CN1422964A - Optimized arrangement method of annealing production for bell-type furnace - Google Patents
Optimized arrangement method of annealing production for bell-type furnace Download PDFInfo
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- CN1422964A CN1422964A CN 02144923 CN02144923A CN1422964A CN 1422964 A CN1422964 A CN 1422964A CN 02144923 CN02144923 CN 02144923 CN 02144923 A CN02144923 A CN 02144923A CN 1422964 A CN1422964 A CN 1422964A
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Abstract
There is a cover annealing production and optimized excluding method in a computer. The method belongs to information technology field, refers to the application of genetic arithmetic and computer simulation in mantle quenched production. The character lies in that the method is made up of genetic arithmetic model and computer simulation quenched production model. The simulation model is used in simulating the whole quenche production process, the genetic arithmetic model optimizes the work scheme, the best scheme can be achieved through the interaction of the two models.
Description
Technical field
The invention belongs to areas of information technology, relate to automatic technology, specially refer to a kind of cover annealing and produce optimization row product method.
Background technology
At present, the annealing room of domestic most of steel mill all adopts bell furnace stove group annealing way, annealing mainly is divided into two stages of heating and cooling, cycle is longer, the annealing cycle of common nitrogen hydrogen bell furnace is general above 70 hours, even adopt comparatively advanced at present perhydro bell-type annealing technology, the annealing cycle is the major power consumer of smelter also more than 35 hours.The heating mantles of annealing room and cooling cowl number design by certain proportion relation usually, and both equal the table sum at the quantity sum.By present bell furnace production technique, heating zone total time is installed the non-cutting time of coming out of the stove additional less than cooling section total time in the annealing cycle, certainly exists bell furnace system resource conflict phenomenon like this.In order to make rational use of resources, to enhance productivity, must produce the row of being optimized to annealing and produce.
Existing row's product method is to start with from job scheduling, improves the effective operation rate of heating mantles, cooling cowl, driving, reduces and avoid the advance selling of heating mantles and cooling cowl, and this is the effective way of present raising annealing room throughput of generally acknowledging.
As S.Moon and A.N.Hrymak. article (Scheduling of the batchannealing process-deterministic case. Computers and ChemicalEngineering at them, 23 (1999), 1193-1208.) in a kind of method of the mathematical modeling that decomposes based on time slot has been proposed, they have considered the batch processing situation, all tables all were in idle condition when promptly hypothesis began to dispatch, on this basis, the method that adopts time slot to decompose has been set up MILP (Mixed Integer Linear Programming) (MILP) model of annealing room, and uses branch and bound method and find the solution.This method has the problem of following two aspects in actual applications: the one, and most of annealing room all is uninterrupted production, the equal idle original state hypothesis of all tables does not meet reality; The 2nd, the MILP model that forms is larger, also not effective especially solution.People such as Dong Jie side they article (mathematical model and the algorithm research of coil of strip thermal treatment scheduling. iron and steel (2001), Vol.36, No.12,73-76.) in introduced a kind of method of simplifying problem, they are a hybrid flow workshop sequencing problem with problem reduction, have designed corresponding greedy sort algorithm and have found the solution.But the annealing process that 12 operational phases will be arranged in the problem modeling process is reduced to two stages to be handled, therefore existing problems still in actual applications.
Usually, domestic most of annealing room adopts manual method to carry out job scheduling according to experience and obtains a feasible row and produce the result, this method not only wastes time and energy but also is difficult to the reasonableness that the row of assurance produces, the conflict of narrow resources such as heating mantles, cooling cowl, driving often appears in the production, the advance selling rate of heating mantles and cooling cowl is bigger, causes the throughput of annealing room to be lower than the throughput of adjacent operation.And when the condition of production and estimate generation were inconsistent, row produced the result and must change or reset.So not only the row of giving produces the workman and has brought very big trouble, and has reduced the efficient of whole production line.
Summary of the invention
The purpose of this invention is to provide a kind of Automatic Optimal row product method that is executed in a computer, for cover annealing production provides optimum job scheduling scheme, what solve that artificial at present row produces wastes time and energy and inefficiency and the problem that is difficult to guarantee result optimal.
Realize technical scheme of the present invention:
The present invention comprises two modules, genetic algorithm module and computer simulation model module.Optimum job scheduling scheme is exactly to be produced by the interaction of these two modules.
Computer simulation model
Anneal on the basis of the production process of producing and various constraint conditions fully analyzing bell furnace stove group, set up the realistic model that cover annealing is produced, this model can be under the prerequisite of known initial production state and job scheduling scheme, the emulation bell furnace stove group production process of annealing in computer, emulation finish the back just can obtain the productive capacity be concerned about.
The present invention is abstracted into each incident to each annealing steps, essential wait is abstracted into waiting event when needing resource to be not being met, the present invention is divided into 4 classes to waiting event according to on-site actual situations is according to priority different, deposit this 4 class waiting event with 4 binary sort trees, each binary sort tree is exactly a waiting event formation, so one has 4 waiting event formations, the waiting event of equal priority is pressed the time of origin ordering.Each resource is abstracted into various objects, and the utilization Object-oriented Technique adopts the event-driven method to set up computer simulation model.
Genetic algorithm
Each scheduling scheme adopts the natural number coding mode as the individuality in the genetic algorithm.The fitness function of computer simulation model as genetic algorithm, the productive capacity that Computer Simulation obtains later on is as the functional value of fitness function, and population size and evolutionary generation can be adjusted according to circumstances.
The reciprocal process of genetic algorithm and computer simulation model is as follows: originally produce a population (group of individuals) at random by genetic algorithm, each individuality in this population is sent into computer simulation model as input, after emulation, obtain needed productive capacity and send into genetic algorithm module as the fitness function value, utilize the overall parallel search ability of genetic algorithm to obtain more excellent population, again this more excellent population is sent into computer simulation model, so circulation is repeatedly till convergence.Then, from the population of the optimum that obtains at last, select the scheduling scheme of the individuality of an optimum as optimum.
Effect of the present invention and benefit are: significantly reduced working strength of workers, saved row in a large number and produced required time, increased the accuracy that row produces the result, having improved that annealing is produced and whole production line production efficiency.
Description of drawings
Fig. 1 is based on the annealing furnace job scheduling schematic diagram of simulation optimization.
Among the figure: (1) scheduling scheme, (2) genetic algorithm module, (3) objective function, (4) Computer Simulation module.
Fig. 2 is the programflow chart of Computer Simulation annealing production module.
Among the figure: (5) simulation initialisation, (6) judge the waiting event that whether satisfies condition, (7) carry out the waiting event that satisfies condition, (8) select a non-waiting event that takes place the earliest, (9) the emulation clock is advanced to the time of origin of this incident, (10) carry out this non-waiting event, (11) judge whether emulation can finish, and (12) finish emulation.
Embodiment
Below in conjunction with accompanying drawing and example in detail specific embodiments of the present invention.
If existing 46 tables, 23 heating mantleses, 23 cooling cowls, 2 drivings, the production program of 10 row's for the treatment of products, each different production order all is a kind of scheduling scheme, adopts different scheduling scheme productions will obtain different productive capacitys, and the purpose that row produces is exactly to select the scheduling scheme of an optimum.
Step 1: start genetic algorithm module
Module (2) is the production program of 10 row's for the treatment of products from 1 to 10 numbering.Produce initial population, each of initial population be individual represent a scheduling scheme, the employing natural number coding, and for example (1,2,3,4,5,6,7,8,9,10) are bodies one by one, represent a kind of scheduling scheme; And (10,9,8,7,6,5,4,3,2,1) is another individuality, represents another kind of scheduling scheme, and its production order is with last a kind of opposite.Initial population is preserved as current population.
Step 2: the maximum algebraically that judges whether to reach regulation
If reach the maximum algebraically of regulation, from all populations of emulation, select individuality with Optimal Production index, the production planning and sequencing of this individuality representative is as the scheduling scheme of optimum, and termination routine moves.Otherwise enter next step.
Step 3: each individuality in the current population is called Computer Simulation module (4) as input.(1) is the individuality of sending into emulation module.Simulation process that each is individual such as step 4 are to step 11.
Step 4: simulation initialisation (5)
Each table, heating mantles, cooling cowl and driving are defined as object, read in the current production status of 46 tables then, these production statuses are converted to corresponding incident or waiting event, and incident is put into event queue in chronological order, and event queue has only 1.Waiting event is put into the waiting event formation, the present invention is divided into 4 classes to waiting event according to practical situation are according to priority different, depositing 4 class waiting events with 4 binary sort trees, each binary sort tree is exactly a waiting event formation, so one has 4 waiting event formations, the waiting event of equal priority is pressed the time of origin ordering.Then, the emulation clock being set is 0.
Step 5: judge the waiting event (6) that whether satisfies condition
Check that according to priority the condition which waiting event is waited for can be met.If satisfy, this wait incident is proposed from waiting list, enter next step; If do not satisfy then skip next step.
Step 6: carry out the waiting event (7) that satisfies condition
Its operation comprises the next incident of generation, takies related resource, destroys self (promptly discharging the memory source that this wait event object takies).After all waiting events that satisfy condition are all carried out, enter next step.
Step 7: select a non-waiting event (8) that takes place the earliest
From event queue, select time of origin incident the earliest, if not only one, choose in the lump.
Step 8: the time of origin (9) that the emulation clock is advanced to this incident
Step 9: carry out this non-waiting event (10)
Operation comprises release and takies related resource, produces next incident or waiting event, destroys self (promptly discharging the memory source that this non-waiting event object takies).
Step 10: judge whether emulation can finish (11)
Judge whether termination condition satisfies, i.e. whether decision event formation is empty,,, then get back to step 5 and continue to carry out if be not empty if sky then enters next step.
Step 11: finish emulation (12)
Step 12: select computing
In the middle of the implementation of emulation, continuous logging program state comprises the waiting time length and the working hour length of heating mantles and cooling cowl, and total time produced in record after EP (end of program).Notice that this total time is not the total time of program run, but the total time that the production process of institute's emulation needs.
After body emulation finishes one by one, utilize recorded data to form the productive capacity that the user was concerned about and send into genetic algorithm module as this ideal adaptation degree functional value (3), genetic algorithm module can adopt the method for roulette that these individualities are selected according to all individual fitness function values in the current population.
Step 13: carry out crossing operation
Each individual part mapping intersection (PMX) method that adopts after selecting is intersected, and crossing-over rate is not less than 0.9.
Step 14: computing makes a variation
What adopt is to exchange variation, two positions of selection at random, and with these two location swaps, aberration rate is less than 0.1.
Step 15: return step 2
All individualities of process selection, intersection and variation as current population, are returned step 2.
Claims (1)
1. one kind is used for the optimization row product method that cover annealing is produced, form by genetic algorithm module and Computer Simulation annealing production module two portions, with computer simulated annealing production process, represent the operation scheduling scheme with natural number coding, interaction with genetic algorithm and realistic model finds optimum scheduling scheme, it is characterized in that:
A) with the fitness function of computer simulated annealing production models, as the fitness function value, represent the individuality of operation scheduling scheme as the legacy algorithm with natural number coding with the production data that obtains after the simulated annealing production as genetic algorithm;
B) in Computer Simulation annealing production models, waiting event according to priority is divided into 4 classes, every class deposits different waiting event formations in, and the waiting event in the identical waiting event formation is pressed the time of origin ordering.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101117659B (en) * | 2006-08-04 | 2010-04-21 | 重庆钢铁集团电子有限责任公司 | Full-hydrogen hood-type annealing furnace control system |
| CN101344780B (en) * | 2008-06-30 | 2010-09-01 | 东北大学 | Optimized furnace combination method and system for cold rolling hood type annealing furnace unit |
| CN102141935A (en) * | 2011-03-22 | 2011-08-03 | 曙光信息产业(北京)有限公司 | Job scheduling method based on dual target optimization genetic algorithm |
| CN104570997A (en) * | 2014-10-22 | 2015-04-29 | 华中科技大学 | Method for discharge and processing production scheduling integration optimization of metal structure components |
| CN105792270A (en) * | 2014-12-24 | 2016-07-20 | 国家电网公司 | Discrete event simulation method applied to WiMAX (World Interoperability for MicrowaveAccess) system and platform thereof |
| CN110738413A (en) * | 2019-10-15 | 2020-01-31 | 中国航空制造技术研究院 | Multi-constraint scheduling calculation method and device for automatic aviation part machining production line |
| CN112926828A (en) * | 2021-01-22 | 2021-06-08 | 东北大学 | Steelmaking production data analysis and optimization scheduling method for medium plate production line |
-
2002
- 2002-12-13 CN CN 02144923 patent/CN1422964A/en active Pending
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101117659B (en) * | 2006-08-04 | 2010-04-21 | 重庆钢铁集团电子有限责任公司 | Full-hydrogen hood-type annealing furnace control system |
| CN101344780B (en) * | 2008-06-30 | 2010-09-01 | 东北大学 | Optimized furnace combination method and system for cold rolling hood type annealing furnace unit |
| CN102141935A (en) * | 2011-03-22 | 2011-08-03 | 曙光信息产业(北京)有限公司 | Job scheduling method based on dual target optimization genetic algorithm |
| CN104570997A (en) * | 2014-10-22 | 2015-04-29 | 华中科技大学 | Method for discharge and processing production scheduling integration optimization of metal structure components |
| CN105792270A (en) * | 2014-12-24 | 2016-07-20 | 国家电网公司 | Discrete event simulation method applied to WiMAX (World Interoperability for MicrowaveAccess) system and platform thereof |
| CN110738413A (en) * | 2019-10-15 | 2020-01-31 | 中国航空制造技术研究院 | Multi-constraint scheduling calculation method and device for automatic aviation part machining production line |
| CN112926828A (en) * | 2021-01-22 | 2021-06-08 | 东北大学 | Steelmaking production data analysis and optimization scheduling method for medium plate production line |
| CN112926828B (en) * | 2021-01-22 | 2023-09-01 | 东北大学 | Steelmaking production data analysis and optimization scheduling method for medium plate production line |
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