CN109426920A - A kind of enterprise's production planning optimization method considering prediction order and practical order - Google Patents
A kind of enterprise's production planning optimization method considering prediction order and practical order Download PDFInfo
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Abstract
The invention discloses a kind of enterprise's production planning optimization methods for considering prediction order and practical order, including the following steps: and History Order task related data (1) is obtained, data classification is carried out, the input as order taking responsibility prediction；(2) it constructs based on the order taking responsibility prediction model for improving time series；(3) prediction order taking responsibility data and practical order taking responsibility data are combined, corresponding production planning optimization objective function is formulated；(4) hybrid optimization algorithm based on traditional artificial ant colony algorithm and Tabu Search algorithm is established, the optimization object function in step 3 is optimized, optimum results are production plan detail, instruct the movable development of actual production and implement.Production planning optimization is divided into two stages through the invention, more accurately realizes the formulation and implementation of textile machine manufacturing enterprise production plan, so as to provide effective guidance for the production planning optimization of corresponding Small and Medium Manufacturing Enterprises.
Description
Technical field
The present invention relates to textile machine manufacturing enterprises to optimize technique study technology neck when formulating shop Planning
Domain, specially a kind of enterprise's production planning optimization method for considering prediction order and practical order.
Background technique
For textile machine manufacture for, production plan be in scheduling order production task it is essential with reference to according to
According to it plays directive function to the movable reasonable arrangement of production between entire enterprises, enterprise and enterprise.Textile machine
Manufacturing enterprise produces movable complexity, diversity clearly, with market economic system as conventionally manufactured enterprise
Continuous variation and the continuous development of modern computer information technology, this complexity and diversity become more and more prominent,
So that the movable stability of the production of enterprise is constantly reducing, the competition situation between enterprise is also constantly changing.Due to
The order personalization of product of textile machine manufacturing enterprise is prominent, order taking responsibility amount it is seasonal it is obvious, order delivery date is short etc. because
Element, so that textile machine manufacturing enterprise is higher for the requirement for formulating reasonable, effective production plan.
Currently, the correlative study method for having scholar that some production planning optimization methods have been proposed, for example, pay stand it is female etc.
(Fu Likun, Qiao Peili are learned based on SC collaboration optimization algorithm [J] the Harbin University of Science and Technology that adapter distribution is searched for
Report, 2015,20 (2): 80-84.) on the basis of multistage entry constrains production lot size problem model, utilize Lagrange
Relaxed algorithm decomposes the production planning problem of entire supply chain, and realizes multi-echelon supply chain batch using adapter distribution algorithm
The coordination optimization of production problem is measured, but this method takes a long time when production plan np problem in solving similar frame enterprise；?
Research and analysis are about by single manufacture (MTO) and manufacturing the production plan of (MTS) two kinds of manufacturing modes and monitoring by inventory and need
After asking, (Beemsterboer, B., Land, M., Teunter, the R.Hybrid MTO-MTS such as Beemsterboer
production planning:An explorative study[J].European Journal of Operational
Research, 2016,248 (2): 453-461. doi:10.1016/j.ejor2015.07.037.) it constructs towards integrated
Markov production planning optimization model under the mixing manufacturing mode of MTO and MTS, but multiple MTO can not be effectively treated in the model
Machine allocation problem caused by delivery date between MTS changes；There are a variety of in a supermatic manufacturing environment
The second level manufacturing shop of processing type, and Choi etc. (Choi, Y.-C., P.A production
planning in highly automated manufacturing system considering multiple
process plans with different energy requirements[J].International Journal Of
Advanced Manufacturing Technology,2014,70(5-8):853-867.doi:10.1007/s00170-
013-5306-1.) for the energy consumption and material assignment problem coordinated between total production plan and second level production plan, mention
Lacked out using the minimum of the sum of total energy consumption, inventory cost and back order punishment as optimization aim, but in the model
The considerations of to energy consumption；(Mardan, E., Amalnik, M.S., Rabbani, the M.An integrated such as Mardan
emergency ordering and production planning optimization model with demand and
yield uncertainty [J].International Journal Of Production Research,2015,53
(20): 6023-6039.doi:10.1080/00207543.2015.1008109.) tight for occurring during manufacturing
Anxious order problem proposes rush order plan and original production plan carrying out flexible integration, and establishes dual-stage production plan and determine
Plan formulate mechanism, supplier is divided into conventional and special two parts, the former be directed to original production plan, the latter mainly for
Rush order plan, but the determination method for proposing each section weight occurrence in integrated model is not known.
There are certain defects in the production planning optimization of textile machine manufacturing enterprise for these above-mentioned methods, due to weaving
The order delivery date of Mechanical Manufacturing Enterprises usually shorter (essentially one week) causes production schedule cycle to shorten, and product manufacturing
Mode is small lot multi-variable manufacturing, while order taking responsibility has the characteristics that apparent seasonality, it is therefore necessary to by History Order
Data are effectively analyzed and are handled, and obtain reasonably predicting task data, and using existing production planning optimization theory as base
Plinth constructs the multiple target production planning optimization model based on time and cost.
Summary of the invention
The purpose of the present invention is to provide a kind of production planning optimization sides, enterprise for considering prediction order and practical order
Method, the practical formulation problem of production plan of textile machine manufacturing enterprise is coped with using the thought of hierarchy optimization, and first layer is to order
Single task prediction interval improves conventional time series prediction technique and enterprise order task is effectively predicted；Second layer master
Production plan layer, in conjunction with business forcast order taking responsibility, a variety of manufacturing resources inside current enterprise and between enterprise into
Row optimization analysis and modeling, to be formulated for actual production plan and optimize providing method guidance, to solve above-mentioned background skill
The problem of being proposed in art.
To achieve the above object, the invention provides the following technical scheme:
A kind of enterprise's production planning optimization method considering prediction order and practical order, including bilevel optimization:
First layer is optimized for the optimization of order taking responsibility prediction interval, is carried out by the related data information to History Order task
It collects, handle and analyzes, provide input for order taking responsibility prediction model, be that optimization is asked with improved Time Series Forecasting Methods
Resolving Algorithm exports to predict order taking responsibility data information accordingly；
The second layer is optimized for the optimization of production plan layer, the result that first layer is optimized and actual order taking responsibility collection
It closes, provides input for production planning optimization model, be that optimization is asked with mixed artificial bee colony algorithm and Tabu Search algorithm
Resolving Algorithm exports as corresponding production planning optimization result detail.
Preferably, the prediction model in the first layer order taking responsibility prediction interval is based on it is assumed hereinafter that condition:
(1) prediction model is only applicable within the scope of some cycles, i.e. T ∈ [Tl,Tu], and the product category for including in source data
In same type, different size；
(2) seasonality of textile machine product other three attribute that compare are more obvious, no longer individually examine in solving model
ConsiderBut consider as an entirety, i.e.,
Wherein, T is predetermined period, TlFor predetermined period start time, TuFor predetermined period finish time,For in t moment
Trend sexual factor impact factor caused by order taking responsibility prediction result,To recycle sexual factor to order taking responsibility in t moment
Impact factor caused by prediction result,For t moment random factor influenced caused by order taking responsibility prediction result because
Son,It indicates in t moment Seasonal impact factor caused by order taking responsibility prediction result.
Preferably, specific step is as follows for the order taking responsibility prediction model:
S1: it is as follows to construct order volume initial matrix according to initial value for parameter setting:
S2: it solvesMatrix N is converted according to four displacement methods of average, is obtained:
N' is converted according to the method for average placed in the middle, is obtained:
It is obtained according to formula S=N/N ":
Using mean value method, four seasonal effect factors are solved:
JudgementWhether 4 are equal to, if be equal to,It remains unchanged, otherwise needs using correction factor method, it may be assumed that
S3: it solvesAccording to formulaIt can acquire:
S4: regression equation is solved according to the data that step S3 is obtained and converts one-dimensional sequence for matrix, it may be assumed that
In order to preferably embody, t value is serialized, above-mentioned sequence amendment are as follows:
Of={ O1,O2,...,Ot',...,Ot*4, t'=1,2 ..., t*4 }
S5: using Eviews9.0 to above-mentioned OfSequence carries out auto-correlation and partial Correlation Analysis, and building autoregression is mobile flat
Equal model ARMA (p, q), and determine p, q value:
S6: according to the ARMA (p, q) determined in step S5, predicted value R is solvedt。
Preferably, the second layer production planning optimization layer is using first layer prediction result and practical order taking responsibility as excellent
Change the input of model, content includes:
(1) related mathematic sign is defined:
(2) objective function of Optimized model: min (F is establishedCoTime,FMeanTime,Fcost), wherein minimize maximum completion
Time:
FCoTime=Min (max { AcCoi}),1i n；AcCoi=Coijk, 1i n, j=o, 1k m
Minimize mean completion time:
Processing cost is minimized, wherein processing cost mainly includes manufacturing procedure cost Cp, transportation cost CtIt is punished with extension
Penalize cost Cd, totle drilling cost Fcost:
Delayi=Ceil ((AcCoi-PlCoi)/60)
(3) constraint condition:
(4) artificial bee colony algorithm and Tabu Search algorithm are mixed, solution analysis is carried out to model, what is obtained is defeated
It is out effective output result of Optimizing manufacture layer；Specific step is as follows:
S1: coding and decoding, and the coding mode based on process, the chromosome of each approximate optimal solution are used in HABC
In all include the encoding gene for completing all manufacturing procedures, each gene is symbol (i, the Job with task to be processedi)
It indicates, and the number that i occurs is Ni；
S2: initialization of population method, initialization of population, which uses, is based on random initializtion method and Clustering roulette side
On the one hand the mode that method combines ensure that the diversity of initial population individual by the chromosomal gene being randomly generated, another
Aspect guarantees the legitimacy of initial population, validity using Clustering roulette strategy；
S3: fitness function, fitness function F as shown by the equation:
F (x)=WCoTime*FCoTime(x)'+WMeanTime*FMeanTime(x)'+WCost*FCost(x)'
S4: crossover operator selects the mode of the sequence crossover based on random task to be processed.
Compared with prior art, the beneficial effects of the present invention are:
Enterprise's production planning optimization method of order and practical order is predicted in this consideration, due to actual in current enterprise
When production plan is formulated, only consider to have signed order taking responsibility data information mostly, that ignores History Order task excavates valence
Value, the present invention are considered these data informations comprehensively, the life of textile machine manufacturing enterprise are coped with using the thought of hierarchy optimization
The practical formulation problem of plan is produced, textile machine manufacturing enterprise production planning optimization model is totally divided into two layers, first layer is to order
Single task prediction interval improves conventional time series prediction technique and to enterprise order task by analysis of history order data
It is effectively predicted；Second layer main production plan layer, i.e. simulation scheduled production layer, based on currently practical order taking responsibility, in conjunction with enterprise
Industry predicts order taking responsibility, and a variety of manufacturing resources between current enterprise inside and enterprise optimize analysis and modeling,
To formulate for actual production plan and optimize providing method guidance, the reasonability and feasible that production plan is formulated ensure that
Property；In addition, can preferably improve asking for algorithm using mixing artificial bee colony algorithm and Tabu Search algorithm in the present invention
It solves speed and solves quality, therefore can be realized to production plan and actual processing process more preferable control.
Detailed description of the invention
Fig. 1 is flow diagram of the invention；
Fig. 2 is that artificial bee colony algorithm and Tabu Search algorithm solution basic flow chart are mixed in the present invention；
Fig. 3 is that the present invention is based on the prediction model figures of conventional time series prediction technique；
Fig. 4 is that the present invention is based on the prediction model figures for improving Time Series Forecasting Methods；
Fig. 5 is the convergence curve comparison diagram of algorithms of different of the present invention；
Fig. 6 is production planning optimization result gunter schematic diagram of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
His embodiment, shall fall within the protection scope of the present invention.
Embodiment one:
In the embodiment of the present invention: a kind of enterprise's production planning optimization method considering prediction order and practical order, including
Bilevel optimization:
First layer is optimized for the optimization of order taking responsibility prediction interval, is carried out by the related data information to History Order task
It collects, handle and analyzes, provide input for order taking responsibility prediction model, be that optimization is asked with improved Time Series Forecasting Methods
Resolving Algorithm exports to predict order taking responsibility data information accordingly；Wherein, the prediction mould in first layer order taking responsibility prediction interval
Type is based on it is assumed hereinafter that condition:
(1) prediction model is only applicable within the scope of some cycles, i.e. T ∈ [Tl,Tu], and the product category for including in source data
In same type, different size；
(2) seasonality of textile machine product other three attribute that compare are more obvious, in solving model no longer individually
ConsiderBut consider as an entirety, i.e.,
Wherein, T is predetermined period, TlFor predetermined period start time, TuFor predetermined period finish time,For in t moment
Trend sexual factor impact factor caused by order taking responsibility prediction result,To recycle sexual factor to order taking responsibility in t moment
Impact factor caused by prediction result,For t moment random factor influenced caused by order taking responsibility prediction result because
Son,It indicates in t moment Seasonal impact factor caused by order taking responsibility prediction result；Order taking responsibility prediction model
Specific step is as follows:
Step 1: it is as follows to construct order volume initial matrix according to initial value for parameter setting:
Step 2: solvingMatrix N is converted according to four displacement methods of average, is obtained:
N' is converted according to the method for average placed in the middle, is obtained:
It is obtained according to formula S=N/N ":
Using mean value method, four seasonal effect factors are solved:
JudgementWhether 4 are equal to, if be equal to,It remains unchanged, otherwise needs using correction factor method, it may be assumed that
Step 3: solvingAccording to formulaIt can acquire:
Step 4: solving regression equation, matrix is converted one-dimensional sequence by the data obtained according to step 3, it may be assumed that
In order to preferably embody, t value is serialized, above-mentioned sequence amendment are as follows:
Of={ O1,O2,...,Ot',...,Ot*4, t'=1,2 ..., t*4 }
Step 5: using Eviews9.0 to above-mentioned OfSequence carries out auto-correlation and partial Correlation Analysis, and building autoregression moves
Dynamic averaging model ARMA (p, q), and determine p, q value:
Step 6: solving predicted value R according to the ARMA (p, q) determined in step 5t
The second layer is optimized for the optimization of production plan layer, the result that first layer is optimized and actual order taking responsibility collection
It closes, provides input for production planning optimization model, be that optimization is asked with mixed artificial bee colony algorithm and Tabu Search algorithm
Resolving Algorithm exports as corresponding production planning optimization result detail.
Second layer production planning optimization layer is using first layer prediction result and practical order taking responsibility as the defeated of Optimized model
Enter, content includes:
(1) related mathematic sign is defined:
(2) objective function of Optimized model: min (F is establishedCoTime,FMeanTime,Fcost), wherein minimize maximum completion
Time:
FCoTime=Min (max { AcCoi}),1i n；AcCoi=Coijk, 1i n, j=o, 1k m
Minimize mean completion time:
Processing cost is minimized, wherein processing cost mainly includes manufacturing procedure cost Cp, transportation cost CtIt is punished with extension
Penalize cost Cd, totle drilling cost Fcost:
Delayi=Ceil ((AcCoi-PlCoi)/60)
(3) constraint condition:
(4) artificial bee colony algorithm and TabuSearch algorithm are mixed, solution analysis is carried out to model, what is obtained is defeated
It is out effective output result of Optimizing manufacture layer；Specific step is as follows:
Step 1: coding and decoding, the coding mode based on process, the dye of each approximate optimal solution are used in HABC
All include the encoding gene for completing all manufacturing procedures in colour solid, each gene be with the symbol of task to be processed (i,
Jobi) indicate, and the number that i occurs is Ni；
Step 2: initialization of population method, initialization of population, which uses, is based on random initializtion method and Clustering wheel disc
On the one hand the mode that bet method combines ensure that the diversity of initial population individual by the chromosomal gene being randomly generated,
On the other hand guarantee legitimacy, the validity of initial population using Clustering roulette strategy；
Step 3: fitness function, fitness function F as shown by the equation:
F (x)=WCoTime*FCoTime(x)'+WMeanTime*FMeanTime(x)'+WCost*FCost(x)'
Step 4: crossover operator, selects the mode of the sequence crossover based on random task to be processed.
Refering to fig. 1-6, based on the above embodiment one, following specific embodiment two is provided:
As shown in fig. 1, a kind of enterprise's production planning optimization method flow box considering prediction order and practical order
Figure:
1, order taking responsibility prediction interval: by enterprise administrator provide from 2010 to 2015 year sales order number
According to as source data, as shown in table 1, wherein black font indicates unique value, and " * ", which is represented, repeats omitted items:
1 order taking responsibility data of table
Time series model building is carried out using Eviews9.0, conventional time series in utilization is illustrated in figure 3 and predicts
Method carries out seasonal forecasting to obtain prediction model being ARMA (5,7) to the sales order of textile machine manufacturing enterprise, such as Fig. 4 institute
It is shown as obtaining the sales order progress seasonal forecasting of textile machine manufacturing enterprise using improvement Time Series Forecasting Methods pre-
Survey model is ARMA (5,6), is learnt by comparison diagram 3 and Fig. 4, the accuracy of improved time series predicting model is higher；
According to prediction model obtain 2016 season sales order predicted value be (77.9746,80.5368,87.0927,
76.8973), the difference of the practical order volume (77,79,86,81) with 2016 is smaller, while according to left side tendency chart in Fig. 4
It learns, there are smaller differences between prediction model curve and actual value curve, meet textile machine manufacturing enterprise order forecasting need
It asks.
2, production planning optimization layer
(1) optimization object function:
Maximal Makespan target: the completion date of all tasks to be processed is not quite similar, Maximal Makespan refer to from
Zero moment starts, to the period of last procedure in all tasks to be processed completed between the moment, so minimizing
Maximal Makespan objective function is as shown by the equation:
FCoTime=Min (max { AcCoi}),1i n、AcCoi=Coijk, 1i n, j=o, 1k m
Mean completion time target: all tasks to be processed in zero moment be all it is ready, due in operation plan system
There are different machining process routes for the different processing tasks of timing, so being averaged for the same task to be processed
Be between working hour all processing tasks since zero moment to the average value for completing to expend the sum of time the moment, target letter
Number formula is as shown:
Processing cost target: processing cost mainly includes manufacturing procedure cost Cp, transportation cost CtWith extension punishment cost
Cd, totle drilling cost FcostAs shown by the equation:
Delayi=Ceil ((AcCoi-PlCoi)/60)
(2) model constraint condition
Weighted value constraint: weighted value be for the influence caused by catalogue scalar functions of reasonable disposition difference specific item scalar functions,
Therefore the sum of each weighted value should be " 1 ", as shown by the equation:
Wcost+WMeanTime+WCoTime=1
Process constraint to be processed: including at least one process route, each process route one for same task to be processed
Denier determination just immobilizes, and corresponding process sequence is also fixed in process route, it is therefore desirable to meet process processing first
After carry out, it may be assumed that
It should ensure that only have at a procedure in processed state in a certain task to be processed of synchronization simultaneously, it may be assumed that
Process equipment constraint: for a certain process equipment, one in a certain task to be processed can only be processed in synchronization
Procedure, it may be assumed that
Other constraints:
(3) mixing artificial bee colony algorithm and TabuSearch algorithm solve, specific steps are as follows:
Step 1: coding and decoding, using the coding mode based on process, in the chromosome of each approximate optimal solution
It all include the encoding gene for completing all manufacturing procedures, each gene is symbol (i, the Job with task to be processedi) table
Show, and the number that i occurs is Ni；
Step 2: initialization of population method initialization of population, which uses, is based on random initializtion method and Clustering wheel disc
On the one hand the mode that bet method combines ensure that the diversity of initial population individual by the chromosomal gene being randomly generated,
On the other hand guarantee legitimacy, the validity of initial population using Clustering roulette strategy；
Step 3: fitness function, fitness function F as shown by the equation:
F (x)=WCoTime*FCoTime(x)'+WMeanTime*FMeanTime(x)'+WCost*FCost(x)'
Step 4: crossover operator, selects the mode of the sequence crossover based on random task to be processed；
Step 5: employing the bee stage, employing the main task of bee is the every search task completed in solution space, paper
Using completing search task based on the local search approach of TS.A food source X is randomly choosed firsti；Basis is based on simultaneously
The local searching strategy of TS produces a new food sourceFood source X is calculated againiWithFitness value, ifIt is better than Xi, then useReplace Xi, otherwise XiIt does not update；
Step 6: following the bee stage, follow bee that can assess the value that each employs food source provided by bee, i.e., it is suitable
Angle value is answered, according to the superiority and inferiority of fitness value and follows probability PonlookerTo decide whether to develop current foodstuff source XiIf
Exploitation current foodstuff source is gone to, then follows bee just to will become and employs bee, will start to complete new exploitation task, otherwise will be kept
It is constant；
Step 7: the investigation bee stage, in the recovery process of food source, if do not had after the thorough search of part
Better food source Xi, employ bee that can carry food source information when returning to honeycomb, after having fed back information, can choose and turn
Investigation bee is turned to go to search new food sourceIt honeycomb can also be stayed in is converted into and follow bee, new food source is waited to believe
Breath, i.e., need more New food source at this time, to enter follow-on search；
Step 8: using first layer prediction data and practical order taking responsibility data as inputting, hybrid algorithm and other algorithms
It is solved respectively, obtained convergence curve figure is as shown in figure 5, wherein Tabu Search (E1), GATS(E2), PSO
(E3)ABC(E4), HABC (E5), from curve it can be seen that the mixing artificial bee colony algorithm and Tabu Search algorithm that propose have
There is better solving speed and solve quality, Fig. 6 show the Gantt chart of production planning optimization result.
In conclusion enterprise's production planning optimization method of this consideration prediction order and practical order, due to being looked forward to currently
When the actual production plan of industry is formulated, only consider to have signed order taking responsibility data information mostly, that ignores History Order task can
Tap value, the present invention consider these data informations comprehensively, and textile machine manufacture enterprise is coped with using the thought of hierarchy optimization
The practical formulation problem of the production plan of industry, textile machine manufacturing enterprise production planning optimization model is totally divided into two layers, first
Layer is that order taking responsibility prediction interval is improved conventional time series prediction technique and ordered to enterprise by analysis of history order data
Single task is effectively predicted；Second layer main production plan layer, i.e. simulation scheduled production layer, based on currently practical order taking responsibility,
In conjunction with business forcast order taking responsibility, a variety of manufacturing resources inside current enterprise and between enterprise optimize analysis
And modeling ensure that the reasonability that production plan is formulated to formulate for actual production plan and optimize providing method guidance
And feasibility；In addition, can preferably improve calculation using mixing artificial bee colony algorithm and Tabu Search algorithm in the present invention
The solving speed and solution quality of method, therefore can be realized to production plan and actual processing process more preferable control.
The foregoing is only a preferred embodiment of the present invention, but protection scope of the present invention be not limited to
This, anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention
And its inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (4)
1. a kind of enterprise's production planning optimization method, which is characterized in that including bilevel optimization:
A1 is collected the related data information of History Order task, handles and analyzes, and is input to order taking responsibility prediction
Model obtains corresponding prediction order taking responsibility data information with improved Time Series Forecasting Methods；
A2, by the prediction order taking responsibility data information acquired in step A1 and actual order taking responsibility set, for production meter
Optimized model offer input is drawn to export using mixed artificial bee colony algorithm and Tabu Search algorithm as Optimization Solution algorithm
To corresponding production planning optimization result detail.
2. a kind of enterprise's production planning optimization method for considering prediction order and practical order as described in claim 1, special
Sign is that the prediction model in the first layer order taking responsibility prediction interval is based on it is assumed hereinafter that condition:
(1) prediction model is only applicable within the scope of some cycles, i.e. T ∈ [Tl,Tu], and the product for including in source data belongs to together
One type, different size；
(2) seasonality of textile machine product other three attribute that compare are more obvious, no longer individually consider in solving model
Tf t, Cyf t, Rf t, but consider as an entirety, i.e.,Rt=Ft(Tf,Sf,Cyf,Rf)=
Tf t*Sf t*Cyf t*Rf t；
Wherein, T is predetermined period, TlFor predetermined period start time, TuFor predetermined period finish time,For in t moment trend
Sexual factor impact factor caused by order taking responsibility prediction result,Order taking responsibility is predicted to tie to recycle sexual factor in t moment
Impact factor caused by fruit,For in t moment random factor impact factor caused by order taking responsibility prediction result, Sf tTable
Show in t moment Seasonal impact factor caused by order taking responsibility prediction result.
3. a kind of enterprise's production planning optimization method as claimed in claim 2, which is characterized in that the order taking responsibility predicts mould
Specific step is as follows for type:
S1: it is as follows to construct order volume initial matrix according to initial value for parameter setting:
S2: S is solvedf t, matrix N is converted according to four displacement methods of average, is obtained:
N' is converted according to the method for average placed in the middle, is obtained:
It is obtained according to formula S=N/N ":
Using mean value method, four seasonal effect factors are solved:
JudgementWhether 4 are equal to, if be equal to,It remains unchanged, otherwise needs using correction factor method, it may be assumed that
S3: O is solvedf t, according to formulaIt can acquire:
S4: regression equation is solved according to the data that step S3 is obtained and converts one-dimensional sequence for matrix, it may be assumed that
In order to preferably embody, t value is serialized, above-mentioned sequence amendment are as follows:
Of=O1,O2,...,Ot',...,Ot*4, t'=1,2 ..., t*4 }；
S5: using Eviews9.0 to above-mentioned OfSequence carries out auto-correlation and partial Correlation Analysis, constructs ARMA model
ARMA (p, q), and determine p, q value:
S6: according to the ARMA (p, q) determined in step S5, predicted value R is solvedt。
4. a kind of enterprise's production planning optimization method as described in claim 1, which is characterized in that the second layer production plan
Using first layer prediction result and practical order taking responsibility as the input of Optimized model, content includes: optimization layer
(1) related mathematic sign is defined:
(2) objective function of Optimized model: min (F is establishedCoTime,FMeanTime,Fcost), wherein minimizes Maximal Makespan:
FCoTime=Min (max { AcCoi}),1≤i≤n；AcCoi=Coijk, 1≤i≤n, j=o, 1≤k≤m；
Minimize mean completion time:
Processing cost is minimized, wherein processing cost mainly includes manufacturing procedure cost Cp, transportation cost CtWith extension punishment cost
Cd, totle drilling cost Fcost:
Fcost=Cp+Ct+Cd、
Delayi=Ceil ((AcCoi-PlCoi)/60)；
(3) constraint condition:
Wcost+WMeanTime+WCoTime=1,
(4) artificial bee colony algorithm and Tabu Search algorithm are mixed, solution analysis is carried out to model, obtained output is i.e.
For the effective output result for producing optimization layer；Specific step is as follows:
S1: coding and decoding, and the coding mode based on process is used in HABC, is wrapped in the chromosome of each approximate optimal solution
Containing the encoding gene for completing all manufacturing procedures, each gene is symbol (i, the Job with task to be processedi) indicate, and i
The number of appearance is Ni；
S2: initialization of population method, initialization of population, which uses, is based on random initializtion method and Clustering wheel disc bet method phase
In conjunction with mode, on the one hand ensure that the diversity of initial population individual, another aspect by the chromosomal gene that is randomly generated
Guarantee legitimacy, the validity of initial population using Clustering roulette strategy；
S3: fitness function, fitness function F as shown by the equation:
F (x)=WCoTime*FCoTime(x)'+WMeanTime*FMeanTime(x)'+WCost*FCost(x)'；
S4: crossover operator selects the mode of the sequence crossover based on random task to be processed.
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