling.
Object function specifically includes：
minΣiΣjΣk(Capability+Cost+Distance}
=min ΣiΣjΣk{|PCj/Lj-PCijk·xijk|+Cij·xijk+Dij
·xijk}
Wherein Capability indicates that capacity loss, Cost indicate that production cost, Distance indicate required Customer Location
With a distance from factory, PCjIndicate the average daily production capacities of factory j；LjIndicate the producing line number that factory j can be provided；PCijk, indicate that order i exists
The producing line k of factory j produces required production capacity supply；CijIndicate that order i produces required cost in factory j；DijExpression is ordered
Distance of single affiliated clients of i apart from factory j.And x thereinijkIt is decision variable, works as xijk=1 is, indicates that order i distributes to work
The producing line k, x of factory jijk=0, then it represents that order i is not yet assigned to the producing line k of factory j.
Above-mentioned production scheduling method solves the overall situation of the corresponding production scheduling of customer order to preset tabu search algorithm
Solution, and the target component of global solution is not less than 2, then obtains the overall situation that target component difference is minimum in global solution by balancer
Solution, i.e. optimal solution, and production scheduling is carried out according to optimal solution, can reach consider carried out under a variety of production scheduling targets it is optimal
Change the effect of production scheduling.
It should be understood that although each step in Fig. 1-2 flow charts is shown successively according to the instruction of arrow, this
A little steps are not that the inevitable sequence indicated according to arrow executes successively.Unless expressly state otherwise herein, these steps
It executes there is no the limitation of stringent sequence, these steps can execute in other order.Moreover, at least one in Fig. 1-2
May include that either these sub-steps of multiple stages or stage are held in synchronization to multiple sub-steps step by step
Row is completed, but can be executed at different times, the execution sequence in these sub-steps or stage be also not necessarily successively into
Row, but can either the sub-step of other steps or at least part in stage are held in turn or alternately with other steps
Row.
In one embodiment, as shown in figure 3, providing a kind of production scheduling device, device includes：
Order information acquisition module 200, for obtaining customer order information；
Global solution acquisition module 400 is obtained for being parsed to customer order information by default tabu search algorithm
The corresponding multiple global solutions of customer order information, the number of the target component of global solution are at least 2；
Optimal solution acquisition module 600, the global solution for searching difference minimum between target component in global solution, and by mesh
The global solution of difference minimum is as optimal solution between mark parameter.
Production scheduling module 800, for carrying out production scheduling according to optimal solution.
Above-mentioned production scheduling device solves the overall situation of the corresponding production scheduling of customer order to preset tabu search algorithm
Solution, and the target component of global solution is not less than 2, then obtains the overall situation that target component difference is minimum in global solution by balancer
Solution, i.e. optimal solution, and production scheduling is carried out according to optimal solution, the waste that production scheduling process generates is reduced, synthesis can be reached and examined
Consider the effect that optimal production scheduling is carried out under a variety of production scheduling targets.
Optimal solution acquisition module 600 specifically includes in one of the embodiments,：
Absolute value calculation unit, the absolute value for calculating difference between target component in global solution；
Optimal solution searching unit, for searching, the sum of absolute value of difference is minimum corresponding between target component in each global solution
Global solution, using the global solution of the sum of absolute value of difference between target component minimum as optimal solution.
Production scheduling device further includes tabu search algorithm structure module in one of the embodiments, and TABU search is calculated
Method builds module：
Inquiry of historical data unit, the historical production data for inquiring each factory and each factory location information；
Plant data confirmation unit, for determining that each factory completes order capacity loss and institute according to historical production data
Production cost is needed, range information of the client from factory is determined according to Customer Location in order and each factory location information；
Object function construction unit, for being believed with a distance from factory according to capacity loss, required production cost and client
Breath obtains object function；
Tabu search algorithm construction unit, for building tabu search algorithm with object function according to default edge function.
Tabu search algorithm structure module further includes in one of the embodiments,：
Normalization unit, for each factory to be completed the capacity loss of order, each factory complete the production cost of order with
And each factory is normalized with a distance from required Customer Location.
Global solution acquisition module 400 specifically includes in one of the embodiments,：
Order data converting unit, for customer order information to be converted to order data according to default edge function；
Global solution acquiring unit, for obtaining global solution according to object function and order data.
Specific about production scheduling device limits the restriction that may refer to above for production scheduling method, herein not
It repeats again.Modules in above-mentioned production scheduling device can be realized fully or partially through software, hardware and combinations thereof.On
Stating each module can be embedded in or independently of in the processor in computer equipment, can also store in a software form in the form of hardware
In memory in computer equipment, the corresponding operation of the above modules is executed in order to which processor calls.
In one embodiment, a kind of computer equipment is provided, which can be terminal, internal structure
Figure can be as shown in Figure 4.The computer equipment includes the processor connected by system bus, memory, network interface, display
Screen and input unit.Wherein, the processor of the computer equipment is for providing calculating and control ability.The computer equipment is deposited
Reservoir includes non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system and computer journey
Sequence.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculating
The network interface of machine equipment is used to communicate by network connection with external terminal.When the computer program is executed by processor with
Realize a kind of production scheduling method.The display screen of the computer equipment can be liquid crystal display or electric ink display screen,
The input unit of the computer equipment can be the touch layer covered on display screen, can also be to be arranged on computer equipment shell
Button, trace ball or Trackpad, can also be external keyboard, Trackpad or mouse etc..
It will be understood by those skilled in the art that structure shown in Fig. 4, is only tied with the relevant part of application scheme
The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to application scheme, specific computer equipment
May include either combining certain components than more or fewer components as shown in the figure or being arranged with different components.
In one embodiment, a kind of computer equipment, including memory and processor are provided, is stored in memory
Computer program, the processor realize following steps when executing computer program：
Obtain customer order information；
Customer order information is parsed by default tabu search algorithm, it is corresponding multiple to obtain customer order information
The number of global solution, the target component of global solution is at least 2；
Search the global solution of difference minimum between target component in global solution, and by between target component difference minimum it is complete
Office's solution is used as optimal solution.
Production scheduling is carried out according to optimal solution.
In one embodiment, following steps are also realized when computer program is executed by processor：
Calculate the absolute value of difference between target component in global solution；
The minimum corresponding global solution of the sum of absolute value of difference between target component is searched in each global solution, by target component
Between difference the minimum global solution of the sum of absolute value as optimal solution.
In one embodiment, following steps are also realized when computer program is executed by processor：
Inquire the historical production data of each factory and each factory location information；
Determine that each factory completes order capacity loss and required production cost according to historical production data, according in order
Customer Location and each factory location information determine range information of the client from factory；
Range information according to capacity loss, required production cost and client from factory obtains object function；
Tabu search algorithm is built according to default edge function and object function.
In one embodiment, following steps are also realized when computer program is executed by processor：
Each factory is completed into the capacity loss of order, the production cost of each factory completion order and each factory from required visitor
The distance of family position is normalized.
In one embodiment, following steps are also realized when computer program is executed by processor：
Customer order information is converted into order data according to default edge function；
Global solution is obtained according to object function and order data.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program realizes following steps when being executed by processor：
Obtain customer order information；
Customer order information is parsed by default tabu search algorithm, it is corresponding multiple to obtain customer order information
The number of global solution, the target component of global solution is at least 2；
Search the global solution of difference minimum between target component in global solution, and by between target component difference minimum it is complete
Office's solution is used as optimal solution.
Production scheduling is carried out according to optimal solution.
In one embodiment, following steps are also realized when computer program is executed by processor：
Calculate the absolute value of difference between target component in global solution；
The minimum corresponding global solution of the sum of absolute value of difference between target component is searched in each global solution, by target component
Between difference the minimum global solution of the sum of absolute value as optimal solution.
In one embodiment, following steps are also realized when computer program is executed by processor：
Inquire the historical production data of each factory and each factory location information；
Determine that each factory completes order capacity loss and required production cost according to historical production data, according in order
Customer Location and each factory location information determine range information of the client from factory；
Range information according to capacity loss, required production cost and client from factory obtains object function；
Tabu search algorithm is built according to default edge function and object function.
In one embodiment, following steps are also realized when computer program is executed by processor：
Each factory is completed into the capacity loss of order, the production cost of each factory completion order and each factory from required visitor
The distance of family position is normalized.
In one embodiment, following steps are also realized when computer program is executed by processor：
Customer order information is converted into order data according to default edge function；
Global solution is obtained according to object function and order data.
One of ordinary skill in the art will appreciate that realizing all or part of flow in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.Wherein,
Any reference to memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of above example can be combined arbitrarily, to keep description succinct, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield is all considered to be the range of this specification record.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, under the premise of not departing from the application design, various modifications and improvements can be made, these belong to the protection of the application
Range.Therefore, the protection domain of the application patent should be determined by the appended claims.
Claims (10)
1. a kind of production scheduling method, which is characterized in that including step：
Obtain customer order information；
The customer order information is parsed by default tabu search algorithm, it is corresponding to obtain the customer order information
The number of multiple global solutions, the target component of the global solution is at least 2；
The global solution of difference minimum between target component in the global solution is searched, and difference between the target component is minimum
Global solution as optimal solution；
Production scheduling is carried out according to the optimal solution.
2. according to the method described in claim 1, it is characterized in that, described search in the global solution difference between target component
Minimum global solution, and the global solution of difference minimum between the target component is specifically included as the step of optimal solution：
Calculate the absolute value of difference between target component in the global solution；
The minimum corresponding global solution of the sum of absolute value of difference between target component is searched in each global solution, by the target component
Between difference the minimum global solution of the sum of absolute value as optimal solution.
3. according to the method described in claim 1, it is characterized in that, described search the default taboo of customer order information importing
Rope algorithm further includes before the step of obtaining global solution of the tabu search algorithm to the order information：
Search the historical production data of each factory and each factory location information；
Determine that each factory completes order capacity loss and required production cost based on the historical production data, according in order
Customer Location and each factory location information determine range information of the client from factory；
Target letter is obtained according to the capacity loss, the required range information of production cost and the client from factory
Number；
Tabu search algorithm is built according to default edge function and the object function.
4. according to the method described in claim 3, it is characterized in that, it is described according to the capacity loss, it is described needed for be produced into
Further include step before this and range information acquisition object function of the client from factory：
Each factory is completed into the capacity loss of order, each factory completes the production cost of order and each factory
It is normalized with a distance from required Customer Location.
5. according to the method described in claim 3, it is characterized in that,
The object function specifically includes：
min∑i∑j∑k{ Capability+Cost+Distance }=min ∑si∑j∑k{|PCj/Lj-PCijk·xijk|+Cij·
xijk+Dij·xijk}
Wherein Capability indicates that capacity loss, Cost indicate that production cost, Distance indicate required Customer Location from work
The distance of factory, PCjIndicate the average daily production capacities of factory j；LjIndicate the producing line number that factory j can be provided；PCijk, indicate order i in factory
The producing line k of j produces required production capacity supply；CijIndicate that order i produces required cost in factory j；DijIndicate order i
Distance of the affiliated client apart from factory j.And x thereinijkIt is decision variable, works as xijk=1 is, indicates that order i distributes to factory j
Producing line k, xijk=0, then it represents that order i is not yet assigned to the producing line k of factory j.
6. according to the method described in claim 4, it is characterized in that, the tabu search algorithm include default edge function and
Object function, it is described the customer order information to be parsed by default tabu search algorithm, it obtains the client and orders
The step of single information corresponding multiple global solutions, specifically includes：
The customer order information is converted into order data according to the default edge function；
Global solution is obtained according to the object function and the order data, the number of the target component of the global solution is at least
It is 2.
7. according to the method described in claim 1, it is characterized in that, described search the default taboo of customer order information input
Rope algorithm obtains the tabu search algorithm to further including step before the global solution of the customer order information：
It is built by R language and presets tabu search algorithm.
8. a kind of production scheduling device, which is characterized in that described device includes：
Order information acquisition module, for obtaining customer order information；
Global solution acquisition module obtains institute for being parsed to the customer order information by default tabu search algorithm
The corresponding multiple global solutions of customer order information are stated, the number of the target component of the global solution is at least 2；
Optimal solution acquisition module, the global solution for searching difference minimum between target component in the global solution, and will be described
The global solution of difference minimum is as optimal solution between target component；
Production scheduling module, for carrying out production scheduling according to the optimal solution.
9. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In when the processor executes the computer program the step of any one of realization claim 1 to 7 the method.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of method described in any one of claim 1 to 7 is realized when being executed by processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810288929.7A CN108647914B (en) | 2018-04-03 | 2018-04-03 | Production scheduling method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810288929.7A CN108647914B (en) | 2018-04-03 | 2018-04-03 | Production scheduling method and device, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108647914A true CN108647914A (en) | 2018-10-12 |
| CN108647914B CN108647914B (en) | 2021-01-08 |
Family
ID=63745550
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810288929.7A Active CN108647914B (en) | 2018-04-03 | 2018-04-03 | Production scheduling method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108647914B (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109445389A (en) * | 2018-10-30 | 2019-03-08 | 广东浪淘砂新型材料有限公司 | A kind of method and its control system of full-automatic compounding production |
| CN111260320A (en) * | 2020-01-15 | 2020-06-09 | 广东溢达纺织有限公司 | Fabric order scheduling method and device |
| CN112001619A (en) * | 2020-08-20 | 2020-11-27 | 北京京东振世信息技术有限公司 | Production scheduling method and device, computer storage medium and electronic equipment |
| CN112083698A (en) * | 2020-09-10 | 2020-12-15 | 南昌市言诺科技有限公司 | Production line real-time control method and device based on digital twin model |
| CN112132546A (en) * | 2020-09-25 | 2020-12-25 | 杉数科技(北京)有限公司 | Method and device for scheduling production |
| CN112361348A (en) * | 2020-11-19 | 2021-02-12 | 上海电气集团股份有限公司 | Production scheduling method, system, equipment and medium for hazardous waste cement kiln incineration disposal |
| CN112734222A (en) * | 2021-01-06 | 2021-04-30 | 成都星云智联科技有限公司 | Factory order production scheduling self-adaptive method, equipment and storage medium |
| CN113065686A (en) * | 2021-03-10 | 2021-07-02 | 联想(北京)有限公司 | Scheduling data optimization processing method, device and equipment |
| CN113222396A (en) * | 2021-05-08 | 2021-08-06 | 福州大学 | Intelligent ordering and scheduling method for prescription orders in automatic medicine dispensing system |
| CN114282716A (en) * | 2021-12-17 | 2022-04-05 | 海尔优家智能科技（北京）有限公司 | Production schedule generation method and device, electronic equipment and storage medium |
| CN114936778A (en) * | 2022-05-26 | 2022-08-23 | 清华大学 | Component job shop scheduling method and device |
| CN116402238A (en) * | 2023-06-07 | 2023-07-07 | 云印技术(深圳)有限公司 | Production scheduling method, device and equipment for packaging order and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1998004965A2 (en) * | 1996-07-16 | 1998-02-05 | Colorado State University Research Foundation | Method and system for tracking multiple regional objects by multi-dimensional relaxation |
| CN101763601A (en) * | 2010-01-12 | 2010-06-30 | 武汉大学 | Land use partition method based on tabu search algorithm |
| CN101859100A (en) * | 2010-06-18 | 2010-10-13 | 杭州电子科技大学 | Improved particle swarm optimization method based on streamline production scheduling of fuzzy due date |
| CN103729740A (en) * | 2013-12-30 | 2014-04-16 | 北京施达优技术有限公司 | Data processing method and device for generating production plan |
| CN103745270A (en) * | 2013-12-30 | 2014-04-23 | 北京大学 | Data processing method and device for workshop production |
-
2018
- 2018-04-03 CN CN201810288929.7A patent/CN108647914B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1998004965A2 (en) * | 1996-07-16 | 1998-02-05 | Colorado State University Research Foundation | Method and system for tracking multiple regional objects by multi-dimensional relaxation |
| CN101763601A (en) * | 2010-01-12 | 2010-06-30 | 武汉大学 | Land use partition method based on tabu search algorithm |
| CN101859100A (en) * | 2010-06-18 | 2010-10-13 | 杭州电子科技大学 | Improved particle swarm optimization method based on streamline production scheduling of fuzzy due date |
| CN103729740A (en) * | 2013-12-30 | 2014-04-16 | 北京施达优技术有限公司 | Data processing method and device for generating production plan |
| CN103745270A (en) * | 2013-12-30 | 2014-04-23 | 北京大学 | Data processing method and device for workshop production |
Non-Patent Citations (1)
| Title |
|---|
| 杨文强等: "基于改进禁忌搜索的多目标自动化仓库调度 ", 《计算机集成制造系统》 * |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109445389A (en) * | 2018-10-30 | 2019-03-08 | 广东浪淘砂新型材料有限公司 | A kind of method and its control system of full-automatic compounding production |
| CN111260320A (en) * | 2020-01-15 | 2020-06-09 | 广东溢达纺织有限公司 | Fabric order scheduling method and device |
| CN111260320B (en) * | 2020-01-15 | 2023-06-06 | 广东溢达纺织有限公司 | Scheduling method and device for fabric orders |
| CN112001619A (en) * | 2020-08-20 | 2020-11-27 | 北京京东振世信息技术有限公司 | Production scheduling method and device, computer storage medium and electronic equipment |
| CN112083698A (en) * | 2020-09-10 | 2020-12-15 | 南昌市言诺科技有限公司 | Production line real-time control method and device based on digital twin model |
| CN112083698B (en) * | 2020-09-10 | 2021-08-20 | 南昌市言诺科技有限公司 | Production line real-time control method and device based on digital twin model |
| CN112132546A (en) * | 2020-09-25 | 2020-12-25 | 杉数科技(北京)有限公司 | Method and device for scheduling production |
| CN112361348B (en) * | 2020-11-19 | 2022-10-21 | 上海电气集团股份有限公司 | Production scheduling method, system, equipment and medium for hazardous waste cement kiln incineration disposal |
| CN112361348A (en) * | 2020-11-19 | 2021-02-12 | 上海电气集团股份有限公司 | Production scheduling method, system, equipment and medium for hazardous waste cement kiln incineration disposal |
| CN112734222A (en) * | 2021-01-06 | 2021-04-30 | 成都星云智联科技有限公司 | Factory order production scheduling self-adaptive method, equipment and storage medium |
| CN113065686A (en) * | 2021-03-10 | 2021-07-02 | 联想(北京)有限公司 | Scheduling data optimization processing method, device and equipment |
| CN113222396A (en) * | 2021-05-08 | 2021-08-06 | 福州大学 | Intelligent ordering and scheduling method for prescription orders in automatic medicine dispensing system |
| CN113222396B (en) * | 2021-05-08 | 2022-06-21 | 福州大学 | Intelligent ordering and scheduling method for prescription orders in automatic medicine dispensing system |
| CN114282716A (en) * | 2021-12-17 | 2022-04-05 | 海尔优家智能科技（北京）有限公司 | Production schedule generation method and device, electronic equipment and storage medium |
| WO2023109229A1 (en) * | 2021-12-17 | 2023-06-22 | 海尔优家智能科技（北京）有限公司 | Production schedule generation method and apparatus, and electronic device and storage medium |
| CN114936778A (en) * | 2022-05-26 | 2022-08-23 | 清华大学 | Component job shop scheduling method and device |
| CN114936778B (en) * | 2022-05-26 | 2022-12-09 | 清华大学 | Component job shop scheduling method and device |
| CN116402238A (en) * | 2023-06-07 | 2023-07-07 | 云印技术(深圳)有限公司 | Production scheduling method, device and equipment for packaging order and storage medium |
| CN116402238B (en) * | 2023-06-07 | 2023-08-25 | 云印技术(深圳)有限公司 | Production scheduling method, device and equipment for packaging order and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108647914B (en) | 2021-01-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108647914A (en) | Production scheduling method and device, computer equipment and storage medium | |
| Tsao et al. | A continuous approximation approach for the integrated facility-inventory allocation problem | |
| US20150149257A1 (en) | Systems and methods for enterprise profit optimization | |
| Khalifehzadeh et al. | Solving a fuzzy multi objective model of a production–distribution system using meta-heuristic based approaches | |
| Bülbül et al. | A linear programming-based method for job shop scheduling | |
| Koç | An evolutionary algorithm for supply chain network design with assembly line balancing | |
| CN111260194A (en) | Accurate material supply method and system based on block chain | |
| CN113065714A (en) | Multi-project type multi-target centralized purchasing decision optimization method and system | |
| CN111105050B (en) | Fan maintenance plan generation method, device, equipment and storage medium | |
| Will et al. | C3o: Collaborative cluster configuration optimization for distributed data processing in public clouds | |
| Zhang et al. | Big data-enabled intelligent synchronisation for the complex production logistics system under the opti-state control strategy | |
| Overbeck et al. | Development and analysis of digital twins of production systems | |
| CN109740799A (en) | Budget data generation method, device, computer equipment and storage medium | |
| CN117540870A (en) | Product shortage prediction method, system, equipment and readable medium | |
| Raoofpanah et al. | Extended hybrid tabu search and simulated annealing algorithm for location-inventory model with multiple products, multiple distribution centers and multiple capacity levels | |
| Sun et al. | Study on third-party logistics service provider selection evaluation indices system based on analytic network process with BOCR | |
| Braune et al. | Applying genetic algorithms to the optimization of production planning in a real-world manufacturing environment | |
| KR102237389B1 (en) | Computing apparatus for supporting material cost calculation considering bidirectional flow and material cost calculation method of computing apparatus | |
| Musiał et al. | Improvement of production process scheduling with the use of heuristic methods | |
| Khokhlovsky et al. | MODERNISATION OF A CONTROL CABINET PRODUCTION PROCESS USING MULTICRITERIA OPTIMIZATION LOGIC AND AUGMENTED REALITY. | |
| US20140149186A1 (en) | Method and system of using artifacts to identify elements of a component business model | |
| CN114169602A (en) | Asset ex-warehouse combination optimization method and device, computer equipment and storage medium | |
| Guan et al. | Advanced load forecast with hierarchical forecasting capability | |
| Chen et al. | Cost allocation of capacity investment games | |
| Marinkovic et al. | Digital Twins for Internal Transport Systems: Use Cases, Functions, and System Architecture |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |