CN105550751B - Utilize the steel-making continuous casting production scheduling method of priority policy genetic algorithm - Google Patents

Utilize the steel-making continuous casting production scheduling method of priority policy genetic algorithm Download PDF

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CN105550751B
CN105550751B CN201510934188.1A CN201510934188A CN105550751B CN 105550751 B CN105550751 B CN 105550751B CN 201510934188 A CN201510934188 A CN 201510934188A CN 105550751 B CN105550751 B CN 105550751B
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郑忠
徐兆俊
高小强
龙建宇
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Abstract

The present invention proposes a kind of steel-making continuous casting production scheduling method using priority policy genetic algorithm, includes the following steps：Production scheduling planned target function is established, establishes constraint condition set, computing is iterated to object function using priority policy genetic algorithm, asks for decision variable, is specially：Carry out model initialization；Calculate feasible solution：It devises and real coding is combined by the segmented of conticaster casting time information and heat process equipment information structure, randomly generate the activity duration according to the regularity of distribution, the operation plan of no time conflict is obtained by retrodicting calculating and conflict resolving method；Population genetic optimizes：The processing weight assignment of equipment is can perform come the matching relationship between process equipment in quantificational description reality with task, and introduces genetic manipulation in the form of equipment selects priority policy, carries out Evolution of Population.This method can solve equipment selection and the uncertain problem of activity duration in production, the executable production scheduling plan optimized.

Description

Utilize the steel-making continuous casting production scheduling method of priority policy genetic algorithm
Technical field
The present invention relates to technical field of metallurgical control, and in particular to a kind of refining using priority policy genetic algorithm Steel continuous casting dispatching method.
Background technology
Steel-making continuous casting production scheduling is the important component of iron and steel enterprise's production management.In reality produces, due to life Environment and working condition are produced there are a variety of uncertainties, the effect that production scheduling plan is difficult to carry out or performs can be caused to be limited. Therefore, the reasonable efficient formulating method of executable production scheduling plan is studied, to promoting the overall operation efficiency of production system, drop Low material consumption, energy consumption and cost etc. are of great significance.
In recent years, steel-making continuous casting production scheduling problems are as research hotspot, mainly around modeling and model solution method into Row.Modeling method mainly has Mathematical Planning modeling, mathematics library and simulation modeling etc., wherein, Mathematical Planning modeling is most often Model describes method.The optimization method of problem mainly has optimal method and near-optimal method.Optimal method Including mathematical methods such as Mathematical Planning, branch boundary and Lagrangian methods, this kind of method is for small-scale production scheduling problems It can effectively solve.Also mixed integer linear programming model, for describing the process constraint in STEELMAKING PRODUCTION and solving steel-making Continuous casting scheduling problem, model have made standardization to the activity duration in addition to continuous casting working procedure.Near-optimal method is main Including a variety of intelligence computation methods (genetic algorithm, ant group algorithm, ant colony algorithm etc.), heuristic, artificial intelligence approach with And mixed method of a variety of methods etc..Near-optimal method for solving has higher solution efficiency, more towards practical application, but The reasonability of solving result also needs further to examine.
At present, the production scheduling problems under condition of uncertainty start to attract attention.Existing method mainly employs at random Variable, fuzzy theory and rough set theory etc. handle uncertain problem.Also have in the prior art and utilize Generalized Rough Sets The modeling method of theoretical description uncertain variables, and solved with differential evolution algorithm；And it represents uncertain using fuzzy number and adds Work temporal information establishes the Fuzzy Programming Model of steel-making continuous casting production scheduling problems and is solved.This kind of method mainly collects In in production scheduling time uncertainty problem, to the uncertain problem of equipment selection, usually to assume devices dispatch Simplified process methods unrestricted or using heuristic dispatching rules, so may result in production scheduling plan and are difficult to hold The problems such as row.
The content of the invention
In order to overcome above-mentioned defect in the prior art, priority policy is utilized the object of the present invention is to provide a kind of The steel-making continuous casting production scheduling method of genetic algorithm, this method can solve equipment selection and activity duration in production Uncertain problem, the executable production scheduling plan optimized.
In order to realize the above-mentioned purpose of the present invention, the present invention provides a kind of using priority policy genetic algorithm Steel-making continuous casting production scheduling method, includes the following steps：
S1, steel-making continuous casting scheduling controller are connected and obtain with the MES data storehouse of steel mill and MES FTP client FTPs respectively Steel-making continuous casting planning data in the MES data storehouse of steel mill and MES FTP client FTPs；
S2, establishes production scheduling planned target function, and the object function is：
MinZ=α1×f1+α2×f2
Wherein, α1Deviate rejection penalty coefficient caused by predetermined casting time, α for conticaster2Etc. for heat in production Treat rejection penalty coefficient caused by the time；α1And α2It is all algorithm adjustable parameter.
f1Represent that each conticaster deviates the summation of predetermined casting time amount, f2Represent that respectively pouring time interior all heats is producing The summation of stand-by period in the process, is embodied as：
Wherein, L is the quantity of conticaster；TqFor q platform conticasters predetermined casting time；tqIt is actual for q platforms conticaster Casting time, i.e.,
N is heat sum, and i is processing heat, and M is total for process, ΩjFor the collection of heat i available devices on process j It closes,It is to be heat i in process j-1 equipment kj-1With process j equipment kjBetween haulage time；Process j-1 is The precedence activities of process j, equipment kj-1For equipment kjTight preceding equipment；
KjFor all devices set on process j,It is heat i in process j equipment kjBefore upper operation Stand-by period；
It is heat i in process j equipment kjAt the beginning of upper processing；It is heat i in precedence activities j-1 Equipment kj-1At the beginning of upper；It is heat i in precedence activities j-1 equipment kj-1On end time；For Heat i is in precedence activities j-1 equipment kj-1On process time；
S3, establishes constraint condition set, and the constraint condition set includes one of following constraints or any combination：It respectively pours secondary Constraints, heat limited stand-by period constraints before process are poured by the company of interior heat, and machine capability is extracted constraints, stove The secondary constraints being at most processed on every procedure once, same heat will can just carry out down when previous process processes The constraints of one process processing, equipment can use constraints, optional equipment constraints, time uncertainty in production earliest Constraints；
S4 is iterated computing to object function using priority policy genetic algorithm, asks for decision variable, specifically Algorithm include the following steps：
S41 carries out model initialization：Input model parameter information and initial time genetic algorithm iteration count t=0, Generate the initial population P (t) that population scale is Q；
S42 calculates feasible solution：Definite chromosome coding (is united according to Time Distribution according to steel grade and homework type Meter) activity duration is randomly generated, no time conflict is obtained according to the conflict resolving method retrodicted in calculating and equipment of inverse process Production scheduling plan；
S43 carries out population genetic optimization：According to the task processing weight of equipment, selected by combining equipment in actual environment Hereditary variation, the crossover operation of priority policy are selected, carries out Evolution of Population, optimization is generated and solves and export；
S5, controller carry out the shop job scheduling of steel smelting-continuous casting production according to obtained optimal solution, and to production run System implements effectively control.
The steel-making continuous casting production scheduling method using priority policy genetic algorithm of the present invention is selected for equipment Uncertain problem, it is proposed that assign equipment task processing weighted value quantificational description method, and with equipment select priority The form combination genetic algorithm of strategy forms the Optimization Solution algorithm of model, can solve the steel-making continuous casting under condition of uncertainty Scheduling problem.
The additional aspect and advantage of the present invention will be set forth in part in the description, and will partly become from the following description It obtains substantially or is recognized by the practice of the present invention.
Description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination accompanying drawings below to embodiment Substantially and it is readily appreciated that, wherein：
Fig. 1 is steel smelting-continuous casting production process schematic diagram in the prior art；
Fig. 2 is algorithm stream of the present invention using the steel-making continuous casting production scheduling method of priority policy genetic algorithm Journey；
Fig. 3 is the steel mill production procedure schematic diagram in a kind of preferred embodiment of the present invention；
Fig. 4 is the production scheduling plan Gantt chart using the genetic algorithm establishment of the present invention；
Fig. 5 is simulation calculation value and the production for the flow activity duration that each heat is cast since converter tapping to conticaster Actual achievement compares figure.
Specific embodiment
The embodiment of the present invention is described below in detail, the example of the embodiment is shown in the drawings, wherein from beginning to end Same or similar label represents same or similar element or has the function of same or like element.Below with reference to attached The embodiment of figure description is exemplary, and is only used for explaining the present invention, and is not considered as limiting the invention.
Fig. 1 is 3 production links that existing steel smelting-continuous casting process mainly includes：Steel-making, refining and continuous casting.Steel-making is with connecting Casting link respectively contains a parallel unit, and refines link and generally comprise multiple parallel units, to realize different refinery practices It is required that.General steel smelting-continuous casting production process is as shown in Figure 1：The high temperature liquid iron shipped from blast furnace is through hot metal pre process procedures After be blended into converter smelting into molten steel, molten steel is poured into the ladle under converter on trolley, by the hauling operation of overhead traveling crane and trolley, Steel ladle is transported to refining link, and according to the manufacturing technique requirent successively refined molten steel on different refining equipments, refining is completed Afterwards, then by overhead traveling crane and trolley, steel ladle is transported to continuous casting and implements to cast, forms strand.
In steel mill's production scheduling, heat refers to the molten steel that some converter produces in a smelting cycle, due to one The molten steel of heat is loaded into a ladle, so the object being scheduled before from steel-making to continuous casting is heat, heat is steel mill Minimum production unit in production scheduling.The secondary heat set for referring to the direct casting on same conticaster is poured, is steel mill's production Maximum production unit in scheduling.Steel smelting-continuous casting scheduling scheme formulates flow：User's contract is turned according to technical standard first Turn to production contract；Then charging plan and casting plan are worked out according to steel capacity and technological requirement etc., and combines heat The establishment hot rolling modular plan such as rolling power and technological requirement is rolled, the production batch that steel-making is mutually coordinated with hot rolling production is formed and counts It draws.In Production Lot Planning, it has been determined that pour secondary casting conticaster and pour time processing sequence of interior heat and production Technique.
Steel-making continuous casting production scheduling be on the basis of Production Lot Planning, using within the plan phase it is several pour time in heat as Minimum planning unit, in the case where pursuing the best results of composite evaluation function on the premise of meeting production technology constraint, peace It arranges and is produced on several equipment K in N number of heat to M procedures.It is productions of the heat i to be determined from converter to continuous casting Cheng Zhong selects appropriate processing apparatusTo perform the processing tasks such as steelmaking-refining-continuous casting, and determine that heat exists At the beginning of being processed in equipmentProcess timeAnd the end time
And there are i ∈ (1, N), j ∈ (1, M), k ∈ (1, Kj),KjFor the set of all devices on process j.
The present invention is in order to solve the steel-making continuous casting scheduling problem under condition of uncertainty, it is proposed that is mixed using priority policy The steel-making continuous casting production scheduling method of genetic algorithm, specifically comprises the following steps：
The first step：Steel-making continuous casting scheduling controller respectively with the MES of steel mill (Manufacturing Execution Systems, manufacturing execution system) database and MES FTP client FTPs connect and obtain the MES data storehouse of steel mill and MES visitors Steel-making continuous casting planning data in the end system of family.Steel-making continuous casting scheduling problem under condition of uncertainty is solved according to data above.
Second step：Establish solving model, the basic assumption of model of the present invention：1. known batch plan；2. known conticaster Predetermined casting time (conticaster is taken to adopt the empirical value of the predetermined casting time after pouring the moment, specific empirical tests can be opened earliest With the empirical data of corresponding steel mill)；3. heat along production procedure process when, logistics time is made of three parts：Before process Stand-by period, the activity duration in process and the haulage time for being transported to next process；4. known heat is in each process The regularity of distribution of upper activity duration and inter process haulage time is (by the real production operation time counted according to former produce reality Statistics obtains)；5. each equipment earliest available time at known planning moment.
JIT production principle and mistake high latency in being produced in view of reality can generate shadow to molten steel metallurgical performance It rings, sets to minimize conticaster and deviate predetermined casting time and heat the stand-by period caused rejection penalty jointly before process For the optimization aim of production scheduling.
Steel-making continuous casting production scheduling planning model object function is：
MinZ=α1×f1+α2×f2 (1)
It needs to be determined that decision variable be：
Wherein, α1Deviate rejection penalty coefficient caused by predetermined casting time, α for conticaster2Etc. for heat in production Treat rejection penalty coefficient caused by the time.
f1Represent that each conticaster deviates the summation of predetermined casting time amount, f2Represent that respectively pouring time interior all heats is producing The summation of stand-by period in the process, is embodied as：
Wherein, L is the quantity of conticaster；TqFor q platform conticasters predetermined casting time；tqIt is actual for q platforms conticaster Casting time, i.e.,
N is heat sum, and i is processing heat, and M is total for process, ΩjFor the collection of heat i available devices on process j It closes,It is to be heat i in process j-1 equipment kj-1With process j equipment kjBetween haulage time；Process j-1 is The precedence activities of process j, equipment kj-1For equipment kjTight preceding equipment；
KjFor all devices set on process j,It is heat i in process j equipment kjBefore upper operation Stand-by period；
It is heat i in process j equipment kjAt the beginning of upper processing；It is heat i in precedence activities j-1 Equipment kj-1At the beginning of upper；It is heat i in precedence activities j-1 equipment kj-1On end time；For Heat i is in precedence activities j-1 equipment kj-1On process time；
3rd step：Establish constraint condition set, it is main even poured including conticaster, the constraint that molten steel production technology etc. routinely produces Condition.Also, appointed for equipment selection and the uncertain problem of activity duration, consideration present in description production process with equipment The constraint and take random number with according in real activity duration statistical distribution that business processes weighted value non-zero to represent optional equipment Mode represent the random constraints of activity duration.
Constraint condition set includes one of following constraints or any combination：It respectively pours time company of interior heat and pours constraints, Heat limited stand-by period constraints before process, machine capability are extracted constraints, heat most quilt on every procedure The constraints of processing once, same heat will process the constraint item that can just carry out subsequent processing processing when previous process Part, equipment can use constraints, optional equipment constraints, time uncertainty constraints in production earliest.
Specifically constraints is：
1) respectively pour time company of interior heat and pour constraints：
Wherein, i+1 is next heat that heat i is processed in same equipment；For end process M equipment kMIt is upper adjacent Because of the off time that less important work generates between heat, side steel ladle to be poured is turned into casting by rotating including ladle turret Time needed for side；
2) heat Shops With Limited Waiting Times constraints before process：
Wherein,It is heat i in process j equipment kjThe maximum allowable stand-by period before upper operation；
3) machine capability is extracted constraints, i.e. machine can only process a task simultaneously：
Wherein,For process j equipment kjBecause of the off time of less important work generation between upper adjacent heat；
4) heat is at most processed constraints once on every procedure：
5) same heat will process the constraints that can just carry out subsequent processing processing when previous process：
Wherein, j-1 be same heat processing route in process j precedence activities；It is heat i in work Sequence j-1 equipment kj-1With process j equipment kjBetween haulage time；
6) equipment can use constraints earliest, i.e., in each equipment the beginning activity duration of the first heat be later than the equipment most When early available：
Wherein,For process j equipment kjThe beginning activity duration of upper first heat,It is set for initial time process j Standby kjEarliest available time, the initial time refers to the earliest available time of planning moment each equipment；
7) optional equipment constraints, i.e. heat can only select the equipment of the processing weighted value non-zero of task on successor activities into Row processing：
ω[(j-1,kj-1),(j,kj)] >=0,
Wherein, ω [(j-1, kj-1),(j,kj)] it is heat from process j-1 equipment kj-1Go to successor activities j equipment kjOn Task processes weight；
8) time uncertainty constraints in producing, i.e. equipment process time and equipment room haulage time is in a certain range Interior fluctuation；
Wherein,WithRespectively process j-1 equipment kj-1With process j equipment kjIt Between minimum and maximum haulage time,WithRespectively heat i is in process j equipment kjDuring upper minimum process Between and maximum process time.
4th step：Computing is iterated to object function using priority policy genetic algorithm, asks for decision variable, Algorithm flow includes model initialization, feasible solution generates and swarm optimization three phases.Wherein feasible solution generation and swarm optimization Stage constitutes the substep decision optimization solution procedure of model.As shown in Fig. 2, in the model initialization stage, the manufacture of input steel mill The structural information of flow network, batch plan information and various parameters information involved by algorithm from Production database (including uniting The activity duration distribution of meter, genetic algorithm parameter etc.), and initial time genetic algorithm iterator.Stage, root are generated in feasible solution According to definite chromosome coding, the activity duration is randomly generated according to Time Distribution, calculates and sets according to retrodicting for inverse process Standby upper conflict resolving writes respective algorithms and obtains the production scheduling plan of no time conflict.Present in production process The uncertainty of activity duration allows the above process to cycle and carries out, and by repeatedly randomly generating for activity duration, is formed for excellent Neutralizing search as determine chromosome coding expressed by solution space.In the swarm optimization stage, by the search to solution space, with And combine actual environment in equipment selection priority policy genetic manipulation, carry out Evolution of Population, until generate optimization solution and it is defeated Go out.
In the present invention, after next-generation population is generated, return to and carry out feasible solution calculating (for a Xun Huan), this hair It is bright that loop iteration number can be set, the threshold value exited can also be set, when loop iteration number reaches setting value or front and rear The error between optimal solution twice, which is less than, exits threshold value, then terminates iteration.
In real production process, due to the variation of production procedure placement differences and equipment state, the equipment of each process it Between matching relationship be dynamic change.It is devised therefore with task processing weights omega [(j-1, kj-1),(j,kj)] quantify to retouch State it is this kind of can between process equipment Dynamic Matching relation method.ω[(j-1,kj-1),(j,kj)] heat is represented from process j- 1 equipment kj-1Go to successor activities j equipment kjTask processing weight, the size of value embody the reachable state of equipment room with And the priority height of equipment selection.Specific quantization method criterion represents as follows：
If criterion 1 causes process j equipment k because of production distribution or equipment fault etc.jFor process j-1 equipment kj-1It is unreachable Equipment, then：ω[(j-1,kj-1),(j,kj)]=0.
If only has equipment k on criterion 2, process jjFor process j-1 equipment kj-1Reachable device,
Then：ω[(j-1,kj-1),(j,kj)]=1.
If there are multiple process j-1 equipment k on criterion 3, process jj-1Reachable device
Then：
It follows that ω [(j-1, kj-1),(j,kj)] maximum is 1, process j-1 equipment k at this timej-1With process j equipment kj For equipment matching status.
More than quantificational description method is based on, weights omega [(j-1, k are processed by taskj-1),(j,kj)] equipment can be formed The priority policy of selection, i.e.,：Task processing weights omega [(j-1, kj-1),(j,kj)] value is bigger, represent select the equipment into The priority of row task processing is higher, and then task processing weight is introduced into the friendship of genetic algorithm in the form of priority policy again In fork, mutation operation, the probability that genetic algorithm is evolved towards feasible orderly direction is added, is conducive to generate optimization solution.
Specific algorithm of the invention is as shown in Fig. 2, include the following steps：
S41 carries out model initialization：Input model known parameters information and initial time genetic algorithm iteration count t =0, the initial population P (t) that population scale is Q is generated, is specifically randomly generated according to chromosome coding, population is by dyeing Body is formed, and chromosome is made of two piece of digital coding.It so generates to form digital coding by random number, just can generate dye Colour solid.Initial population is just constituted when generating Q chromosome.
S42 calculates feasible solution：Definite chromosome coding (is united according to Time Distribution according to steel grade and homework type Meter) activity duration is randomly generated, no time conflict is obtained according to the conflict resolving method retrodicted in calculating and equipment of inverse process Production scheduling plan.
Equipment selection for executive plan in production process is uncertain, and the present invention was constructed comprising conticaster casting time The piecewise combination real coding mode of information and each heat machining path information, wherein coding first portion contains L platform continuous castings The casting time of machine, second portion contain the machining path of M heat task, i.e., the letter that equipment selects in each process Breath.The chromosome coding used for：
C={ (T1,T2,...,TL),(a1b1f1g1,a2b2f2g2,...,aNbNfNgN)},
Wherein, (T1,T2,...,TL) encoded for casting time, (a1b1f1g1,a2b2f2g2,...,aNbNfNgN) it is each heat Machining path encodes, T1It is 1# conticaster casting times, TLFor the casting time of L# conticasters, aNbNfNgNFor the processing of N# heats Path is：A# converters-b#LF (ladle refining furnace)-f#RH (vacuum circulation degassing refining furnace)-g# conticasters.
For example, chromosome C={ (T1, T2…TL), (1201,3211 ... 3122) },
Wherein, T11# conticaster casting times are represented as T1min；TLThe casting time of L# conticasters is represented as TLmin；1201 represent the production path of 1# heats as:1# converters -2#LF -1# conticasters；3122 represent the processing of N# heats Path is：3# converter -1#LF -2#RH -2# conticasters.
It based on above coding mode, is decoded it during Algorithm for Solving, each heat can be obtained along production procedure Equipment selection information.By randomly generating process time and haulage time by the statistical distribution of activity duration, then using retrodicting With conflict resolving algorithm, the operation plan without time conflict of homologue can be obtained.
Backward algorithm is：
Setup algorithm initial value is N-free diet method production process, i.e.,
Activity duration used in calculating and haulage time are using the random value generated according to actual distribution rule, specific steps For：
S1 makes j=M, and by conticaster casting time information in batch plan information and chromosome, calculates all tasks In last procedure, i.e., the beginning and end activity duration on continuous casting working procedure, the beginning activity duration of each heat：It is to add process time with the beginning activity duration to terminate the activity duration, i.e.,：
S2 produces routing information according to each heat in chromosome, by the activity duration randomly generated and haulage time, falls Beginning and end activity duration of each heat task on precedence activities j-1 on process j is released, is specially：
End activity durations of the heat i on precedence activities j-1：
Beginning activity durations of the heat i on precedence activities j-1：
S3, each station conflicts with the presence or absence of the activity duration in inspection operation (j-1), if so, application collision elimination algorithm solution Certainly,
S4, whether judgment step (j-1) is the first procedure of production procedure (converter), if (j-1) ＞ 1, makes j=j-1, Go back to step S2；Otherwise step S5 is performed；
S5 can use constraint by station：It is constrained with the limited stand-by period：Mark the production Whether operation plan is reasonable.
In the present embodiment, backward algorithm can also use the existing backward algorithm having disclosed.
Task conflict removing method in equipment is：
Conflict resolving algorithm is conflict time size between the adjacent heat of statistics： It takes adjustment haulage time, activity duration and increases the stand-by period to eliminate.Wherein, the activity duration is by steel grade and homework type It influences, adjustable extent is smaller；And haulage time is related with machining path, adjustable extent is larger.Specific adjustable strategies are as follows：
Strategy 1, if the conflict time is more than adjustment haulage time, activity duration and the total maximum energy for increasing the stand-by period Power, the then time that conflicts all are included in the stand-by period, and mark the production plan unqualified,
I.e.：If
Strategy 2, if the conflict time is less than adjustment haulage time and total maximum capacity of activity duration, the time that conflicts lead to Adjustment haulage time and activity duration are spent to eliminate,
I.e.：If
Strategy 3, if conflicting the time between both the above situation, by adjusting haulage time, activity duration and increasing Add the stand-by period to eliminate,
I.e.：
S43 carries out population genetic optimization：According to the task processing weight of equipment, selected by combining equipment in actual environment Hereditary variation, the crossover operation of priority policy are selected, carries out Evolution of Population, optimization is generated and solves and export.
In genetic algorithm, selection operation selects high-quality individual to abandon worst individual by fitness.The present invention uses To ensure that obtained optimum individual will not be intersected in genetic process and mutation operation destroys, this is optimal individual conserving method The important guarantee condition of genetic algorithm convergence.
In view of the chromosome for forming solution is made of two sections of codings of conticaster casting time and plan execution equipment, and Two coded data structures are different, therefore chromosome is carried out using segmentation genetic manipulation mode：Conticaster casting time part Coding using arithmetic crossover and disturbance variation method；And plan to perform the coding of environment division, it is by the way that equipment is selected Priority policy is introduced into genetic manipulation, and the intersection in uniform crossover operator is determined according to task processing weighted value in each equipment Mutation probability in probability and basic bit mutation operation.Genetic manipulation algorithm flow is described as follows：
Step1：Remaining chromosome in previous generation population P (t) in addition to optimum individual is stored in set omegap(t)In, and make Chiasma counter ic=1；
Step2：Crossover operation.From set omegacIn randomly select two parent chromosome C1And C2, and with probability PcIt carries out Intersect, obtain two child chromosome C '1With C '2。
Step3：From set omegap(t)Middle deletion chromosome C1And C2, and by chromosome C '1With C '2It is stored in set omegap(t+1)′ In.If set omegap(t)It is not sky, makes ic=ic+ 2, return to Step2；Otherwise Step4 is performed.
Step4：Make chromosomal variation counter im=1
Step5：Mutation operation.From set omegap(t+1)′Middle extraction parent chromosome C1', and with probability PmInto row variation, Obtain child chromosome C "1；
Step6：From set omegap(t+1)′Middle deletion chromosome C1', and by chromosome C″1It is stored in set omegap(t+1)In.If collection Close Ωp(t+1)′It is not sky, makes im=im+ 1, return to Step5；Otherwise terminate, set omegap(t+1)In chromosome and previous generation population in Optimum individual collectively constitute population P (t+1) of new generation.
The implementation method of crucial genetic manipulation part is：
(1) crossover operation：
1) conticaster is deviateed into predetermined casting time total amount one of target as an optimization in model, therefore the coding of casting time Arithmetic crossover operation is used to avoid causing significantly to deviate showing for predetermined casting time after crossing over many times to control its excursion As so as to improve the reasonability of production plan and enforceability.
In crossover operation, the coding crossover operation method of casting time is：
CT1'=r × CT1+(1-r)×CT2
CT2'=r × CT2+(1-r)×CT1
Wherein, random numbers of the r between [0,1], CT1, CT2It is encoded for the casting time of parent chromosome, CT1', CT2′ It is encoded for the casting time of child chromosome.
2) coding of each heat machining path uses uniform crossover method, i.e. each corresponding gene of two parents is all with one Determine crossover probability to swap.That is, the gene place value of process j is corresponded in two parent chromosome substrings of heat iWith Crossover probability P (j) values be：
The size of P (j) values is depending on process j equipment in child chromosome after intersectingCorresponding to process j+1 equipment Processing weighted valueWith process j equipmentCorresponding to process j+1 equipmentProcessing weighted valueThe legitimacy of child chromosome after intersecting can be ensured to the non-zero effect core of P (j) values.Meanwhile P (j) values are bigger, then the code segment is developed with the probability of bigger to equipment matching status more preferably machining path, is conducive to produce Raw optimization solution.
(2) mutation operation：
1) each conticaster casting time coding variation：Increase or decrease a random perturbation
I.e.：
Random quantitys of the β between [a, b], [a, b] are the relatively microvariations section manually set, specifically can be according to produce reality It is required that setting, significantly deviates predetermined casting time to avoid conticaster casting time after multiple variation,It is opened for conticaster before variation Pour the time,For conticaster casting time after variation.
2) heat production path code takes basic bit mutation, that is, is randomly assigned some gene positions and (removes and correspond to continuous casting work The gene position of sequence) into row variation.When performing mutation operation for the chromosome substring of heat i, correspond to the gene position of process j ValueMake a variation into miscellaneous equipment in the processMutation probability p (j) be：
J+1 be same heat processing route in process j successor activities.
P (j) values size is depending on process j-1 equipment k in child chromosome after variationj-1Corresponding to process j equipmentPlus Work weighted valueWith process j equipmentCorresponding to process j+1 equipment kj+1Processing weighted valueWhether the new chromosome that can be imitated after core variation to the non-zero judgement of p (j) values is legal.Meanwhile p (j) value is bigger, then the code segment is developed with greater probability to equipment matching status more preferably machining path, is conducive to solution more Optimization.
In the present invention, process in weight value function and its dependent variable (such asUsing The form that although has of independent variable it is different, but specific computational methods are consistent.Specifically the criterion all in accordance with the present invention determines.
5th step：Controller carries out the shop job scheduling of steel smelting-continuous casting production according to obtained optimal solution, and to production Runtime implements effectively control.
In the preferred embodiment of the present invention, the not minimum value of calculating target function in genetic algorithm, and It is the maximum for calculating fitness function, fitness function uses following form：
Z be model objective function value, CmaxFor a sufficiently large positive number, such as CmaxMore than z.
In a preferred embodiment of the present invention, calculated with the production scheduling planning example of certain steel mill The inspection of method and application test.The steel mill has pneumatic steelmaking-LF refining-RH refinings-main production process of continuous casting four, life It is as shown in Figure 3 to produce general flow chart.Statistics production actual achievement data have obtained each steel grade process time and equipment room fortune in each process The regularity of distribution of defeated time.Casting plan according to certain time in reality production carries out emulation experiment, as shown in table 1.Heredity Algorithm parameter is arranged to:Population Size 200, evolutionary generation 100, crossover probability PcFor 0.8, mutation probability PmFor 0.01.Through excessive Secondary experiment, the production scheduling plan that genetic algorithm can be optimized in 50s, as shown in Figure 4.
1 casting plan of table
Fig. 5 is the flow activity duration that inside the plan each heat (1≤i≤40) is cast since converter tapping to conticaster The comparison of simulation calculation value and production actual achievement, the genetic algorithm including combining present device selection priority policy (HybridGeneticAlgorithm, HGA) simulation calculation value, it is basic with unbonded present device selection priority policy Genetic algorithm (SimpleGeneticAlgorithm, SGA) simulation calculation value and the comparison for producing actual achievement value.Table 2 gives Simulation calculation and the assembly average of the flow activity duration of production actual achievement, are superior to by the calculated value for relatively understanding HGA and SGA Actual achievement is produced, and HGA effect of optimization becomes apparent.This is because not optimized in reality production to the production schedule, cause Heat often has longer transportation range and a longer stand-by period in real production.Meanwhile Fig. 5 also shows the activity duration Simulation calculation value HGA methods to be more they tended to steadily compared with SGA methods, the reason is that due in not optimized SGA genetic manipulations, The randomness bigger of equipment selection, it is increasingly complex and unreasonable to cause the production path of indivedual heat processing tasks, so as to Add the whole process activity duration.
2 simulation calculation of table and the assembly average of the flow activity duration of production actual achievement
In reality produces, select different machining paths that can generate different influences to transit link, as equipment matches Poor machining path usually has swivel link in longer transportation range or more complicated transport, and logistics is easier on the way Cause larger temperature drop due to wait.Therefore can with inter process equipment matching rate come weigh logistics can be smooth in transit link Operation.
The calculation formula of adjacent inter process equipment matching rate is：
Wherein equipment matching refers to task processing weighted value ω [(j-1, k on heat selection successor activitiesj-1),(j,kj)] most Big equipment production, i.e. ω [(j-1, kj-1),(j,kj)]=1.
The present invention is to introducing the genetic algorithm (HGA) of priority policy and being not introduced into the basic genetic of priority policy Algorithm (SGA) has carried out 10 emulation experiments respectively under identical input condition, wherein to equipment matching rate between converter-LF ηBOF-LF, equipment matching rate η between LF-CCLF-CCWith solution fitness function value y eventuallyEComparative result it is as shown in table 3.
3 the simulation experiment result of table compares
Contrast and experiment, the fitness function average value of last solution is suitable, thus two kinds of genetic algorithms are reducing molten steel Stand-by period is suitable with the optimization performance on JIT production is ensured.But due to being not introduced into the letter of equipment selection priority policy Single genetic algorithm is in genetic process, randomness of the selection with bigger of production equipment, thus the change of property indices value Change amplitude is larger, and compared to relatively low on inter process equipment matching rate.And the mixing for introducing equipment selection priority policy is lost Operation is passed, during evolution, chromosome preferably produces path towards equipment matching relationship with greater probability and develops, last solution Equipment matching rate be averagely higher by 15% than simple generic algorithm, the reasonability of solution is more excellent.
The present invention exists from the angle that is conducive to the plan of steel-making continuous casting production scheduling and can perform effectively in being produced for reality Equipment selection and the activity duration uncertain problem, it is proposed that based on equipment selection priority policy steel-making continuous casting scheduling The modeling method of plan and corresponding blending heredity derivation algorithm.Model employ activity duration distribution function with Machine variable-value method describes the uncertainty of production operation time；And propose the processing weight that can perform equipment with task Assignment is come matching relationship carries out quantificational description to solve the uncertain problem of equipment selection process equipment in being produced reality； Equipment selection priority policy is introduced into the genetic manipulation of genetic algorithm simultaneously and forms the excellent of genetic algorithm progress model Change and solve.By the substep decision mode search solution space for generating feasible solution, carrying out majorization of solutions again, convenient can be optimized Executable steel-making continuous casting operation plan.For certain steel mill steel-making continuous casting operation plan establishment example simulation calculation, Results showed that when the priority policy genetic algorithm proposed has optimization heat machining path, shortens flow operation Between, improve the effect of equipment matching rate, demonstrate the validity of model and algorithm.Further research will be based on the algorithm The steel-making continuous casting dynamic scheduling problem field under reality production disturbance is expanded to, to better conform to the demand of complicated production.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show The description of example " or " some examples " etc. means specific features, structure, material or the spy for combining the embodiment or example description Point is contained at least one embodiment of the present invention or example.In the present specification, schematic expression of the above terms is not Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any One or more embodiments or example in combine in an appropriate manner.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not In the case of departing from the principle of the present invention and objective a variety of change, modification, replacement and modification can be carried out to these embodiments, this The scope of invention is limited by claim and its equivalent.

Claims (10)

  1. A kind of 1. steel-making continuous casting production scheduling method using priority policy genetic algorithm, which is characterized in that including such as Lower step：
    S1, steel-making continuous casting scheduling controller are connected with the MES data storehouse of steel mill and MES FTP client FTPs and obtain steel-making respectively Steel-making continuous casting planning data in the MES data storehouse of factory and MES FTP client FTPs；
    S2, establishes production scheduling planned target function, and the object function is：
    MinZ=α1×f1+α2×f2
    Wherein, α1Deviate rejection penalty coefficient caused by predetermined casting time, α for conticaster2When being waited in production for heat Between generated rejection penalty coefficient；α1And α2It is all algorithm adjustable parameter；
    f1Represent that each conticaster deviates the summation of predetermined casting time amount, f2Represent respectively to pour all heats in secondary in production process The summation of middle stand-by period, is embodied as：
    <mrow> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>q</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>L</mi> </munderover> <mo>|</mo> <msub> <mi>T</mi> <mi>q</mi> </msub> <mo>-</mo> <msub> <mi>t</mi> <mi>q</mi> </msub> <mo>|</mo> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>q</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>L</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mo>|</mo> <msub> <mi>T</mi> <mi>q</mi> </msub> <mo>-</mo> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>M</mi> <mo>,</mo> <mi>q</mi> </mrow> <mi>S</mi> </msubsup> <mo>|</mo> <mo>,</mo> </mrow>
    <mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>&amp;Omega;</mi> <mi>j</mi> </msub> </munderover> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> <mi>&amp;mu;</mi> </msubsup> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>&amp;Omega;</mi> <mi>j</mi> </msub> </munderover> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> <mi>S</mi> </msubsup> <mo>-</mo> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow> <mi>E</mi> </msubsup> <mo>-</mo> <msubsup> <mi>t</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mi>d</mi> </msubsup> <mo>=</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>&amp;Omega;</mi> <mi>j</mi> </msub> </munderover> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> <mi>S</mi> </msubsup> <mo>-</mo> <mrow> <mo>(</mo> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow> <mi>S</mi> </msubsup> <mo>+</mo> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>t</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mi>d</mi> </msubsup> </mrow> </mtd> </mtr> </mtable> <mo>,</mo> </mrow>
    Wherein, L is the quantity of conticaster；TqFor q platform conticasters predetermined casting time；tqIt is opened for q platforms conticaster is actual It pours the time, i.e. the casting time that heat i is processed on process M equipment q is
    N is heat sum, and i is processing heat, and M is total for process, ΩjFor the set of heat i available devices on process j,It is to be heat i in process j-1 equipment kj-1With process j equipment kjBetween haulage time；Process j-1 is work The precedence activities of sequence j, equipment kj-1For equipment kjTight preceding equipment；
    KjFor all devices set on process j, It is heat i in process j equipment kjDuring wait before upper operation Between；
    It is heat i in process j equipment kjAt the beginning of upper processing；It is heat i in precedence activities j-1 equipment kj-1At the beginning of upper；It is heat i in precedence activities j-1 equipment kj-1On end time；For heat i In precedence activities j-1 equipment kj-1On process time；
    S3, establishes constraint condition set, and the constraint condition set includes one of following constraints or any combination：Respectively pour time interior stove Constraints, heat limited stand-by period constraints before process are poured by secondary company, and machine capability is extracted constraints, and heat exists Constraints once is at most processed on per procedure, same heat will can just carry out next work when previous process processes The constraints of sequence processing, equipment can use constraints earliest, and optional equipment constraints, time uncertainty constrains in production Condition；
    S4 is iterated computing to object function using priority policy genetic algorithm, asks for decision variable, specific to calculate Method includes the following steps：
    S41 carries out model initialization：Input model parameter information and initial time genetic algorithm iteration count t=0 are generated Population scale is the initial population P (t) of Q；
    S42 calculates feasible solution：Definite chromosome coding randomly generates the activity duration, according to adverse current according to Time Distribution The conflict resolving method retrodicted in calculating and equipment of journey obtains the production scheduling plan of no time conflict；
    S43 carries out population genetic optimization：It is excellent by combining equipment selection in actual environment according to the task processing weight of equipment The hereditary variation of first grade strategy, crossover operation carry out Evolution of Population, generate optimization and solve and export；
    S5, the Steelmaking-Continuous Casting Production Scheduling plan that controller is optimized according to the optimal solution of output, and to production run system Implement effectively control.
  2. 2. the steel-making continuous casting production scheduling method of priority policy genetic algorithm is utilized as described in claim 1, it is special Sign is：Constraints is concentrated in the step S3, and specific constraints is：
    1) respectively pour time company of interior heat and pour constraints：
    <mrow> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>M</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>M</mi> </msub> </mrow> <mi>S</mi> </msubsup> <mo>=</mo> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>M</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>M</mi> </msub> </mrow> <mi>E</mi> </msubsup> <mo>+</mo> <msubsup> <mi>t</mi> <mrow> <mi>M</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>M</mi> </msub> </mrow> <mi>&amp;delta;</mi> </msubsup> <mo>,</mo> </mrow>
    Wherein, i+1 is next heat that heat i is processed in same equipment；For end process M equipment kMBetween upper adjacent heat Because of the off time that less important work generates, side steel ladle to be poured is turned to needed for casting side by rotating including ladle turret Time；
    2) heat Shops With Limited Waiting Times constraints before process：
    <mrow> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> <mi>&amp;mu;</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>T</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> <mi>&amp;mu;</mi> </msubsup> <mo>,</mo> </mrow>
    Wherein,It is heat i in process j equipment kjThe maximum allowable stand-by period before upper operation；
    3) machine capability is extracted constraints, i.e. machine can only process a task simultaneously：
    <mrow> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> <mi>S</mi> </msubsup> <mo>&amp;GreaterEqual;</mo> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> <mi>E</mi> </msubsup> <mo>+</mo> <msubsup> <mi>t</mi> <mrow> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> <mi>&amp;delta;</mi> </msubsup> <mo>,</mo> </mrow>
    Wherein,For process j equipment kjBecause of the off time of less important work generation between upper adjacent heat；
    4) heat is at most processed constraints once on every procedure：
    Wherein,The equipment k of process j is assigned to for heat ijThe decision variable of processing；
    5) same heat will process the constraints that can just carry out subsequent processing processing when previous process：
    <mrow> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> <mi>S</mi> </msubsup> <mo>&amp;GreaterEqual;</mo> <msubsup> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow> <mi>E</mi> </msubsup> <mo>+</mo> <msubsup> <mi>t</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mi>d</mi> </msubsup> <mo>,</mo> </mrow>
    Wherein, j-1 be same heat processing route in process j precedence activities；It is heat i in process j- 1 equipment kj-1With process j equipment kjBetween haulage time；
    6) equipment can use constraints earliest, i.e., in each equipment the beginning activity duration of the first heat be later than the earliest of the equipment can Used time：
    <mrow> <msub> <mi>t</mi> <mrow> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> </msub> <mo>&amp;GreaterEqual;</mo> <msub> <mi>T</mi> <mrow> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> </msub> <mo>,</mo> </mrow>
    Wherein,For process j equipment kjThe beginning activity duration of upper first heat,For initial time process j equipment kj's Earliest available time, the initial time refer to the earliest available time of planning moment each equipment；
    7) optional equipment constraints, i.e. heat can only select the equipment of task processing weighted value non-zero on successor activities to be added Work：
    ω[(j-1,kj-1),(j,kj)] >=0,
    Wherein, ω [(j-1, kj-1),(j,kj)] it is heat from process j-1 equipment kj-1Go to successor activities j equipment kjOn task Process weight；
    8) time uncertainty constraints in producing, i.e. equipment process time and equipment room haulage time ripple within the specific limits It is dynamic；
    <mrow> <msubsup> <mi>t</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> <mi>d</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>t</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mi>d</mi> </msubsup> <mo>&amp;le;</mo> <msubsup> <mi>t</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <mi>d</mi> </msubsup> <mo>,</mo> </mrow>
    <mrow> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>t</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>,</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>,</mo> </mrow>
    Wherein,WithRespectively process j-1 equipment kj-1With process j equipment kjBetween Minimum and maximum haulage time,WithRespectively heat i is in process j equipment kjThe upper minimum process time and Maximum process time.
  3. 3. the steel-making continuous casting production scheduling method of priority policy genetic algorithm is utilized as described in claim 1, it is special Sign is：The segmented compound staining body coding method used in the step S42：
    C={ (T1,T2,...,TL),(a1b1f1g1,a2b2f2g2,...,aNbNfNgN)},
    Wherein, (T1,T2,...,TL) encoded for casting time, (a1b1f1g1,a2b2f2g2,...,aNbNfNgN) processed for each heat Path code, T1It is 1# conticaster casting times, TLFor the casting time of L# conticasters, aNbNfNgNFor the machining path of N# heats For：A# converter-b#LF-f#RH-g# conticasters, wherein, LF is ladle refining furnace；RH is vacuum circulation degassing refining furnace.
  4. 4. the steel-making continuous casting production scheduling method of priority policy genetic algorithm is utilized as described in claim 1, it is special Sign is：The backward algorithm of inverse process is in the step S42：
    Setup algorithm initial value is N-free diet method production process, i.e.,
    Activity duration used in calculating and haulage time are concretely comprised the following steps using the random value generated according to actual distribution rule：
    S421 makes j=M, and by conticaster casting time information in batch plan information and chromosome, calculates all tasks and exist The beginning and end activity duration on last procedure, i.e. continuous casting working procedure；
    S422 produces routing information according to each heat in chromosome, by the activity duration randomly generated and haulage time, retrodicts Go out beginning and end activity duration of each heat task on precedence activities j-1 on process j；
    Each station conflicts with the presence or absence of the activity duration on S423, inspection operation j-1, if so, application collision elimination algorithm solves；
    Whether S424, judgment step j-1 are the first procedure of production procedure, i.e. converter procedure, if (j-1) ＞ 1, makes j=j- 1, go back to step S422；Otherwise step S425 is performed；
    S425 can use constraint by station：It is constrained with the limited stand-by period：Mark the production Whether operation plan is reasonable.
  5. 5. the steel-making continuous casting production scheduling method of priority policy genetic algorithm is utilized as described in claim 1, it is special Sign is：Conflict resolving method in the step S42 in equipment is：
    Conflict resolving algorithm is conflict time size between the adjacent heat of statistics, takes adjustment haulage time, activity duration and increasing Add the stand-by period to eliminate, specific adjustable strategies are as follows：
    If the time that conflicts is more than adjustment haulage time, activity duration and the total maximum capacity for increasing the stand-by period, conflict Time is all included in the stand-by period, and marks the production scheduling plan unqualified,
    If the time that conflicts is less than adjustment haulage time and total maximum capacity of activity duration, the time that conflicts is by adjusting transport Time and activity duration eliminate,
    If conflicting the time between both the above situation, by adjusting haulage time, activity duration and increase the stand-by period To eliminate.
  6. 6. the steel-making continuous casting production scheduling method of priority policy genetic algorithm is utilized as described in claim 1, it is special Sign is：In the crossover operation, the arithmetic crossover method of the combination priority policy of casting time coding is：
    CT1'=r × CT1+(1-r)×CT2
    CT2'=r × CT2+(1-r)×CT1,
    Wherein, random numbers of the r between [0,1], CT1, CT2It is encoded for the casting time of parent chromosome, CT1', CT2' it is filial generation The casting time coding of chromosome.
  7. 7. the steel-making continuous casting production scheduling method of priority policy genetic algorithm is utilized as described in claim 1, it is special Sign is：In the crossover operation, the uniform crossover method of the combination priority policy of the coding of heat machining path is：
    Each corresponding gene of two parents is all swapped with certain crossover probability, i.e. the two of heat i parent chromosome Correspond to the gene place value of process j in substringWithCrossover probability P (j) values be：
    <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&amp;omega;</mi> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msubsup> <mi>k</mi> <mi>j</mi> <mn>2</mn> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <msubsup> <mi>k</mi> <mrow> <mi>j</mi> <mo>+</mo> <mn>1</mn> </mrow> <mn>1</mn> </msubsup> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>&amp;times;</mo> <mi>&amp;omega;</mi> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msubsup> <mi>k</mi> <mi>j</mi> <mn>1</mn> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <msubsup> <mi>k</mi> <mrow> <mi>j</mi> <mo>+</mo> <mn>1</mn> </mrow> <mn>2</mn> </msubsup> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>,</mo> </mrow>
    Wherein,For process j equipmentCorresponding to process j+1 equipmentProcessing weighted value,For process j equipmentCorresponding to process j+1 equipmentProcessing weighted value.
  8. 8. the steel-making continuous casting production scheduling method of priority policy genetic algorithm is utilized as described in claim 1, it is special Sign is：In the mutation operation, the coding mutation operation method of casting time is：
    <mrow> <msubsup> <mi>t</mi> <mi>q</mi> <mn>2</mn> </msubsup> <mo>=</mo> <msubsup> <mi>t</mi> <mi>q</mi> <mn>1</mn> </msubsup> <mo>+</mo> <mi>&amp;beta;</mi> <mo>,</mo> </mrow>
    Wherein, β is the disturbance quantity of conticaster casting time,For conticaster casting time before variation,It is opened for conticaster after variation Pour the time.
  9. 9. the steel-making continuous casting production scheduling method of priority policy genetic algorithm is utilized as described in claim 1, it is special Sign is：In the mutation operation, the coding mutation operation method of each heat machining path is：
    When performing mutation operation for the chromosome substring of heat i, correspond to the gene place value of process jMake a variation into the process Upper miscellaneous equipmentMutation probability p (j) be：
    <mrow> <mi>p</mi> <mrow> <mo>(</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>&amp;omega;</mi> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msubsup> <mi>k</mi> <mi>j</mi> <mn>2</mn> </msubsup> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>&amp;times;</mo> <mi>&amp;omega;</mi> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msubsup> <mi>k</mi> <mi>j</mi> <mn>2</mn> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>,</mo> </mrow>
    Wherein,For process j-1 equipment kj-1Corresponding to process j equipmentProcessing weighted value,For process j equipmentCorresponding to process j+1 equipment kj+1Processing weighted value.
  10. It is adjusted 10. the steel-making continuous casting using priority policy genetic algorithm as described in claim 1,2,7, one of 9 produces Degree method, it is characterised in that：The assignment method of task processing weight is：
    If because production distribution or equipment fault etc. cause process j equipment kjFor process j-1 equipment kj-1Unreachable equipment, then ω [(j-1,kj-1),(j,kj)]=0,
    If only has equipment k on process jjFor process j-1 equipment kj-1Reachable device, then
    ω[(j-1,kj-1),(j,kj)]=1,
    If there are multiple process j-1 equipment k on process jj-1Reachable deviceThen
    <mrow> <mi>&amp;omega;</mi> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msubsup> <mi>k</mi> <mi>j</mi> <mi>n</mi> </msubsup> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>=</mo> <mfrac> <msup> <mi>d</mi> <mo>&amp;prime;</mo> </msup> <msubsup> <mi>t</mi> <mrow> <mo>&amp;lsqb;</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <msub> <mi>k</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>,</mo> <msubsup> <mi>k</mi> <mi>j</mi> <mi>n</mi> </msubsup> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mi>d</mi> </msubsup> </mfrac> </mrow>
    Wherein：
    ω[(j-1,kj-1),(j,kj)] it is heat from process j-1 equipment kj-1Go to successor activities j equipment kjOn task processing Weighted value.
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TWI818176B (en) * 2019-06-11 2023-10-11 日商日立製作所股份有限公司 Planning aids and planning aids

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* Cited by examiner, † Cited by third party
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CN106055836B (en) * 2016-06-27 2019-01-29 重庆大学 Continuous casting unit pours the Multipurpose Optimal Method of heat selection, sequence and casting time decision
CN107871195A (en) * 2016-09-28 2018-04-03 北京北方华创微电子装备有限公司 The schedule sequences generation method and device of a kind of apparatus for production line
US10649993B2 (en) 2016-11-14 2020-05-12 International Business Machines Corporation Continuous caster scheduling with template driven search
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CN109711756A (en) * 2019-01-23 2019-05-03 重庆大学 Steel-making continuous casting production scheduling method and system based on target guiding cellular Automation Model
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US10867309B1 (en) * 2019-09-19 2020-12-15 Coupang Corp. Systems and methods for outbound forecasting based on a fulfillment center priority value
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CN111161112B (en) * 2019-12-27 2023-04-18 广东宜教通教育有限公司 Intelligent course scheduling method and system for administrative classes, computer equipment and storage medium
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CN114936778B (en) * 2022-05-26 2022-12-09 清华大学 Component job shop scheduling method and device
CN114707432B (en) * 2022-06-06 2022-10-14 浙江大学滨江研究院 Forging factory intelligent scheduling method based on genetic algorithm
CN117575216A (en) * 2023-11-15 2024-02-20 淄博京科电气有限公司 Intelligent factory management method and system based on Internet of things and industrial big data

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101630380A (en) * 2009-07-08 2010-01-20 西安电子科技大学 Job-shop scheduling method based on multi-population evolution mechanism
CN103927584A (en) * 2014-04-17 2014-07-16 湖北欣纬应急科技有限公司 Resource scheduling optimization method based on genetic algorithm

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020095393A1 (en) * 2000-06-06 2002-07-18 Mchaney Roger Computer program for and method of discrete event computer simulation incorporating biological paradigm for providing optimized decision support
US20060218108A1 (en) * 2005-03-24 2006-09-28 Sergey Panfilov System for soft computing simulation

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101630380A (en) * 2009-07-08 2010-01-20 西安电子科技大学 Job-shop scheduling method based on multi-population evolution mechanism
CN103927584A (en) * 2014-04-17 2014-07-16 湖北欣纬应急科技有限公司 Resource scheduling optimization method based on genetic algorithm

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI818176B (en) * 2019-06-11 2023-10-11 日商日立製作所股份有限公司 Planning aids and planning aids

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