CN109961189A - New college entrance examination timetabling algorithm based on genetic algorithm - Google Patents
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Abstract
The present invention relates to the new college entrance examination timetabling algorithms based on genetic algorithm, solves the timetabling arithmetic in new college entrance examination " 3+3 " mode, in conjunction with the teachers' instruction plan of Ministry of Education's defined, in view of the practical situations of middle school's row's class, it may finally obtain optimizing treated satisfactory random row's class result.The present invention is able to satisfy the hard constraint condition of timetabling arithmetic under new entrance examination policies, guarantees that same teacher is not simultaneously present two classrooms and without several teachers while the case where appear in a class.The present invention is able to satisfy the soft-constraint condition of timetabling arithmetic under new entrance examination policies, guarantees that most class, teaching programme simultaneously advance the every subject of per tour daily.Teacher's number needed for guaranteeing simultaneously and classroom number are minimum, by teaching resource reasonable distribution, reduce teacher and classroom redundancy.The present invention is able to satisfy the customized constraint condition of user of timetabling arithmetic under new entrance examination policies, and after inputting subject score weight corresponding with the period, row's class result of genetic algorithm is mobile to the direction of user's input condition.
Description
Technical field:
The invention belongs to field of neural networks, design, which is realized, carries out row's class with genetic algorithm under a kind of new entrance examination policies
Method.
Background technique:
With the continuous development of China's educational undertaking, computer intelligence Course Arrangement obtains in educational administration information management system
It is more and more widely used.The essence of row's class be exactly be all one group of instructional blocks of time appropriate of course arrangement and place, make to teach
Learning work can go on smoothly.Timetabling arithmetic is the main bugbear that each school faces on teaching resource management, optimization collocation.
Row's class difficulty under new entrance examination policies is bigger.
New entrance examination policies permit examinee chosen from " history land materialization is raw " this six subject three subjects as oneself
Test subject, and being no longer limited only by can only selection section or natural sciences.The freedom degree for improving student's selection in this way, can be most
The characteristics of big degree plays student.After student selects three subjects oneself being good to participate in college entrance examination, remaining three section only needs
By nationwide examination for graduation qualification, but the problem of bringing simultaneously is exactly the class that school needs to open up different Select-Course Modes for them, significantly
Increase row's class task in school Educational Affairs Office.
Timetabling arithmetic has proven to np complete problem.It is directed to original timetabling arithmetic, there are many scholars with exhaustion
The methods of method, simulated annealing, genetic algorithm, ant group algorithm are realized, can achieve preferable effect.But it is directed to new peak
The timetabling arithmetic under policy is examined, currently without more mature implementation method.In Hou Fayi scholar " under based on the new college entrance examination mode of 3+3
The class's of walking Education Administration Information System design and realize " in, though the concept of new entrance examination policies is expounded, and with UML modeling
Method realizes away a class Education Administration Information System, but the system is mainly by arranging to curricula-variable information, after automatic curriculum scheduling such as
Fruit effect is bad also to be needed to manually adjust, that is to say, that row's class module does not have automatic majorization function in the system.We can be with
Think that the system is more biased towards in SIM system information management, row's class module is simultaneously not perfect.Meanwhile Intelligent Course Scheduling purpose seeks to avoid people
Work participates in, if resource distribution reasonably optimal school timetable cannot be obtained, the result of row's class does not just have meaning.Except this with
Outside, then without scholar to the timetabling arithmetic under new entrance examination policies it studies.
Currently, genetic algorithm is widely used in optimization problem.Genetic algorithm is simulation Darwinian evolutionism
The computation model of the biological evolution process of natural selection and genetic mechanisms is a kind of by simulating natural evolution process searches most
The process of excellent solution.Genetic algorithm is may be a population of a potential disaggregation since the problem that represents, an and population
Then it is made of the individual of the certain amount by gene coding.After initial population generates, according to the survival of the fittest and the survival of the fittest
Principle develops by generation and produces the solution become better and better.In every generation, selected according to fitness size individual in Problem Areas a
Body, and it is combined intersection and variation by the genetic operator of natural selection, produce the population for representing new disaggregation.This
Process will lead to the same rear life of kind of images of a group of characters natural evolution and be more adaptive to environment than former generation for population, optimal in last reign of a dynasty population
Individual can be used as problem approximate optimal solution by decoding.It is directed to timetabling arithmetic, the present invention is found optimal by genetic algorithm
School timetable.
The constraint condition of timetabling arithmetic is divided into three classes: hard constraint price adjustment, soft-constraint condition, the customized constraint condition of user.
Wherein hard constraint condition includes: and cannot attend class in different classes the same position teacher same time, and same class's same time is only
A subject can be arranged, each class there must be form master etc..Soft-constraint price adjustment includes: that required teacher's sum is minimum, required
Classroom sum is minimum, certain subject even hall does not occur or not in more piece class in the sky etc..The customized constraint condition of user refers to row's class
The special limiting condition of system user addition.Present invention provide that the customized constraint condition of user are as follows: vice section chief and review one's lessons as far as possible not
The morning is appeared in, language number English is not present in afternoon as far as possible.When one group of school timetable can satisfy hard constraint condition, just with the valence of applying
Value.On this basis, school timetable more meets soft-constraint condition, then more tallies with the actual situation.Meeting hard constraint condition and soft-constraint
In the case where condition, school timetable more meets the customized constraint condition of user, then the user satisfaction of school timetable is higher, it is believed that the class
Table is more excellent.
When solving timetabling arithmetic under new entrance examination policies, we can encounter the following more stern challenge.If made
With method of exhaustion row's class, it is unable to get random effect, also can not just carry out optimization processing, we judge after school timetable superiority and inferiority such as at this time
Fruit is dissatisfied just to be needed artificially to modify, and is disagreed with the purpose of Intelligent Course Scheduling.
Under new entrance examination policies, if carrying out the part class of walking row's class, the class of walking is fixed the time, the class within the time
Between students be busy with changing classroom in corridor.For the university big compared to break long campus, the part class of walking system is carried out in middle school
Obviously reality is not met.Meanwhile high-quality teacher resource is also distributed to high-caliber student by us, this requires us to
It is raw to carry out delamination Teaching.Comprehensively consider, we must carry out row's class to teaching class, and the identical student of selected three section is assigned to one
Class, then teacher and classroom resources are allocated.
We first consider teacher resource, and quantity of teachers means that the expense of school, it is intended that teacher resource is sufficiently sharp
With not having many teacher's class hour numbers situation far from up to standard as far as possible.Because " 3+3 " teacher that different classes are related to is not
Together, selected three section is from the teacher of unselected three section for different, the teacher of selected three section and unselected three section of number that gives lessons of the same class
Required number difference of taking personal charge of the shift, so it is difficult to carry out random row's class.It can be with if not considering quantity of teachers in row's class hour at random
The row's of being readily available class result.If it is minimum to take into account teacher's number, when occur two classes require certain class-teaching of teacher and two classes other
When course has arranged only surplus section at the same time, since the same teacher cannot attend class to two classrooms simultaneously, program may
Endless loop is fallen into, row's class fails at this time.
When considering classroom resources, we can be found that student's quantity just determines class's number, the classroom in teaching building
Number is without optimization.But for course in reading, music lesson, the vice section chiefs such as sport, if the class period of different classes is not staggered by we,
Just need many Sveerz Deluxes, or even also to extend playground, it is clear that this be do not meet it is actual.So we will be taught based on vice section chief
The number of chambers at least carries out row's class.
Firstly, the input of genetic algorithm be potentially may solution (not necessarily meeting condition), with optimization process constantly into
Row, finally obtains satisfactory solution.It is directed to new college entrance examination timetabling arithmetic, satisfactory RANDOM SOLUTION generation is had any problem,
Generation RANDOM SOLUTION without restriction can bring extreme difficulties to the control condition of optimization process.If optimization process cannot be looked for successfully
To global optimum, it is exactly the redundancy of resource allocation for actual conditions, needs to pay more financial resources manpower and material resources.Pass through optimization
It is risky that process, which goes control condition, because if artificially checking whether final school timetable meets the requirements, workload is no less than
Manual curriculum scheduling.If school timetables implementation is waited to find that bug will cause unnecessary loss again.For optimization algorithm, the production of input
Life is difficult.
Secondly, the input restrictive condition of original timetabling arithmetic optimization algorithm is less, because class the same in each class, is awarded
There is the teacher of conflict that can give lessons to other classes between class hour.For new college entrance examination timetabling arithmetic, some teachers only need to certain two
Class's class also needs section's purpose if one of class's time conflict, this teacher are at half idle state at the same time
An other teacher, this is the unreasonable of resource allocation for school.
If its output is fixed when input determines using the method for exhaustion, even if output addition points-scoring system is given, when school timetable can not
Can not also it change when satisfactory.So the output for generating high quality is difficult for the method for exhaustion.
The original available good effect of the timetabling arithmetic method of exhaustion artificially may be used because the subject of all class's classes is identical
Carry out meet demand to design row's class template of complete set.But different Select-Course Modes class quantity is not true under new college entrance examination mode
Fixed, this will lead to that whole issue can not be solved with a template.
New college entrance examination timetabling arithmetic is not carried out going deep into thinking, be can not creatively by the method for exhaustion in conjunction with genetic algorithm,
Their difficult point can be respectively overcome by respectively taking advantage again.For method of exhaustion row's class, present invention uses 4 sets of templates, wherein template
It chooses and careful thinking, available satisfactory method of exhaustion row class output is all passed through in arrangement.It will determine to solve and be calculated as optimization
The input of method, each step update condition require to go deep into thinking, including variation, and supervision is compared, and is updated, final modules
Difficulty all overcome one by one.
It is directed to for new peak examination topic, wants to obtain high-caliber school timetable using the method for exhaustion or genetic algorithm row's class respectively
It is all remarkable.Only further investigation is likely to creatively combine them.In conjunction with there are also problems, needing constantly to solve later
Certainly, row's class could be carried out under new entrance examination policies and obtains high-caliber row's class result.
Summary of the invention
For the problem of Intelligent Course Scheduling under current new entrance examination policies, the present invention is by combining the method for exhaustion and genetic algorithm, first
The feasibility school timetable for meeting soft-constraint condition and hard constraint condition is obtained, genetic algorithm optimization processing is then carried out to it makes school timetable
Agree with the customized constraint condition of user, finally obtains teacher's number of high quality at least and the least random school timetable of classroom number, simultaneously
It can guarantee that teaching process promotes simultaneously and every subject per tour only has a class daily.
Algorithm main flow is divided into following 15 step:
Step 1: inputting different Select-Course Mode classes quantity.As the input of system, class's sum maximum value is 40.
40 classes can be described as music, the fine arts, computer, read classroom only one, playground can only accommodate two simultaneously
When a class of dismission activity, the more suitable value that takes.Weekly, using class's system is closed, (two classes are simultaneously same totally 20 period in the afternoon
One place is by same teachers' instruction), accommodate up to 40 classes.If vice section chief can largely appear in the morning, can at most accommodate
80 classes.It, can more 40 classes if each classroom of more one group of vice section chief.Can be understood as total class's number is suitably 40* vice section chief
Classroom group number (vice section chief arranges in the afternoon as far as possible)~80* vice section chief classroom group number (vice section chief can be arranged in whole day arbitrary period).
Step 2: selection language number English teacher and six three numbers of taking personal charge of the shift for choosing three section teachers are selected, it can two classes of band or three classes.
Step 3: using the concept of sparse matrix, when simplifying m- class-teacher three-dimensional matrice (in this three-dimensional matrice,
Numerical value is that there are such Zu Shijian- class-teacher's corresponding relationships for 1 representative), pass through the location information sum number for indicating nonzero element
It is believed that breath is to eliminate data redundancy.We will seek target and be transformed to two-dimensional matrix TS_TB_teacher from above-mentioned three-dimensional matrice
And TS_TB_class.Class's information is stored in two-dimensional matrix TS_TB_teacher, stores religion in two-dimensional matrix TS_TB_class
Teacher's information.The two two-dimensional matrixes can supervise each other, all contain all information in Zhe Shijian- class-teacher's three-dimensional matrice.
The present invention is that basic concept carries out row's class, therefore classroom information is equal to class's information with class teaching style.Except vice section chief
Outside, K class student attends class in the classroom K.Vice section chief has fixed classroom, such as music is attended class in Sveerz Deluxe.
Step 4: school timetable dimensionality reduction.By daily 8 class, 5 days weekly, i.e. 40 class weekly.By projection, by 40 periods
It is divided into 15 kinds of periods (a kind of every kind of subject period), row's class is carried out to the period of dimensionality reduction.Same teacher is not to of the same class
When giving lessons, can between the class hour identical subject of number switching time section.
As for 15 periods, being us distributes to 15 subjects for 40 periods, several in each subject corresponding templates
The real time section of a fixation.Period dimension after dimensionality reduction is identical as section's mesh number.We assume that L class weekly, shares m section
Mesh, wherein each subject class hour number is different, it is assumed that 1~subject of subject m has N1~Nm class respectively.Need to guarantee N1+ ...+
L can be tieed up period matrix dimensionality reduction at this time and tieed up at m by Nm=L.1~subject of subject m corresponding period is fixed respectively, to m
Tie up period row after class, mapping reduction L ties up school timetable.
Step 5: carrying out row's class using school timetable of the template to dimensionality reduction.Need one template of every ten classes, between template mutually
It influences and (needs especially processing, specific processing mode as follows at template interface).
The school timetable of dimensionality reduction indicates that 1~10 class of school timetable of dimensionality reduction is arranged with template 1 with TB_teacher and TB_class
Class, and so on, 31~40 classes carry out row's class with template 4.If should be noted that language number English teacher with three classes, 10 classes
With template 1, but 11 classes and 12 classes with template 2, they use same group of language number English teacher, to consider that the conflict at template interface is asked
Topic.
It is illustrated about template-setup: in different template-setups, changing the position of language number English and five vice section chiefs, do not change
" 3+3 " is related to subject and reviews one's lessons the position of class.If indicating that vice section chief, X indicate that Chinese language, Y indicate that mathematics, Z indicate English with W, often
A subject corresponds to five class in true school timetable, then four groups of templates are respectively set in order are as follows: XYZW, WYZX, YWZX,
ZXWY。
After the template of certain class determines, during to dimensionality reduction period school timetable row class, class hour the identical subject of number can
Sequence exchanges.If class's first, second, the third global template 1, the dimensionality reduction class school timetable of first is that the raw history land of language number English materialization reviews one's lessons sound
Body U.S. meter reads (15 periods corresponding course after dimensionality reduction when first class Select-Course Mode is materialization life), the dimensionality reduction class class of second
Table reviews one's lessons history body U.S. meter for number English metaplasia object land and reads sound, and so on.
Step 6: restoring true school timetable.The method of exhaustion is arranged the period of dimensionality reduction the teacher's school timetable for obtaining dimensionality reduction after class
The class school timetable TB_class of TB_teacher and dimensionality reduction pass through the corresponding teacher's school timetable TS_TB_ of mapping reduction actual time section
Teacher and true class's school timetable TS_TB_class.
Template is indicated with TS_TB.Restoring true school timetable is TS_TB_teacher and TS_TB_class, that is to say, that if
The dimensionality reduction school timetable period 1 corresponds to teacher S, and the dimensionality reduction school timetable period 1 corresponds to the true school timetable period 2,10,19,26,37, then very
This 5 periods all correspond to teacher S in real school timetable.
Step 7: setting evaluation module scores to the result of method of exhaustion row's class.Weight distribution as just user from
Define an embodiment of constraint condition.The difference of the customized constraint condition of user leads to the weight distribution difference that scores, influences to optimize
The high school timetable of user satisfaction finally can be obtained in direction.Points-scoring system can also have the distribution of other weights, and this system sets this and comments
Merotype is used only for the optimization performance of test macro, observes whether final school timetable can change to anticipated orientation.
We only set the scoring events of language number English from morning to night 8 class as 8 to 1 in this example, vice section chief and review one's lessons from
The early 8 class scoring events of evening that arrive are 1 to 8." subject involved by 3+3 " is not related to scoring, it is secondary because language number English is assigned to morning
Section and reviewing one's lessons is assigned at night, other subjects are assigned to one day intermediate period automatically at this time.To own in one group of school timetable
After the score summation of period, by score first divided by class's number, then divided by 159 (all score subjects optimal situations in one week
It is 159 points), multiplied by 100, the scoring of hundred-mark system can be obtained.
About the elaboration divided by 159: language (5 section) number (5 section) English (5 section) 15 class can not all be come the morning by us
First class, vice section chief (5 section) and reviews one's lessons (2 section) and all comes the 4th class in afternoon, because of first segment in morning in one week 40 class
Class only has 5 sections.So when the weight distribution set using us is scored, best result is not for for certain class's school timetable
22 class * 8 divide.Optimal situation should be that language number English comes first three class of every morning, therefore is scored at (8+7+6) * 5=105.It is secondary
Section and seven class altogether is reviewed one's lessons, 2 class come third class in afternoon, and 5 class come the 4th class in afternoon, therefore are scored at 8*5+7*
2=54.It needs so we normalize score intermediate steps divided by 159 (105+54).
Step 8: new explanation generation mechanism.The process that new explanation generates is certain two class of random crossover fixation number, exchanged
Cheng Zhong is exchanged if meeting hard constraint condition and soft-constraint condition, as being unsatisfactory for, is not exchanged.This requires us to randomly select
Two periods for exchange will be in the same class, i.e. teacher and class corresponding relationship is constant.Because what new explanation generated
Purpose is to meet the customized constraint condition of user, if do not met using destroying hard constraint condition and soft-constraint condition as cost
The purpose that we optimize.
It is also believed that respectively hard constraint condition, soft-constraint condition, user are customized about from high to low for priority
Beam condition.The present invention meets user as far as possible and makes by oneself on the basis of guaranteeing centainly to meet hard constraint condition and soft-constraint condition
Adopted constraint condition makes every effort to the school timetable for having application value to tally with the actual situation for obtaining high level.
In the present invention, hard constraint condition is collision problem, including two o'clock: two teachers do not enter the same class simultaneously,
And the same teacher is not simultaneously to two classroom lectures.Soft-constraint condition is divided into three: the every subject of per tour daily at most upper one
Class, total teacher's number is minimum, and total classroom number is minimum.The customized constraint condition of user is determined by user, is embodied, is commented with grading module
The numerical response row's class result divided agrees with degree with the customized constraint condition of user.
Exchange process will guarantee that every subject at most only has a class daily after changing, while teacher's number is minimum, and classroom number is minimum.
During updating school timetable, it is also contemplated that the conjunction class system of vice section chief, being related to vice section chief will operate simultaneously with another class for closing class, Yao Zengtian
Vice section chief special classroom supervises array.The effect of vice section chief special classroom check matrix is two teacher's same times for preventing same vice section chief
Section enters the corresponding special classroom of the vice section chief.
Step 9: initial population generates.30 individuals are identical in initial population, are row's class result of the method for exhaustion.At this time when
Former generation optimal solution and history optimal solution are method of exhaustion row's class result.
Step 10: population at individual is respectively mutated (small probability), the new population after generating variation.The process of variation is in population
Individual generates new explanation each by new explanation generation mechanism, forms new population.It is scored respectively the individual of population, with working as former generation
The highest individual that scores is updated when former generation optimal solution.Compare when former generation optimal solution and history optimal solution, if working as former generation optimal solution
Scoring be higher than history optimal solution scoring, then with work as former generation optimal solution more new historical optimal solution.
Step 11: eliminative mechanism.Individual choice is carried out to the population after variation, eliminates worst individual with eliminative mechanism.
Retain the individual of scoring preceding 20, eliminates after scoring 10 individual.In 10 superseded individuals, scoring the 21st, 23,25,27,29
Individual is substituted with history optimal solution, and the individual of scoring the 22nd, 24,26,28,30 is substituted with when former generation optimal solution.Then it is planted
Group's crossover operation.
Step 12: population is intersected.By 30 individuals of population by after marking and queuing, scoring the 1st intersects with scoring the 2nd, scores
3rd intersects with scoring the 4th, and so on, scoring the 29th intersects with scoring the 30th.The process of intersection is after randomly selecting for several times
One group of class's (closing certain two class that class attends class together) carries out school timetable exchange, such as exchanges latter two filial generation and is all satisfied hard constraint condition
It is then exchanged with soft-constraint condition, otherwise cancels exchange.It is scored respectively the individual of population, with when highest of former generation scoring
Body, which updates, works as former generation optimal solution.Compare when former generation optimal solution and history optimal solution, if gone through when the scoring of former generation optimal solution is higher than
The scoring of history optimal solution, then with when former generation optimal solution more new historical optimal solution.
Step 13: eliminative mechanism.Individual choice is carried out to the population after intersection, eliminates worst individual with eliminative mechanism.
Retain the individual of scoring preceding 20, eliminates after scoring 10 individual.In 10 superseded individuals, scoring the 21st, 23,25,27,29
Individual is substituted with history optimal solution, and the individual of scoring the 22nd, 24,26,28,30 is substituted with when former generation optimal solution.Then it is planted
Group's mutation operation.
Step 14: repeating step 10 and arrive step 13, until the convergence of genetic algorithm result is (after number for the optimal no change of history
Change), at this time using the result of genetic algorithm row's class as final output.
Step 15: generating and print school timetable, class-period school timetable and classroom-period school timetable are stored in .csv respectively
File.
1) present invention uses genetic algorithm, solves the timetabling arithmetic in new college entrance examination " 3+3 " mode, available optimization
Random row's class result that treated.
2) present invention is able to satisfy the hard constraint condition of timetabling arithmetic under new entrance examination policies, guarantees that same teacher does not occur simultaneously
Two classrooms and without several teachers simultaneously appear in a class the case where.
3) present invention is able to satisfy the soft-constraint condition of timetabling arithmetic under new entrance examination policies, guarantees that the every subject of per tour is most daily
One class, teaching programme simultaneously advance.Teacher's number needed for guaranteeing simultaneously and classroom number are minimum, and teaching resource reasonable distribution subtracts
Few teacher and classroom redundancy.
4) present invention is able to satisfy the customized constraint condition of user of timetabling arithmetic under new entrance examination policies, inputs subject and time
After the corresponding score weight of section, the condition that row's class result of genetic algorithm is inputted to user is mobile.
5) the present invention is based on the new entrance examination policies of the Ministry of Education, in conjunction with the teachers' instruction plan of Ministry of Education's defined, it is contemplated that
The practical situations of middle school's row's class carry out optimization processing to method of exhaustion row's class result with genetic algorithm, obtain making us full
The random school timetable of meaning.
Detailed description of the invention
Fig. 1 method flow diagram
Fig. 2 genetic algorithm Course Arrangement main interface
Fig. 3 genetic algorithm Course Arrangement input interface
Fig. 4 genetic algorithm row's class result is stored in .CSV file
Fig. 5 all_class.csv previewing file
Fig. 6 all_teacher.csv previewing file
Specific embodiment:
Method flow diagram is as shown in Figure 1, the interface of genetic algorithm row's class is as Figure 2-3 under new entrance examination policies, row's class knot
Fruit is as Figure 4-Figure 6.
It below will be to some technical details further description of the invention.
1) teacher workload is arranged
1. regulation language number English teacher needs to give lessons to 2~3 classes, per tour 5 class weekly.
2. regulation six selects the three three main subject teachers chosen with 2~3 classes, per tour 4 class weekly.
3. regulation six selects three unchecked three secondary subject teachers with 4 classes, per tour 2 class weekly.
4. provide music, sport, the fine arts, computer, read five vice section chief teachers with 10 classes, per tour 1 class weekly.
2) class arranges all class hours
There are weekly 5 Zakats in each class, daily 8 class, therefore per tour 40 class weekly.We distribute this 40 class by following rule
Then: each 5 class of language number English, six select three each 4 class of three sections chosen, and six select three unchecked each 2 class of three sections, and five vice section chiefs are each
1 class reviews one's lessons 2 class.
3) hard constraint condition controls
The feasibility of hard constraint conditional decision school timetable needs to guarantee without two teachers while entering a class and none religion
The case where teacher needs while teaching the Liang Ge class of different location.
Row's class can be carried out for class-period school timetable, storage content is teacher's information in matrix, while with teacher-
Period school timetable exercises supervision, can pre- anti-collision.It is important to note that the content stored in teacher-period school timetable
For class's information.
4) soft-constraint condition controls
Soft-constraint condition is very important in timetabling arithmetic.The satisfaction of soft-constraint condition or not directly determine the school timetable
Superiority and inferiority and the school timetable whether there is practical application value.
1. daily every most class of subject
Row's class template is set, provides that same class does not repeat in one day in template, i.e., it will 40 periods weekly
Several subjects are clearly divided into, subject-time corresponding position is fixed.It can guarantee that every teacher does not appear in a sky in this way
More piece class or many days do not have the case where class, at the same can guarantee teaching of the teacher with more classes and several same subject teachers into
Degree simultaneously advances.
2. teacher's number is minimum
Row's class hour is carried out on the basis of row's class template, the identical subject of class hour number can be interchanged weekly, and religion can be realized
Teacher's number is minimum.Unique deficiency may be the last one teacher of every symbolization of accounts it is possible that the situation that class hour number is discontented with.
3. classroom number is minimum
Homeroom sum is by input different mode class's quantity it has been determined that the only vice section chief classroom that can optimize.It examines
Multiple Sveerz Deluxes and computer classroom can not be opened up by considering some schools, thus vice section chief we be arranged close class's class mode.
Every vice section chief teacher to a class in the class is closed, be equivalent to two class hour workload, also comply with Ministry of Education's class hour number regulation.
4. template rotation
Because five vice section chief rotation only have 5 kinds of situations, when inputting class's quantity greater than 10, it would be desirable to template is added,
Namely set one group of new each section's period allocation rule.
It should be noted that when language number English teacher and six select the three three section teachers that choose band Liang Ge class when, template increases
It, only need to be with upper one group of template vice section chief period without intersection if no requirement (NR).
When language number English teacher and six select have the case where 3 classes of band in the three three section teachers chosen when, in the joining place of template,
It needs to guarantee without coverage condition.Such as 10 classes press 1 row's class of template, 11 classes press 2 row's class of template, but 10 classes and 11 classes at this time teacher have
Share situation, it is possible to keep the Lothrus apterus of template contral ineffective.
5) the customized constraint condition of user
The customized constraint condition of user is to measure the standard of user satisfaction.It is directed to the actual conditions of each school not
Together, subject-period weight can be set in we, scores school timetable.To focus on normalization and percentage in scoring process
System.Certain group school timetable scoring is higher, this group of school timetable more meets the customized constraint condition of user.
6) genetic algorithm row class Evolution of Population mechanism
1. new explanation generation mechanism
Old solution generates the process of new explanation, to guarantee that hard constraint condition and soft-constraint condition are set up simultaneously, just there is comparison in this way
The meaning of scoring.Meet the customized constraint condition of user with sacrificial hard constraint condition and soft-constraint condition, such way is
It is worthless.
This two class for requiring us to exchange needs in the same class, that is, certain class and several religions given lessons for it
Teacher's corresponding relationship is constant.Our special circumstances also in need of consideration are to review one's lessons and vice section chief.Because reviewing one's lessons no teacher, pertain only to mutually
It changes and is not related to supervising, when more beginning teacher supervises two-dimensional matrix without updating the school timetable for corresponding to teacher.In addition, vice section chief is using conjunction class
System, when being related to vice section chief exchange every time, all should two classes simultaneously Lothrus apterus and at the same time exchange.
To guarantee every section at most only upper class daily, during new explanation generates, if encounter exchange after some day go out
The existing identical section's purpose class of two sections, needs to stop to exchange, and is changed the selection of class position at random next time.
In addition, we will also add new supervision mechanism, in addition to needing to supervise class's two-dimensional matrix with teacher's two-dimensional matrix,
Also need to add one group of vice section chief special classroom check matrix.Because other subjects of different classes are attended class in respective class, do not relate to
And place conflict, when class's difference, classroom is different.Vice section chief then makes an exception, and by taking music lesson as an example, several Music Teachers share one
Sveerz Deluxe cannot use classroom in the same time.At this time in addition to the school timetable of teacher to be considered itself, it is also contemplated that identical vice section chief
Different teacher's school timetables need Lothrus apterus.
2. initial population generates
For this system using the result of method of exhaustion row's class as the input of genetic algorithm, 30 individuals of initial population are exhaustion
The output result TS_TB_class of method row's class, therewith it is adjoint be 30 identical TS_TB_teacher teacher's check matrixs and
30 corresponding identical special classroom's check matrixs.
Wherein TS_TB_class is the m- class-religion of concept clock synchronization stored by the method for exhaustion first with data in sparse matrix
Teacher's three-dimensional matrice carries out data redundancy elimination, then to time shaft dimensionality reduction, then carries out row's class using template to the school timetable of dimensionality reduction, most
Pass through the true school timetable of mapping reduction afterwards.
During Population Evolution, need to require the TS_TB_ in Population Regeneration every time after making a variation and intersecting for several times
When class school timetable, the corresponding TS_TB_teacher teacher's check matrix of real-time update and special classroom's check matrix
3. Variation mechanism
Population Variation is individual from operating, and brings new gene for population.The variation of population be in population individual respectively after
New explanation generation mechanism, certain two class of certain individual of random crossover fixation number will guarantee that individual is simultaneously after exchanging when exchanging
Meet hard constraint condition and soft-constraint condition.
It after 30 individuals are respectively mutated in population, generates 30 new individuals (30 new explanations), that is, generates the novel species after mutation
Group.It after saltation, updates and works as former generation optimum individual and history optimum individual, and carry out eliminative mechanism and carry out individual choice.
4. crossover mechanism
It is individual interoperability that population, which is intersected, and gene outstanding in population is combined.The row of scoring in population is arranged in this system
Two close individuals of name are intersected.For example, scoring highest individual is intersected with the second high individual of scoring, score minimum
Body is intersected with the second low individual of scoring.
During population is intersected, one group of class in odd-times parent (two classes for closing class's vice section chief) is exchanged every time
School timetable needs two filial generations met after changing to be all satisfied hard constraint condition and soft-constraint condition.Here control exchange times are surprise
For several times, lead to the filial generation generated after exchanging twice probability identical with parent to same group of class to reduce operation twice.
After new population after intersection generates, this system individually scores to individual each in population, and former generation is worked as in update
Optimum individual and history optimum individual, and carry out eliminative mechanism and carry out individual choice.
5. eliminative mechanism
In the continuous evolution process of population, population is whole mobile to high direction of scoring, and all individuals become in final population
In consistent.
The eliminative mechanism of this system are as follows: 10 individuals for scoring minimum in population after intersecting or making a variation are eliminated, wherein scoring
21st, 23,25,27,29 individual is replaced with history optimal solution, and the individual of scoring the 22nd, 24,26,28,30 is optimal with former generation is worked as
Solution replaces.Then, continue intersection or mutation operation after the population after eliminative mechanism selection.
Present invention provide that needing staggeredly to assign history optimal solution and when former generation optimal solution when eliminating worst individual, it is therefore an objective to
Guarantee to have in crossover process history optimal solution and when former generation optimal solution is intersected, while preventing the identical intersection behaviour of parent
Make.
6. more new historical is optimal
After being generated after variation or the new population intersected, individually scored individual each in population, with working as former generation
The highest individual that scores updates former generation optimal solution.
Compare when former generation optimal solution and history optimal solution, if scored when the scoring of former generation optimal solution is higher than history optimal solution,
Then with when former generation optimal solution more new historical optimal solution.
If history optimal solution meets algorithm termination condition, algorithm optimization process terminates.Conversely, constantly recycle, until
Scoring convergence.
7. algorithm termination condition
By long-term experiment, it has been found that due to the mutual restriction that influences each other between school timetable not of the same class, no matter input condition
How to change, the final scoring of new college entrance examination genetic algorithm row's class is all between 55~100.Continuous 2000 generation scoring is arranged in this system
And school timetable it is constant when, determine genetic algorithm row class terminate.
7) school timetable is generated and is printed
1. school timetable generates
We are using row's class result of the method for exhaustion as the input of genetic algorithm, by the output of genetic algorithm as final row
Class result.
2. school timetable prints
Row's class result is printed upon in .CSV file by we, it should be noted that former teacher's two-dimensional matrix and class's two-dimensional matrix with
The school timetable position corresponding relationship of printing.
When exporting class-period school timetable, we will show corresponding teacher and location information, pay special attention to " 3+3 "
Teacher should mark A and B respectively, to distinguish selected or not be selected.
When exporting teacher-period school timetable, we will show corresponding class and location information, pay special attention to vice section chief religion
Band class, teacher institute is the split run still class of conjunction.
Claims (1)
1. the new college entrance examination timetabling algorithm based on genetic algorithm, which is characterized in that be divided into following 15 step:
Step 1: inputting different Select-Course Mode classes quantity；As the input of system, class's sum maximum value is 40；
Step 2: selection language number English teacher and six selecting three numbers of taking personal charge of the shift for choosing three section teachers, two classes of band or three classes；
Step 3: utilizing the concept of sparse matrix, the three-dimensional matrice of m- class-teacher when simplifying, by indicating nonzero element
Location information and data information eliminate data redundancy；It will seek target and be transformed to two-dimensional matrix TS_TB_ from above-mentioned three-dimensional matrice
Teacher and TS_TB_class；Class's information, two-dimensional matrix TS_TB_class are stored in two-dimensional matrix TS_TB_teacher
Middle storage teacher's information；
It is that basic concept carries out row's class, therefore classroom information is equal to class's information, K class in addition to vice section chief with class teaching style
Student attends class in the classroom K；Vice section chief has fixed classroom；
Step 4: school timetable dimensionality reduction；By daily 8 class, 5 days weekly, i.e. 40 class weekly；By projection, 40 periods are divided
For 15 kinds of periods, a kind of every kind of subject period carries out row's class to the period of dimensionality reduction；Same teacher gives lessons to not of the same class
When, the switching time section between the class hour identical subject of number；
It is that 40 periods are distributed to 15 subjects as 15 periods, several fixations in each subject corresponding templates
Real time section；Period dimension after dimensionality reduction is identical as section's mesh number；
Step 5: carrying out row's class using school timetable of the template to dimensionality reduction；One template of every ten classes is needed, is influenced each other between template,
Need especially processing, specific processing mode as follows at template interface；
The school timetable of dimensionality reduction indicates that 1~10 class of school timetable of dimensionality reduction carries out row's class with template 1 with TB_teacher and TB_class, with
This analogizes, and 31~40 classes carry out row's class with template 4；
When language number English teacher is with three classes, if what these three classes used is not a template, since they share same group
Language number English teacher, will consider the collision problem at template interface；Template interface is then not present when language number English teacher is with two classes
The collision problem at place；
It is illustrated about template-setup: in different template-setups, changing the position of language number English and five vice section chiefs, do not change " 3+
3 " the positions for being related to subject and reviewing one's lessons class；If indicating that vice section chief, X indicate that Chinese language, Y indicate that mathematics, Z indicate English, Mei Geke with W
Mesh corresponds to five class in true school timetable, then four groups of templates are respectively set in order are as follows: XYZW, WYZX, YWZX, ZXWY；
After the template of certain class determines, during to dimensionality reduction period school timetable row class, class hour the identical subject of number can sequence
Exchange；
Step 6: restoring true school timetable；The method of exhaustion is arranged the period of dimensionality reduction the teacher's school timetable TB_ for obtaining dimensionality reduction after class
The class school timetable TB_class of teacher and dimensionality reduction pass through the corresponding teacher's school timetable TS_TB_ of mapping reduction actual time section
Teacher and true class's school timetable TS_TB_class；
Template is indicated with TS_TB；Restoring true school timetable is TS_TB_teacher and TS_TB_class, that is to say, that if dimensionality reduction
The school timetable period 1 corresponds to teacher S, multiple periods in the corresponding true school timetable of dimensionality reduction school timetable period 1, then multiple in true school timetable
Period all corresponds to teacher S；
Step 7: setting evaluation module scores to the result of method of exhaustion row's class；Weight distribution is customized as just user
One embodiment of constraint condition；The difference of the customized constraint condition of user leads to the weight distribution difference that scores, influences optimization side
To；
The scoring event of language number English from morning to night 8 class is set as 8 to 1, vice section chief and reviews one's lessons from morning to night 8 class scoring events and be
1 to 8；" subject involved by 3+3 " is not related to scoring, because language number English is assigned to morning, vice section chief and reviewing one's lessons is assigned at night,
Other subjects are assigned to one day intermediate period automatically at this time；It, will after the score summation of all periods in one group of school timetable
Score is first divided by class's number, then divided by 159, multiplied by 100, obtains the scoring of hundred-mark system；
Step 8: setting new explanation generation mechanism；
The process that new explanation generates is certain two class of random crossover fixation number, in exchange process, if meeting hard constraint condition
It then exchanges with soft-constraint condition, as being unsatisfactory for, does not exchange；Two periods for exchange that this requirement randomly selects will be
In the same class, i.e. teacher and class corresponding relationship is constant；Because the purpose that new explanation generates is to meet the customized constraint item of user
Part, if not meeting the purpose of optimization to destroy hard constraint condition and soft-constraint condition as cost；
It also holds that priority is respectively hard constraint condition, soft-constraint condition, the customized constraint condition of user from high to low；It is protecting
On the basis of card centainly meets hard constraint condition and soft-constraint condition, meet the customized constraint condition of user as far as possible；
Wherein, hard constraint condition is collision problem, including two o'clock: two teachers do not enter the same class and same simultaneously
Teacher is not simultaneously to two classroom lectures；Soft-constraint condition is divided into three: the every subject of a per tour at most upper class daily, total teacher
Number is minimum, and total classroom number is minimum；The customized constraint condition of user is determined by user, is embodied with grading module, and the numerical value of scoring is anti-
Class result and the customized constraint condition of user should be arranged agrees with degree；
Exchange process will guarantee that every subject at most only has a class daily after changing, while teacher's number is minimum, and classroom number is minimum；It updates
During school timetable, it is also contemplated that the conjunction class system of vice section chief, being related to vice section chief will operate simultaneously with another class for closing class, Yao Zengtian vice section chief
Array is supervised in classroom；Check matrix effect in vice section chief classroom is prevent two teacher's same periods of same vice section chief into the vice section chief
Corresponding classroom；
Step 9: initial population generates；30 individuals are identical in initial population, are row's class result of the method for exhaustion；Work as former generation at this time
Optimal solution and history optimal solution are method of exhaustion row's class result；
Step 10: population at individual is respectively mutated, the new population after generating variation；The process of variation be in population individual each by
New explanation generation mechanism generates new explanation, forms new population；It is scored respectively the individual of population, with when highest of former generation scoring
Body, which updates, works as former generation optimal solution；Compare when former generation optimal solution and history optimal solution, if gone through when the scoring of former generation optimal solution is higher than
The scoring of history optimal solution, then with when former generation optimal solution more new historical optimal solution；
Step 11: setting eliminative mechanism: individual choice being carried out to the population after variation, eliminates worst individual with eliminative mechanism；
Retain the individual of scoring preceding 20, eliminates after scoring 10 individual；In 10 superseded individuals, scoring the 21st, 23,25,27,29
Individual is substituted with history optimal solution, and the individual of scoring the 22nd, 24,26,28,30 is substituted with when former generation optimal solution；Then it is planted
Group's crossover operation；
Step 12: population is intersected: by 30 individuals of population by after marking and queuing, scoring the 1st intersects with scoring the 2nd, score the 3rd and
Score the 4th intersection, and so on, scoring the 29th intersects with scoring the 30th；The process of intersection is after randomly selecting one group for several times
Class is to close certain two class that class attends class together to carry out school timetable exchanges, such as exchanges latter two filial generation and is all satisfied hard constraint condition and soft
Constraint condition then exchanges, otherwise cancels exchange；It is scored respectively the individual of population, scores highest individual more with when former generation
Newly work as former generation optimal solution；Compare when former generation optimal solution and history optimal solution, if the scoring when former generation optimal solution is higher than history most
The scoring of excellent solution, then with when former generation optimal solution more new historical optimal solution；
Step 13: eliminative mechanism；Individual choice is carried out to the population after intersection, eliminates worst individual with eliminative mechanism；Retain
The individual of scoring preceding 20 eliminates after scoring 10 individual；In 10 superseded individuals, the individual of scoring the 21st, 23,25,27,29
It is substituted with history optimal solution, the individual of scoring the 22nd, 24,26,28,30 is substituted with when former generation optimal solution；Then population change is carried out
ETTHER-OR operation；
Step 14: repeating step 10 and arrive step 13, until genetic algorithm result restrains, at this time make the result of genetic algorithm row's class
For final output；
Step 15: generating and print school timetable, class-period school timetable and classroom-period school timetable are stored in .csv file respectively.
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