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INTRODUCTION 1.1 General Introduction
HEURISTIC SEARCH
In computer science, artificial intelligence, and mathematical optimization, a heuristic is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. The objective of a heuristic is to produce quickly enough a solution that is good enough for solving the problem at hand. This solution may not be the best of all the actual solutions to this problem, or it may simply approximate the exact solution. ut it is still valuable because finding it does not require a prohibitively long time. Heuristics may produce results by themselves, or they may be used in conjunction with optimization algorithms to improve their efficiency (e.g., they may be used to generate good seed values). All of the search methods in the preceding section are uninformed in that they did not take into account the goal. hey do not use any information about where they are trying to get to unless they happen to stumble on a goal. !ne form of heuristic information about which nodes seem the most promising is a heuristic function h(n), which takes a node n and returns a non"negative real number that is an estimate of the path cost from node n to a goal node. he function h(n) is an
underestimate if h(n) is less than or e#ual to the actual cost of a lowest"cost path from node n to a goal.
Results about NP-hardness in theoretical computer science make heuristics the only viable option for a variety of complex optimization problems that need to be routinely solved in realworld applications. he trade"off criteria for deciding whether to use a heuristic for solving a given problem include the following$ •
!ptimality$ %hen several solutions e&ist for a given problem, does the heuristic guarantee that the best solution will be found' o we actually need the best one'
!
•
ompleteness$ %hen several solutions e&ist for a given problem, can the heuristic find them all' o we actually need all solutions' *any heuristics are only meant to find one solution.
•
Accuracy and precision$ an the heuristic provide a confidence interval for the purported solution' +s the error bar on the solution unreasonably large'
•
&ecution time$ +s this the best known heuristic for solving this type of problem' -ome heuristics converge faster than others. -ome heuristics are only marginally #uicker than classic methods.
HER!"#!$ "E%R$H %&'(R!#H) *" $&%""!$%& )E#H(+ /esults about 01"hardness in theoretical computer science make heuristics the only viable option for a variety of comple& optimization problems that need to be routinely solved in real"world applications.2 Hence we can see that classical method just cant help to solve 01 hard problems and since time" table scheduling is an 01 hard problem ,we have to opt for heuristic search algorithm
A3A0A4 !3/ 5A--+A5 -A/H Heuristic evaluations are easy to use, fast and as cheap as you want it . 1lus, since each observed usability problem is e&plained with reference to an established usability principle, it is fairly easy to generate fi&es. +t is a good method for finding both major and minor problems in a user interface. Heuristic evaluations can be employed early in the design life cycle to find usability problems. his makes them considerably easier and cheaper to fi& then than if the problems were to be discovered in later phases of the design or not at all. herefore, heuristic evaluations stand a better chance of actually being used in practical design situations. +t re#uires a certain level of knowledge and e&perience to apply the heuristics effectively hese usability e&perts are sometimes hard and e&pensive to come by, especially if they need to have domain e&pertise. +f the evaluators are not part of the development t team, they may not be aware of technical limitations on the design or why certain design decisions were made ifferences between development team and evaluators may arise, which can impede communication and correction of the problems identified during the evaluation Heuristic evaluations are loosely structured and therefore run the risk of finding one"time, low"priority problems. hese problems may not be
"
important to correct. 6inally, heuristic evaluations does not allow a way to assess the #uality of redesigns . 7acktracking is one of methods for the problem which has a combination of solutions of a large but still limited number. +ts important feature is solution space generated in the search while implementing. +ts shortcoming is the bigger time comple&ity. herefore we should use it with caution8 +t is best used in combination with other algorithms.
#xecution time$execution time of heuristic search is very less then blind or classical searches +!,,EREN# #PE" (, HER!"#!$ "E%R$HE"
/ 'ENE#!$ %&'(R!#H) In the computer science field of artificial intelligence, a genetic algorithm %&'( is a search heuristic that mimics the process of natural selection. This heuristic %also sometimes called a meta heuristic( is routinely used to generate useful solutions to optimization and search problems.)!* &enetic algorithms belong to the larger class of evolutionary algorithms %#'(, which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. Initialization of genetic algorithm
population. he population size depends on the nature of the problem, but typically contains several hundreds or thousands of possible solutions. raditionally, the population is generated randomly, allowing the entire range +nitially many individual solutions are (usually) randomly generated to form an initial of possible solutions (the search space). !ccasionally, the solutions may be 9seeded9 in areas where optimal solutions are likely to be found.
+election uring each successive generation, a proportion of the e&isting population is selected to breed a new generation. +ndividual solutions are selected through a fitness"based process, where fitter solutions (as measured by a fitness function) are typically more likely to be selected. ertain selection methods rate the fitness of each solution and preferentially select the best solutions. !ther methods rate only a random sample of the population, as the former process may be very time" consuming.
#he fitness function is defined over the genetic representation and measures the #uality of the represented solution. he fitness function is always problem dependent. 6or instance, in the knapsack problem one wants to ma&imize the total value of objects that can be put in a knapsack of some fi&ed capacity. A representation of a solution might be an array of bits, where each bit represents a different object, and the value of the bit (: or ;) represents whether or not the object is in the knapsack. 0ot every such representation is valid, as the size of objects may e&ceed the capacity of the knapsack. he fitness of the solution is the sum of values of all objects in the knapsack if the representation is valid, or : otherwise. +n some problems, it is hard or even impossible to define the fitness e&pression8 in these cases, a simulation may be used to determine the fitness function value of a phenotype (e.g. computational fluid dynamics is used to determine the air resistance of a vehicle whose shape is encoded as the phenotype), or even interactive genetic algorithms are used.
Genetic operators he ne&t step is to generate a second generation population of solutions from those selected through genetic operators$ crossover (also called recombination), and) that support the importance of mutation"based search.
-
Although crossover and mutation are known as the main genetic operators, it is possible to use other operators such as regrouping, colonization"e&tinction, or migration in genetic algorithms. ?@ +t is worth tuning parameters such as the mutation probability, crossover probability and population size to find reasonable settings for the problem class being worked on. A very small mutation rate may lead to genetic drift (which is non"ergodic in nature). A recombination rate that is too high may lead to premature convergence of the genetic algorithm. A mutation rate that is too high may lead to loss of good solutions unless there is elitist selection. here are theoretical but not yet practical upper and lower bounds for these parameters that can help guide selection through e&periments (see utorials). Mutation is a genetic operator used to maintain genetic diversity from one generation of a
population of genetic algorithm chromosomes to the ne&t. +t is analogous to biological mutation. *utation alters one or more gene values in a chromosome from its initial state. +n mutation, the solution may change entirely from the previous solution. Hence 4A can come to better solution by using mutation. *utation occurs during evolution according to a user"definable mutation probability. his probability should be set low. +f it is set too high, the search will turn into a primitive random search.
he classic e&le of a mutation operator involves a probability that an arbitrary bit in a genetic se#uence will be changed from its srcinal state. A common method of implementing the mutation operator involves generating a random variable for each bit in a se#uence. his random variable tells whether or not a particular bit will be modified. his mutation procedure, based on the biological point mutation, is called single point mutation. !ther types are inversion and floating point mutation. %hen the gene encoding is restrictive as in permutation problems, mutations are swaps, inversions and scrambles. he purpose of mutation in 4As is preserving and introducing diversity. *utation should allow the algorithm to avoid local minima by preventing the population of chromosomes from becoming too similar to each other, thus slowing or even stopping evolution. his reasoning also e&plains the fact that most 4A systems avoid only taking the fittest of the population in generating the ne&t but rather a random (or semi"random) selection with a weighting toward those that are fitter. ?; 6or different genome types, different mutation types are suitable$ •
7it string mutation he mutation of bit strings ensue through bit flips at random positions.
&le$ ;:;::;: ;:;:;;:
/!--!3/ +n genetic algorithms, crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the ne&t. +t is analogous to reproduction and biological crossover, upon which genetic algorithms are based. ross over is a process of taking more than one parent solutions and producing a child solution from them. here are methods for selection of the chromosomes. hose are also given below.
Termination his generational process is repeated until a termination condition has been reached. ommon terminating conditions are$ •
A solution is found that satisfies minimum criteria
•
6i&ed number of generations reached
•
Allocated budget (computation time
•
he highest ranking solutionBs fitness is reaching or has reached a plateau such that successive iterations no longer produce better results
•
*anual inspection
•
ombinations of the above
0/ #%1 "E%R$H /
abu search, created by 6red %. 4lover in ;CD> and formalized in ;CDC, is a metaheuristic search method employing local search methods used for mathematical optimization. 5ocal (neighborhood) searches take a potential solution to a problem and check its immediate neighbors (that is, solutions that are similar e&cept for one or two minor details) in the hope of finding an improved solution. 5ocal search methods have a tendency to become stuck in suboptimal regions or on plateaus where many solutions are e#ually fit. abu search enhances the performance of these techni#ues by using memory structures that describe the visited solutions or user"provided sets of rules. ?= +f a potential solution has been previously visited within a certain short"term period or if it has violated a rule, it is marked as 9tabu9 (forbidden) so that the algorithm does not consider that possibility repeatedly. abu search (-) is a metaheuristic algorithm that can be used for solving combinatorial optimization problems (problems where an optimal ordering and selection of options is desired). he memory structures used in tabu search can roughly be divided into three categories$ ?@ E -hort"term$ he list of solutions recently considered. +f a potential solution appears on the tabu list, it cannot be revisited until it reaches an e&piration point. E +ntermediate"term$ +ntensification rules intended to bias the search towards promising areas of the search space. E 5ong"term$ iversification rules that drive the search into new regions (i.e. regarding resets when the search becomes stuck in a plateau or a suboptimal dead"end). -hort"term, intermediate"term and long"term memories can overlap in practice. %ithin these categories, memory can further be differentiated by measures such as fre#uency and impact of changes made. !ne e&le of an intermediate"term memory structure is one that prohibits or encourages solutions that contain certain attributes (e.g., solutions which include undesirable or desirable values for certain variables) or a memory structure that prevents or induces certain moves (e.g. based on fre#uency memory applied to solutions sharing features in common with unattractive or attractive solutions found in the past). +n short"term memory, selected attributes in solutions recently visited are labeled 9tabu"active.9 -olutions that contain tabu"active elements are banned. Aspiration criteria are employed that override a solutionBs tabu state, thereby including the otherwise"e&cluded solution in the allowed set (provided the solution is good enough2 according to a measure of #uality or diversity). A simple and commonly used aspiration criterion is to allow 0
solutions which are better than the currently"known best solution. -hort"term memory alone may be enough to achieve solution superior to those found by conventional local search methods, but intermediate and long"term structures are often necessary for solving harder problems.?> abu search is often benchmarked against other metaheuristic methods " such as -imulated annealing, genetic algorithms, Ant colony optimization algorithms, /eactive search optimization, 4uided 5ocal -earch, or greedy randomized adaptive search. +n addition, tabu search is sometimes combined with other metaheuristics to create hybrid methods. he most common tabu search hybrid arises by joining - with -catter -earch, ?F?D a class of population"based procedures which has roots in common with tabu search, and is often employed in solving large non"linear optimization problems. abu list$ solution that have been visited recently and cannot be visited in near future until e&piration Aspiration list$ onditions at which tabu list members will be considered .usually ie +f penalty of tabued solution is less than current best
andidate list$ All the top #uality solutions are kept here and the best is chosen as current best +f solution in candidate list is chosen as current, that entry is tabued so in ne&t few steps it wonGt be considered 0! $ we have to choose from candidate list even if it is worse than current solution
!." 1roblem +tatement
%e are going to use genetic algorithm to solve university time table scheduling problem. imetable scheduling is an 01 hard problem. *any different ways and algorithm were used like backtracking, dynamic programing, brute force etc. but all these methods were really inefficient as well as #uality of the solution was mediocre. hrough already published research papers we can conclude heuristic search algorithms are way more time efficient and give better #uality of the solution. there are many heuristic algorithms like simulated annealing , tabu search ,genetic algorithm which can be useful to achieve a good #uality solution .but using a combination of these algorithm was apparently more useful. Here our approach is focused on 4enetic Algorithm but use of other algorithms (heuristic or non" heuristic ) is there wherever its proved to be better.
2
6ollowing is the problem statement for creating a niversity timetable
he following will be the input which will be taken by the user$ •
•
name of the teachers in the university All the subjects they are going to teach.
•
*a&imum number of hours allocated to teacher
•
0ame of the different batches
•
-ubject that those different batches will be taught
•
0ame of all the rooms available
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0umber of days in a week classes are going to be held
0ow we have three variables namely$ •
-tudents (batches)
•
eachers
•
-ubjects
6ollowing are the assumptions before we make 7atch"-ubject"eacher combinations •
!ne teacher only teaches ; class
•
+f a batch have a class of particular subject in their curriculum, they will be studying it for ; hour a week
•
0o preferences are allowed for any teacher
Here we will try to make valid teacher Isubject"batch pairs, 6ollowing are the constrains which must be followed •
*a&imum number of teaching hours of teachers must not be e&ceeded
•
eacher should get the classes only for the subjects it teaches
•
7atch"subject"teacher must be uni#ue
•
7atch"subject pair within 7atch"subject"teacher must be uni#ue
his is the main array (char final) -0! ; = J .
7atch 7; 7= 7K LLLL..
.
LLLL.
3
eacher ; = J LLLL..
subject -= -= -; LLLL..
LLLL.
LLLL.
.
LLLL..
LLLL..
LLLL..
=@
LLLL..
LLLL..
LLLL..
. . . F@ . .
6ollowing are the errors and scope for further analysis of the allocation •
0o teacher e&ist to teach particular subject
•
1ossibility that no teacher left for teaching a particular subject, since all e&isting teachers have been allocated a class
•
here are teachers in the university which have no classes to teach
•
-cope for remove few teachers ,as even without them all the classes can be allocated easily
0ow we have allocated a uni#ue integer to every batch Isubject Iteacher combination and we will try to allocate those integers to an J" array
!4
6irst +nde&
M number of days
-econd inde& Mnumber of time slots in a day hird +nde&
M number of rooms
%e will try to allocate each integer to ; of the slots here
HARD CONSTRAINS
hese are the constrains that must be folloe! during the coding. and without satisfying these constrains solution is not valid. hey are listed as follows$ •
!ne teacher cannot teach two classes at the same time
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A group of student cannot attend two classes at one time
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wo classes cannot be held in one room at the same time
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A teacher cannot teach on more than ma&imum allotted hours
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here cannot be any classes on holyday
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*a&imum no. of hours allotted for a day cannot be e&ceeded
SO"T CONSTRAINS
hese are the constrains that are not compulsory for a solution to follow , but #uality of the time table is decided by accomplishment of these constrains hese are listed as follow. •
+n students time table we have to try to reduce the number of breaks during a day, so that they have more time for self"study at home.
•
%e must ensure that a group of student donGt have to attend collage for only to attend ; class
•
not more than timema&<= class for teacher and student
•
less breaks for teachers
"ITNESS "UNCTION
!!
his is the function that will decide the #uality of the time table that are created by putting penality on each soft constrains accordingly .lesser the penalty better is the #uality of the time table. 6itness M ;<1enalty 1enalty for each soft constrains is as follows
;) 6or teacherGs time table ; hour break is preferred over = or J hour breaks .so penality for each break for n hours will be (n";)N= i.e. (n";)O(n";) =) his implies J hours I penalty of C =hours " penalty of K 6or each break of n hours of student penalty is (n)NJ his implies = hour break I penalty of D J hours break Ipenalty of =F
J) +f for a batch there is only one class in that day penalty is J: K) 6or each back to back classes of teacher penalty of @ @) +f there are any 7atch"subject"teacher combination could not be allocated to the J"d array then penalty is @:: AIM
!ur aim is to find the best time tables having the following #ualities •
6ollow all the hard constrain
•
*aking optimal use of the resources
•
7est fitness function
1.4) Approach to problem in terms of technology /platform to be used 's we are using genetic algorithm to generate time5table, which consist of three methods namely crossover, mutation and calculating fitness functions , &enetic algorithm instead of focusing on one most optimal solution ,it focus on many different sub5optimal solution ,that results in high need of computation and memory. +o for this project due to high computation needs we are using 677 , since it is most optimum in terms of computation.
INPUT AN !T"#A$% !"
0ame of the teachers, subjects and batches is taken as input All these values will be stored in different te&t files. %e chose to store it in te&t file instead of database because of the computational advantage. And no sensitive data is needed to be stored.
Storing !ata into the !ata structure from te#t file •
ata is read from the te&t file and stored in the arrays
•
6rom that2 array final2 is created
In$ut of the %alues of &arra' final( into the )*D arra' •
wo different algorithms are used one is the the basic random algorithm
•
!ther one is the new algorithm called (arrow system2)
•
hese two different algorithms are compared and analysed.
Im$ro%ement of the basic solution
wo different ways are there to do that •
rossover
•
*utation
Deci!ing the best solution to a$$l' the crosso%er an! mutation •
/oulette %heel -election
•
/ank -election
•
ournament selection
"itness "unction •
+t is the measure How good the solution is 2
•
ime"table of every teacher and batch is stored in different arrays
•
6itness function defined above is applied
1.&)!upport for No'elty/ significance of problem +t is area of great interest for algorithmist to tackle time"table scheduling problems as not only re#uired by vast organisations but also give them opportunity to tackle 01hard problems IT IS RE+URED IN , •
• •
All the universities and schools for scheduling classes scheduling e&ams scheduling events during the festival all the airports for scheduling flights railway stations for scheduling trains
!
•
programme management $it is the process of managing several related projects, often with the intention of improving an organization8s performance
even after being such an important area ,mostly because of comple&ity of the problem scheduling is done manually ,so to help big organisation for there such important task ,it is important to design a proper algorithm so that a lot of time can be saved . •
different ways that can be combined for solving the problem
J chromosomes from set; are taken hey are allowed crossover with all the chromosomes os set= his reduce the candidate solution Hence decresce the calculations
Coo$erati%e genetic algorithm wo spicies of chromosomes ( solutions) • are made hree solutions are chosen from each • spicies hey are allowed to crossover • alculation decrese 7ut diversity also • decreases .memetic algorithm, 4A+0 greatly coutperforms
@) niversity ime able -cheduling using 4enetic Artificial +mmune 0etwork
4A .very promising algorithm for solving the time tabling problem. .onverges faster .reaches global ma&ima
!
>) Algorithms of ime table 1roblem Application and 1erformance valuation in ourse -cheduling -ystem
.4A provide diverse values of solution .4A reaches to global ma&ima .mutation increases the diversity .-aving the best solution is helpful . all the steps are same but instead of mutation ,sometimes tabu search is performed.
F)An Algorithm to Automatically 4enerate -chedule for -chool 5ectures sing a Heuristic Approach
. e&tension to constraint propagation .some pre constrains can save a lot of computation .fitness fuction is improved imetables are randomly selected from the population and used for breeding. 0o favouritism is given to fitter timetables.
D)A +*A75 1/++!0 6!/ H0+A5 A+!0A5 -Q-* -+04 40+ A54!/+H* C) se of Active /ules and 4enetic Algorithm to 4enerate the automatic ime"able
. he objective of the work is to create a model used to generate the acceptable schedule using probabilistic operators.
;:)*anaging ncertain ata using *ulti riteria /epeat rossover 4enetic Algorithm
*ulti riteria 4enetic algorithms can produce good balance between their precision and their comple&ity. -ystems use genetic algorithms for providing learning and adaptation capabilities from uncertain data set
=) 7ackground -tudy
2.1) Literature Survey ;) 4enetic Algorithms vs. abu -earch in imetable -cheduling -. . huO7 0ational Raohsiung +nstitute of echnology, aiwan, /.!.., Oniversity of -outh Australia, H. 5. 6ang , A+ Application 4roup . S . ept, hina -teel orp, aiwan, /! +,;CCC
!/
to find the optimum solution throught genetic algorithm through genetic algorithm and tabu search,both of them can beat searching like back tracking hands down.in this research paper tabu search performed better than genetic algorithm. -till since this was the first paper to tackle scheduling problem through heuristic search so this have great importance in this major project.
=) -olving imetable -cheduling 1roblem sing 4enetic Algorithms 7ranimir -igl, *arin 4olub, 3edran *ornar 6aculty of lectrical ngineering and omputing, niversity of Pagreb nska J, +:::: Pagreb, roatia +"=::J his paper uses genetic algorithm to solve the time table scheduling problem using J"d array of day ,tim and classes,and uses crossover and mutation process to optimise.this paper also make huge improvement over genetic algorithm as shown above.main difference between this and the above research paper is the improvement that they did on genetic algorithm,stating he first step of the improved crossover operator simply copies e#ual genes from parents to the child individual. 0o additional conflicts can be added through copying those e#ual values. ifferent parentGs genes are specially marked and delegated for further processing. +n the second step, the crossover operator checks the list of e#uations for each marked gene in child individual and counts the number of potential conflicts generated for both parentGs choices. he gene from a parent that generates fewer conflicts is chosen, so no additional conflicts are generated in addition to the conflicts caused by the parents.
epartment of +nformation *anagement heng"-hiu niversity Tiun"Huei Ho, epartment of lectronic ngineering, heng"-hiu niversity
Here this algorithm chooses ;: "=: random solutions and call them lolal ma&imaGs.and out of those
!0
=: solutions whichever is the best is called global ma&ima .each of those =: solutions are changed by changing ="J of their positions that are chosen randomly .to find better solutions .out of those =: solutions their best solutions are found and out of them the best is chosen to be global ,in this way algorithm have power to come out of local ma&imaGs as compare to genetic algorithm(traditional)algorithm performed better ,but in this paper it is not compared with improved genetic algorithm.
K) USIN0 A 0ENETIC A10ORITHM O/TIMI2ER TOO1 TO SO13E UNI3ERSIT4 TIMETA51E SCHEDU1IN0 /RO51EM
-ehraneh 4haemi *ohammad aghi 3akili Ali Aghagolzadeh U4haemi, *vakil, AghagolV Wtabrizu.ac.ir 6aculty of lectrical S omputer ngineering niversity !f abriz +"=::F
Mo!ifie! genetic algorithm-mga. •
two sets of chromosomes are made(solutions)
•
J chromosomes from set; are taken
•
hey are allowed crossover with all the chromosomes os set=
•
his reduce the candidate solution
•
Hence decresce the calculations
Coo$erati%e genetic algorithm •
wo spicies of chromosomes ( solutions) are made
•
hree solutions are chosen from each spicies
•
hey are allowed to crossover
•
alculation decrese
•
7ut diversity also decreases
his type of chromosome representation needs less number of bits for each gene. herefore algorithm needs shorter time for running. he obtained results show that -X operator in upper mutation rates and X and *X operator in lower mutation rates perform well and *X operator is better than others. 7y varying mutation rate during algorithm running better results are achieved.
!2
Also ournament selection operates better than /ank %eighting selection. he *4A and 4A methods with population size less than 4A yield better results and accelerate the algorithm. 0ote that the unsatisfied constraints number with 4A method is less than *4A method
@) Uni%ersit' Time Table Sche!uling using 0enetic Artificial Immune Netor6
Antariksha 7haduri +"=::C -cheduling is one of the important tasks encountered in real life situations. 3arious scheduling problems are present, like personnel scheduling, production scheduling, education time table scheduling etc. ducational time table scheduling is a difficult task because of the many constraints that are needed to be satisfied in order to get aY feasible solution. ducation time table scheduling problem is known to be 01 Hard. Hence, evolutionary techni#ues have been used to solve the time table scheduling problem. *ethodologies like 4enetic Algorithms (4As), volutionary Algorithms (As) etc have been used with mi&ed success. +n this paper, we have reviewed the problem of educational time table scheduling and solving it with 4enetic Algorithm. %e have further solved the problem with a memetic hybrid algorithm, 4enetic Artificial +mmune 0etwork (4A+0) and compare the result with that obtained from 4A. /esults show that 4A+0 is able to reach the optimal feasible solution faster.than that of 4A. +n this paper, we discussed in detail the educational time tabling problem, which is basically a scheduling problem. he problem is known to be 01 hard, and is of very high importance to educational institutes. %e discussed in brief the various methods used in solving the time tabling problem. A detailed overview has been provided for previous works for solving this problem, with links to such works, with more emphasis on solving the problem using evolutionary algorithms. hen, the problem is structured and a chromosome)
Algorithms of Time table Problem Application and Performance Evaluation in Course Scheduling System Qunfeng ong;, 7ei Zi=, %ushi 4ao;, Zingzhu %ang;
abu search algorithm can accept inferior solutions in the search process8 the new solution is not randomly generated in the current field, but selected the best solution, which the best solution of probability is more than other solutions. hen the search can escape from local optimal solution, and turn to other areas, increasing the probability of global optimum solution for local search. %e use tabu search to replace the mutation operator. he tabu search can get better individual solutions, and be out of local optimum he tabu search is applied to the local search, it can greatly avoid the phenomenon of over"mature for genetic algorithms, but it is also unwise to use tabu search fre#uently, it waste running time. +n fact, we can call the tabu search to replace the mutation operation in appropriate time. -o basically all the steps are same but instead of mutation ,sometimes tabu search is performed. 4enetic search algorithm has been validated in the test and achieved good results. 4enetic search algorithm is a more practical method, convergence is fast, and time distributes uniform. 7ut in practice conditions may not be terminated, the purpose is to provide different possible solutions in order for users to choose. However, if the constraint conditions are too harsh, there may be no feasible solution, in such case, human intervention is necessary. ourse scheduling problem is a multidisciplinary hard problems, this article attempt genetic algorithm only on the field of course scheduling, the encoding of genetic algorithms and parameter settings needed to be improved and e&plored, the effect on the algorithm need be improved in further.
7. An Algorithm to Automaticall' 0enerate Sche!ule for School 1ectures Using a Heuristic A$$roach Anirudha 0anda, *anisha 1. 1ai, and Abhijeet 4ole
SUMMAR4
+n this paper authors have tried to avoid random selection, by there point of view +t decreases an overhead of computation when we apply all the proper condition while mapping teachers with time"room
"4
And if they see any clash the lecture already mapped with that time slot is sent to clash data if that lecture is not present in output data slot that is if the teacher of that lecture is no available ata that time he intention of the algorithm to generate a time"table schedule automatically is satisfied. he algorithm incorporates a number of techni#ues, aimed to improve the efficiency of the search operation. +t also, addresses the important hard constraint of clashes between the availability of teachers. he non"rigid soft constraints i.e. optimization objectives for the search operation are also effectively handled. 4iven the generality of the algorithm operation, it can further be adapted to more specific scenarios, e.g. niversity, e&amination scheduling and further be enhanced to create railway time tables. hus, through the process of automation of the time"table problem, many an" hours of creating an effective timetable have been reduced eventually. he most interesting future direction in the development of the algorithm lies in its e&tension to constraint propagation. %hen there is a value assigned to a variable, such assignment can be propagated to unassigned variables to prohibit all values which come into conflict with the current assignments. he information about such prohibited value.
D) A +*A75 1/++!0 6!/ H0+A5 A+!0A5 -Q-* -+04 40+ A54!/+H* SANDEE/ SIN0H RA8AT an! 1A9SHMI RA:AMANI Qear"=:;: Journal of Theoretical and Applied Information Technology
SUMMAR4 his paper deals with the implementation of a computer program, which employs 4enetic Algorithms (4As) in the #uest for an optimal class timetable generator. he program is written in Tava and incorporates a repair strategy for faster evolution. his paper also e&plains an e&le usage of 4enetic Algorithms (4As) for finding optimal solutions to the problem of lass imetable. +t is seen that the 4A could be improved by the further incorporation of repair strategies, and is readily scalable to the complete timetabling problem. he system at present does not take care of other constraints like unavailability of lecturers, small size of rooms and time re#uired by the
lecturer to move from one class timetable is used at the dept. 8HAT;S NE8 5ree!ing Timetables imetables are randomly selected from the population and used for breeding. 0o favouritism is given to fitter timetables. A child timetable is bred by performing unity order based crossover on the parents. his means that each parent has an e#ual chance of providing each gene.
"!
<. Use of Acti%e Rules an! 0enetic Algorithm to 0enerate the automatic Time*Table
7harkha 0arang Ambika and /ashmi 7ansal Qear"=:;J International Conference on Emerging Tren!s in Engineering an! Management
SUMMAR4
his document proposes an optimized techni#ue to automate time table generation system. ime table generation system involves various challenging constraints of resources including faculties, rooms, time slots etc. he roposed techni#ue filters out the best of active rules and 4enetic algorithm to generate the optimized solution. 4enetic Algorithm and Active /ules together form a complete sphere for developing a system, which needs to satisfy various constraints. Active /ules provide event"condition"action2 model for the implementation of any rule based system. +n genetic algorithm every individual are characterized by a fitness function. After analysis if there is higher fitness then it means better solution and then after based on their fitness, parents are selected to reproduce offspring for a new generation where fitter individuals have more chance to reproduce. he objective of the work is to create a model used to generate the acceptable schedule using probabilistic operators.
;:) *anaging ncertain ata using *ulti riteria /epeat rossover 4enetic Algorithm Qear "=::C r.4./AHA*A0+ 0+T.1.T!-1H +nternational Tournal of /ecent rends in ngineering SUMMAR4 *ulti riteria 4enetic algorithms can produce good balance between their precision and their comple&ity. -ystems use genetic algorithms for providing learning and adaptation capabilities from uncertain data set. A set of techni#ues discussed here, can be used to enhance searching and retrieving information from an e&isting fuzzy Rnowledge 7ase (R7). he proposed genetic
algorithm tries to be capable of generating solutions from both crisp and fuzzy valued data. he search algorithm transforms vague and uncertain data represented as fuzzy sets for accurate solution generation. 1rocedures are proposed which learns from the different available knowledge base for its application on uncertain data. +n this paper, a repeat crossover 4enetic Algorithm is formulated for its usage on uncertain data represented as fuzzy sets and it can be implemented for many real world applications.
*.1 "'erall description of the pro+ect ""
)=>=> INTRODUCTION )=>=>=>./UR/OSE
+t is area of great interest for algorithmist to tackle time"table scheduling problems as not only re#uired by vast organisations but also give them opportunity to tackle 01hard problems IT IS RE+URED IN , •
All the universities and schools
for scheduling classes
scheduling e&ams
scheduling events during the festival
•
all the airports for scheduling flights
•
railway stations for scheduling trains
•
programme management $it is the process of managing several related projects, often with the intention of improving an organization8s performance
even after being such an important area ,mostly because of comple&ity of the problem scheduling is done manually ,so to help big organisation for there such important task ,it is important to design a proper algorithm so that a lot of time can be saved . ?=>=@. DE"INATION
imetable -cheduling 1roblems are particularly challenging for Artificial +ntelligence and !perations /esearch techni#ues. hey are problems of time"based planning and combinatorial optimization that tend to be solved with a cooperation of search and heuristics, which usually lead to satisfactory but sub"optimal solutions. here are many kinds of timetable scheduling problems in the daily life, such as e&amination, lecture, transportation timetables. +n general, there are several common difficulties in these kinds of problems$
"
•
here e&ists no deterministic polynomial time algorithm8 they are computationally NP complete problems.
•
+t is very difficult to design effective heuristics8 Advanced search techni#ues using various heuristics to prune the search space will not be guaranteed to find an optimal solution.
•
A general algorithmic approach to a problem may turn out to be inapplicable8 certain special constraints are often re#uired in a particular instance of that problem.
•
Real world timetable scheduling problems often involve constraints that cannot be
precisely represented or even stated. +n a timetable"scheduling problem, events, for e&le e&am subjects, must be slotted to certain times which satisfy several of constraints. Rnowledge"based and !/"based approaches to solving such problems are hard to develop, are often slow and can be infle&ible because the architecture itself was based on specific assumptions about the nature of the problem. onstraint satisfaction techni#ues have been used to solve hard constraints8 however, it is more difficult to handle soft constraints such as preferences. *ost often people still resort to handcrafted solutions starting from an easier solution and making a se#uence of modifications to cater for changed re#uirements. his typically leads to suboptimal solutions that bring significant organizational or financial penalties. ? ;J developed a 4enetic Algorithm (4A) based timetable system which has been successfully used by a university. )=>=>=). SCO/E •
modification of the genetic algorithm by
modified genetic algorithm
coperative genetic algorithm
4enetic Artificial +mmune 0etwork
ombining it with tabu search
"-
ombination of mutation and crossover
•
-ince it is an 01hard problem, no best solution can e&ist ,there is always scope for more
•
reate a whole new algorithm for this pupose
•
6ind a new combing algorithm, that is we can try to find an alrithm which we can combine with 4A for better result
•
ry to combine 4A with non"informed search like backtracking
•
ombination of more than two algorithm
•
Application of mutithreading, since there are so many computation are going on, there is a lot of scope for multi"threading
•
+ncreasing efficiency by making multi"threading algorithm more efficient
•
1roject can be e&tend for the use of air"port management team, for scheduling flights
•
seful for railway management team for scheduling trains
•
4ives very deep knowledge about , how to tackle np"hard problem.
*.1.1.4) ",%#,I%+n the last few years, colleges and universities around the world are e&panding enrollment in order to develop more high"#uality personnel, various countriesB educational reform is also thorough unceasingly. he essence of course scheduling #uestion is the curriculum, the teacher and the student should be assigned in the appropriate time section to the appropriate classroom. +t is a multi" objective scheduling problem which involve more factors, and known as the ime table 1roblem in operations research (ime table 1roblem[ called 1). Automated ourse -cheduling algorithm, 1, was proved to be 01"complete problem as early as in the F:s. he computing time is e&ponential increase, and integration time and space as dual constraints. 6rom classroom (space) point of view it is similar to the binpacking problem. 7ut increased many restraints in the time. -o it
"
is a more comple& problem than the bin"packing problem. %e can establish theoretical profundity from this thesis. At present, in mathematics did not have an general algorithm to be able to solve the 01 complete problem well. -olving the 01 complete problem only to be able to depend on the appro&imate method, many scholars have conducted the research regarding this.
1rovide a good algorithm to our audience so that they can generate a helpful time table with least wastage of their time and recourses
•
To generate a time table scheduler we have to use heuristic search
•
Till date ,most helpful heuristic search has been 9genetic algorithm:
•
;e will first define the problem
•
'll their constrains ,soft as well as hard
•
!ur aim is to find a solution which satisfy all the hard constrains for sure and as many soft constrain as it can in accepatable time and space
•
Appling heuristic search like genetic algorithm or improved algorihms over genetic algorithm as we have seen earliar
•
ompare the algorithms
•
-ince it is an 01hard problem and no fully efficient algorithm can be found
"/
•
As we have learned from the research papers that by using best combination of algorithm like genetic algorithm(crossover and mutation) ,tabu search , particle swarm etc.
3.2.2.2 Product functions
•
1roduct have many functionalities and wide area for use
•
All the universities and schools
for scheduling classes
scheduling e&ams
scheduling events during the festival
•
all the airports for scheduling flights
•
railway stations for scheduling trains
•
programme management $it is the process of managing several related projects, often with the intention of improving an organization8s performance
•
#fficiently generates timetables ,so vast number of resources can be utilised
*.1..*)user characteristics •
+01 A0 -!/A4$ program will take all the input from the user and will save it in its database for all the future computation
•
/A+04 /A0!* -!5+!0-$ using the above input program,by using the appropriate algorithm will generate some random solutions
•
A55A+04 6+0-- 60+!0$ by using the already decided penality ,program will calculate the fitness functions of the solutions and will sort them in increasing
"0
•
A115Q+04 40+ A54!/+H* H/!4H *A+!0$ them by using fitness score program will apply genetic algorithm or its improved version to create best possible solution in minimum possible time
•
-/+Q$ program will delete all the information of the organisation after creating time" table and will never upload it publically
•
66++0Q$ 1rogram will try to take minimum time and resources
•
-/"6/+05Q +0/6A$ no knowledge about the software will be re#uired to use it ,very straight design
•
*5+15 -!5+!0- $ *ore than ; acceptable solution will be provided,so that organisation can choose out of many solution ,which one suits them best.
"perating en'ironment
"2
*.1..4) esign/implementation constraints
HARD CONSTRAINS
hese are the constrains that must be folloe! during the coding. and without satisfying these constrains solution is not valid. hey are listed as follows$ •
!ne teacher cannot teach two classes at the same time
•
A group of student cannot attend two classes at one time
•
wo classes cannot be held in one room at the same time
"3
•
A teacher cannot teach on more than ma&imum allotted hours
•
here cannot be any classes on holyday
•
*a&imum no. of hours allotted for a day cannot be e&eeded
SO"T CONSTRAINS
hese are the constrains that are not compulsory for a solution to follow ,but #uality of the time table is decided by accomplishment of these constrains hese are listed as follow. •
+n time table of teacher more ; hours breaks are preffered then =or J or more hours break
•
+n students time table we have to try to reduce the no. of breaks during a day,so that they have more time for self study at home.
•
ven if there are breaks in students time table we must ensure there are more ; hour break rather than = or J hours break
•
%e must ensure that a group of student donGt have to attend collage for only to attend ; or two class,there has to be minimum J classes in a day so that it will be worth it to come to collage on a particular day
•
%e must avoid back to back classes for teachers
*.1..&) Assumptions and dependencies •
't max - variables for which computation is reqired
•
•
#nough memory is available in the machine for the to do all the required computation •
•
•
Input is done correctly ,bug due to spelling mistake wont be handled,that is for example 4
't one place its physics and at another is phisics, so these two will be taken as two subject even might user intended to write the same thing •
!ne teacher only teaches ; class
•
+f a batch have a class of particular subject in their curriculum, they will be studying it for ; hour a week
•
0o preferences are allowed for any teacher
*. 0unctional reuirements •
1age should be available on the internet for the user
•
1age should query about if user want to make the time5table
•
=ption about exiting should always be there
•
1rogram should be able to take input about teacher and store it
•
1rogram should be able to check if no. of teachers doesn>t over flow and if they do then it should show error message accordingly
•
then option should be provided for editing teacher information
•
1rogram should be able to take input about batch and store it
•
1rogram should be able to check if no. of batch doesn>t over flow and give error message accordingly.
•
Then it should be able to provide options about reducing batches or increasing the teachers
•
1rogram should be able to check if no. of teachers are sufficient for amount of batches by using information about maximum working hours of the teacher
•
1rogram should be able to take input about rooms and store it
•
1rogram should be able to check if no. of rooms will be sufficient for given batches and display the error message
•
Then program should be able to give option about adding new rooms or reducing batches
!
•
1rogram should be able to compute the time table by appling appropriate algorithm
•
Then time table should be uploaded so that user can download it
*.* Non 0unctional reuirements
*.*.1) /erformance reuirements •
high
•
solutions should be created at fast speed , so that user do not have to wait for a long time
•
size of the page on the website should be low so that time take to load our website is low
•
site should be really easy to use so that user don>t have to waste their time in understanding 9how to use it:
•
computation of time table should be fast
•
resources used for computation should be least as possible
•
algorithm should be efficient
*.*.)"perating constraints •
user should provide all the information
•
user should no close the window while computation is going on
•
user should be connect through internet throughout the process
•
user should have software in which time5table is available for download "
J.J.J) /latform constraints •
user should have a browser in its machine
•
*s word or pdf should be download on the userGs machine
•
-erver should have operating system that support c\\ and html
•
\\ should be available at the server
)=)=B. Accurac' an! /recision
ser should enter all the data correctly that is no spelling mistake ,especially when information about subject is added •
0o repletion of data ,as program will not be prepared to tackle repetition
•
-oftware wont be able to provide most efficient result as heuristic search is used
)=)=?. $ortabilit'
's basic software are used like c77 and html software will be portable
*.& esign iagrams *.&.1Use ase diagrams
*.*. lass diagrams / ontrol 0lo2 iagrams
-
/
0
2
3
-4
-!
4) Implementation details and issues 30ocus on No'elty 0unctionality ompleity and 5uality) -.!( Implementation details and issues
Input and storage he following will be the input which will be taken by the user$ •
•
name of the teachers in the university
•
All the subjects they are going to teach. *a&imum number of hours allocated to teacher
•
0ame of the different batches
•
-ubject that those different batches will be taught
•
0ame of all the rooms available
•
0umber of days in a week classes are going to be held
These values are stored in text files, for faster computation Storing !ata into the !ata structure from te#t file •
ata is read from the te&t file and stored in the arrays
•
6ollowing is the list of arrays (only important ones)
eacher
"
contains name of the teachers
7atch
"
contains name of the batch
-ubject
"
contains list of subject
each]sub
"
list of teachers and the subject they teach
7atch ]sub
"
list of batches and subjects they are being taught
6inal
"
uni#ue pair of 7atch"subject"eachers
his is the array final2 his is the most important array .
-0! ; = J .
7atch 7; 7= 7K LLLL..
eacher ; = J LLLL..
subject -= -= -; LLLL..
.
LLLL.
LLLL.
LLLL.
.
LLLL..
LLLL..
LLLL..
-"
=@
LLLL..
LLLL..
LLLL..
. . F@ .
Here we will try to make valid teacher Isubject"batch pairs, 6ollowing are the constrains which must be followed •
*a&imum number of teaching hours of teachers must not be e&ceeded
•
eacher should get the classes only for the subjects it teaches
•
7atch"subject"teacher must be uni#ue
•
7atch"subject pair within 7atch"subject"teacher must be uni#ue
6ew other things to take care of$ •
0o teacher e&ist to teach particular subject
•
1ossibility that no teacher left for teaching a particular subject, since all e&isting teachers
•
have been allocated a class here are teachers in the university which have no classes to teach
•
-cope for remove few teachers ,as even without them all the classes can be allocated easily
In$ut of the %alues of &arra' final( into the )*D arra'
Here now we will allocate the -0o of the final 2 table into this integer J" array
-
6irst +nde&
M number of days
-econd inde& Mnumber of time slots in a day hird +nde&
M number of rooms
+n this case$ 0umber of days
M@
0umber of time slots in a day MD number of rooms
M>
-o this array have @ODO> slots,which is e#uals to =K: elements,as shown below
-o if there are n values in array final2, we have to allocate n value to these =K: slots 0^M=K: here are two methods to do this ;) /andom allocation
--
•
+n this method we chose a random values of
ay
ime
/oom
6or n times, for n combinations of 7atch"subject"teacher •
And allocate each combination to the inde& chosen
•
6or e&le for inde& ;,we get value
ay MK
me MF
/oom MJ
0ow if name of the J"d array is
his impies ?K?F?JM;
=) Arrow method •
+n this method we choose random values of
ay
ime
And NOT room •
+n this way we have chosen an ;" array of length ;Oroom
-
•
After we choose a random arrow, we try to fill all the slot of the arrow, by finding valid values in the final2 array
•
As there are daysOtime number of arrows
•
%e take all the arrows until we allocate all the combinations
•
%e might have to traverse the array final2 ,(dayOtime) times
Im$ro%ement of the basic solution
After the two algorithms used to generate the random solutions ,we used two methods namely crossover and mutation to improve the solution MUTATION
-/
CROSSO3ER
-0
"itness "unction
his is the function that will decide the #uality of the time table that are created by putting penality on each soft constrains accordingly .lesser the penalty better is the #uality of the time table. 6itness M ;<1enalty 1enalty for each soft constrains is as follows
;) 6or teacherGs time table ; hour break is preferred over = or J hour breaks .so penality for each break for n hours will be (n";)N= i.e. (n";)O(n";) his implies J hours I penalty of C =hours " penalty of K
=) 6or each break of n hours of student penalty is (n\;)NJ his implies = hour break I penalty of D J hours break Ipenalty of =F
J) +f for a batch there is only one class in that day penalty is @: K) 6or each back to back classes of teacher penalty of @ -2
@) +f there are any 7atch"subject"teacher combination could not be allocated to the J"d array then penalty is @::
6or e&le if given below is the time table of the batch b;
?ay@Time T!
T"
Aon
6lass!
Tue ;ed
6lass
Thur
6lass2
Bri
6lass!4
+at
6lass!
T
T-
T
6lass" 6lass6lass/
T0
T2
6lass
6lass0 6lass3 6lass!!
6lass!"
alculating Penalty •
Csing the above instructions we will calculate the penalty
•
for Aonday there are !D!hour break and !Dhours break 1enalty E % !7!(F7 %7!(F E "F7-F E 0"
•
T/
penalty on Tuesday 1enaltyE4 's there is only one class on that day
4.1. Algorithms 36odule 2 ise7 2ith respect to design)
No'ice algorithm take the 9data: array ,start traversing from ! to n G 6hose random day and time #sE#mptyHspace%( If%esEE!( r E reaker%( If %brEE4( uE ackup%( If %buEE4( -3
store it in unallocated array #mptyHspace%( G Bor kE 4 to roommax If%there is any unallocated space( Jeturn ! reaker%( G
Bor %kE4 to roomax( 6heck if that teacher and batch already have any class on arr)day*)time*)k* If %yes( Jeturn 4 #lse Jeturn !
ackup%( G Traverse the whole tt serially ,given dayDtime be the starting point #sE#mptyHspace%( If%esEE!( r E reaker%( If%brEE4( Jeturn 4 #lse if%brEE!( Jeturn ! Arro2 algorithm =ut of dayDtime arrow choose them randomly until data is finised or no arrow left Bor %kE4 to roommax( Bor%iE4 to n(
@@ n is maximum value of data needed to be allocated
6heck if the data can be allotted If%yes( reakK If%no allocation when loop is over( 6all it unallocated
4
6utation take the best solution for %iE4 to n(
@@n being predicted value for mutation
choose any two pair if day5time and room interchange there content calculate fitness of new solution if %better( replace it with earliar solution
rosso'er Take the two best solution Let there be predecided crossover point let it be c Let n be the max no of solution Bor %iE4 to c( #lement in solution ! be same #lement in solution " be same Bor%iEc to n( #lement in solution ! be from the solution" #lement in solution " be from solution !
4. #is8 Analysis and 6itigation
1
*
4
&
( !
9
:
;
Ris6 I!
Descri$tio Ris6 Area n - I!entif' of Ris6 Ris6 Areas for 'our $roect.
/ro bab ilit' -/.
Im$ac t -I.
RE -/ I.
1
;hile creating random solution possibility is large diversity of solution are not selected Instead of going toward maxima due to crossover and mutation
Algorithmic
M
M
'lgorithmic
M
M
Mitigati on /lan if F is G4;
Ris6 Selecte ! for Mitigat i on -4N. N
N
Cniform crossover can be used ,which doent change the couloms which are same for both
which randomare ,quality of the solution decrease
6rossover between different solution is used along with mutation to increase the diversity
the parents ,so that no new conflict is added
*
#ven though algorithm converges towards maxima ,but its local maxima and not global maxima
'lgorithmic
M
M
N
Csetabu search to increase the diversity that is tabu search can expand ability of solution to reach global maxima
4
'lgorithm is convergin g toward global
'lgorithmic
A
M
N
?uringthe process best solution is kept safe that is many
"
Conti nge nc' $lan if an'
&
maxima but during mutation or crossover, it somehow change the best solution for less
copies of best solution are made ,so that even when few copies are used for mutation and crossover, we still have best solution for future
better solution
comparison
1rogram is running for a very long time if solution doesn>t converges ,which might result in
algorithmic
L
M
N
Cser5input
A
A
<
memory overflow
(
Cser might make mistake during input of the data,that is for example user might write 9physics: as 9phisics: . then these will be taken as two different subject even when user didn>t
Aultiple termination conditions are applied ,based on time as well no. of iteration whichever is received first
intend that
9
Cser might add repeated data ,that is user might add name of one reacher twice and during the computati on more that maximum number of classes will be added for that teaher
Cser5input
A
A
<
:
+torage overflow,
hardware
L
L
!
<
hardware
L
L
!
<
as a large amount of storage is required for computati on ,if software is being run on old machine ,it might not allow to declare data structure of that high storage capacity
;
#ven when
-
terminatio n is defined properly,if sorware tried to run on machine with low computati on power , it will take a lot of time to execute and might even hang the system
&) Testing 30ocus on 5uality of #obustness and Testing) &.1 Testing Plan
T'$e of Test
8ill Test 5e
CommentsE#$lanations
/erforme!
Softare Com$onent
Reuirements Testing
Q-
/e#uirement esting is performed to determine that the program modules perform as per the design specifications provided by end user. +t will involve testing of all the re#uirements of the project depending upon various factors like resources,
*anual work, need to plan out all the software re#uirements, time needed to develop, technology to be used etc.
budget and time.
/e#uirement testing is testing the re#uirements whether they are feasible or not. 7ecause a project depends on a number of factors like time, resources, budget etc. 7efore start working on a project its important to test these re#uirements
Unit Testing
Q-
nit testing will test individual units
nit testing will be performed on individual blocks of code like the classes, functions, user controls etc. +t will be performed on every small block of code. 5ist of component . input teacher .input batches .time table allotment .mutation application
Integration Testing
Q-
+ntegration esting will be useful for testing the project as a whole. he integrated project involves the combination of various unit modules.
/
+ntegrating the various modules such as input,analyzing and storing input.generation of the time table etc.
/erformance Testing
Q-
1erformance testing helps in finding the server response time, server response time, performance specifications of the system when multiple users are using the system. +t will help in determining that whether the system meets the performance criteria or not in such a situation.
1erformance of oncerted is taken for multivariate #ueries and compared.
+n our project performance will be analyzed on performance of #uery and concurrency that is thread waiting for other threads completion.
Stress Testing
Q-
-tress testing is normally used to understand the upper limits of capacity within the application landscape. +t helps in determining the applicationBs robustness in terms of e&treme load.
oncerted is tested with high volume of data and high number of concurrent threads on various data structures. 5ocking manager and ransaction manager are similarily tested %iil be applied on$ .0o.of timetable can be generated and stored .mutation applied .data about teachers ,batches and subject,once we define the value of room,day and time
Com$liance Testing
0!
ompliance testing is the process of verifying whether a product meets the standard product specifications it was designed to meet. +t determines whether a product or system meets some specified standard that has been developed for efficiency or
0
0ot performing.
interoperability. %e do not re#uire this testing as there are no fi&ed standards in our project work area we basically strive for user satisfaction.
Consistenc'
Q-
onsistency testing is where we test the consistency of the data present in our storage structures and before and after transactions on the data.
1erforming
Testing
program behaves as the programmer is expecting. 'nother purpose is to check the robustness of the program
1oa! Testing
Q-
Load testing is performed to determine a system>s behavior under both normal and anticipated peak load conditions.
0eeds to be performed on whole application.
5oad testing is normally done by subjecting a computer, peripheral, server, and net application to a work level approaching the limits of its specification to measure its capability. %e need to perform load testing before running to ensure scalability of application according to the load.
%ill set input data to ma& 3olume Testing
Q-
3olume testing refers to testing a software application with a certain amount of data which can be the database size or the interface file.
2
@.= omponent decomposition and type of testing re#uired$ S= No=
> @
) B
1ist of 3arious Com$onents that reuire testing
T'$e of Testing Reuire!
nit +nput of the data /eading values from files and storing them nit in data structure How much input data can be handled 3olume 0o. of time tables can be generated and -tress stored
0o. of mutation can be perfromed 4enerating time table,trying to allocate most of the data
-tress nit
7lack 7o& esting %hitebo&testing
7
/unning code many time ,by changing data and some times with wrong data
onsistency
7lack 7o& esting
. List all test cases in prescribed Test Case i!
In$ut
e#$ecte! Out$ut
Status /ass"ail
1
Oalues entered about
+aved in the expected
pass
&
#xtended upto 344444
format +tored correctly in the data structure #very slot should be filled +toredcorrectlyinthe data structures Increased performance ofthehuiristic
pass
4
teacher and subjects Input values in the data base Oalues equivalent to the slots in time table !4
*
3
Bail Bail
pass
(
Migh no. values
'llocation with least huiristic
+ubjective
?ata set,some times incorrect
6orrect output,robustness
pass
9
.-( #rror and #xception Mandling
Test case i! *
4
Test case for com$onents Debugging Techniue How much input data can be Print debugging handled, volume, 7lack 7o& esting 0o. of time tables can be Post7mortem debugging generated and stored,stress ,black bo&
.( Limitations of the solution
0indings < onclusion /.! Bindings he initial scheduling problem with large number of binary variables has been reduced to the acceptable size by eliminating certain dimensions of the problem and encorporating those dimensions into constraints. he grouping of several binary variables into one gene value significantly reduced the individual size. 0ow it is possible to try to solve the full size problem (problem of the whole 6/ schedule) with genetic algorithm approach. -uch a representation of the scheduling problem achieves the acceptable algorithm speed, so small size problems are solved in tens of seconds. -ignificant improvements have been achieved by using intelligent operators. he intelligent algorithm converges much faster then the basic algorithm and represents a good starting point for complete solving of the full scale problem. 4enetic algorithm with basic operators used random genes for itGs operations and was not directed to a solution. Adding intelligence to the operators by finding genes with less conflicts improved algorithmGs behavior and overall solution of the problem. o completely solve the full scale problem, further algorithms improvements will have to be made. %hen generating the constraints, it could be useful to sign each one, so no constraint will be set (and checked) twice. +ndividuals should be generated in such way that classes which are more difficult to schedule occupy the front genes of an individual, while classes easier to schedule should occupy the back genes. his would be useful for intelligent crossover operator, which sets and checks the conflicts from front to back of the individual. Also, a parallel computing approach should be tried, so checking space of the problem could be widened. ach thread could start with different initial population and the #uality of solution is e&pected to be better.
/4
;hich results in incomplete allotment of the of the classes
#ven though there exists a solution ,old algorithm was not able find that
Its advantage was fast execution
ut its most robust
1rovide complete solution ,even for the big data set
6omplexity is greater
=elo2 are the fe2 comparison of the algorithm ?ata setE/0 =perating system $ Cbuntu !-.4'pplication$ g77 compiler 1rocessor$ inter core i ". ghz Jam$ -gb 'lgorithm !is new algorithm %arrow algorithm( 'lgorithm " is pure genetic algorithm y5axis is heuristic value x5axis is no. of mutation
Bollowing is the comparison of the two algorithm
/!
elow is the time taken by algorithm of different number of mutation y5axis is the time taken x5axis is the number of mutation
elow is the variation of heuristic value with the number of mutation
/"
he combination of the algorithm proved out to be better instead of using only ; algorithm, when we used combinations of = or more algorithm ,results gets better. As it is shown below instead of using mutation of tabu search alone ,combing them in e#ual proportion gave better fitness value
This is !45mutation P !45tabu vs "4 tabu computations
/
This is vs "4 mutation computations vs !45mutation P !45tabu
Tabu vs mutation
/." 6onclusion 4enetic search algorithm has been validated in the test and achieved good results. 4enetic search algorithm is a more practical method, convergence is fast, and time distributes uniform. 7ut in practice conditions may not be terminated, the purpose is to provide different possible solutions in order for users to choose. However, if the constraint conditions are too harsh, there may be no feasible solution, in such case, human intervention is necessary. ourse scheduling problem is a multidisciplinary hard problems, this article attempt genetic algorithm only on the field of course scheduling, the encoding of genetic algorithms and parameter settings needed to be improved and e&plored, the effect on the algorithm need be improved in further.
/. Buture ;ork
/-
+t is area of great interest for algorithmist to tackle time"table scheduling problems as not only re#uired by vast organisations but also give them opportunity to tackle 01hard problems IT IS RE+URED IN , •
• • •
All the universities and schools for scheduling classes scheduling e&ams scheduling events during the festival all the airports for scheduling flights railway stations for scheduling trains programme management $it is the process of managing several related projects, often with the intention of improving an organization8s performance
CHA11EN0ES
+t is area of great interest for algorithmist to tackle time"table scheduling problems as not only re#uired by vast organisations but also give them opportunity to tackle 01hard problems ven if time"table scheduling have vast number of uses around the world,still in most of the cases it is done manually and softwares are not being used. 6ollowing are the reasons$ 01hard problem ,and completely efficient solutions are impossible to find. • lassical algorithm like backtracking just doesnGt work ,may even result in space
/
#eferences
?;Algorithms of ime table 1roblem Application and 1erformance valuation in ourse -cheduling -ystem, Qunfeng ong;, 7ei Zi=, %ushi 4ao;, Zingzhu %ang;,-handong 1olytechnic niversity, Tinan, hina,+"=:;; ?=4enetic Algorithms vs. abu -earch in imetable -cheduling, -. . huO7 0ational Raohsiung +nstitute of echnology, aiwan, /.!.., Oniversity of -outh Australia, H. 5. 6ang , A+ Application 4roup,. S . ept, hina -teel orp, aiwan, /!, ;CCC hird +nternational onference on Rnowledge"7ased +ntelligent +nformation ngineeing -ystems, 31" Aug"+9 -ept ;CCC, Adelaide, Australia ?J-olving imetable -cheduling 1roblem sing 4enetic Algorithms , 7ranimir -igl, *arin 4olub, 3edran *ornar,6aculty of lectrical ngineering and omputing, niversity of Pagreb nska J, +:::: Pagreb, roatia, 25th Int. Conf. 'Information Technology Interfaces IT1 2003, June 16
1 !, 2003, Catat, Croatia ?Kimetable -cheduling sing 1article -warm !ptimization,-hu"huan hu, Qi"in hen epartment of +nformation *anagement,heng"-hiu niversity, Tiun"Huei Ho, epartment of lectronic ngineering, heng"-hiu niversity, Proceedings of the First International
Conference on Innovative Computing, Information and Control (ICICIC'06) ?@niversity ime able -cheduling using 4enetic Artificial +mmune 0etwork,Antariksha 7haduri, =::C +nternational onference on Advances in /ecent echnologies in ommunication and omputing ?>-+04 A 40+ A54!/+H* !1+*+P/ !!5 ! -!530+3/-+Q
//
+*A75 -H5+04 1/!75*,-ehraneh 4haemi *ohammad aghi 3akili Ali Aghagolzadeh U4haemi, *vakil, AghagolV Wtabrizu.ac.ir,6aculty of lectrical S omputer ngineering,niversity !f abriz,+ "=::>
"=?%TI,%> 'spiring to work in a good development environment that put my abilities to challenge and create value for the organization while contributing to my personal and professional growth.
%UATI"NA@ 5UA@I0IATI"N!> •
•
•
achelor of #ngineering in 6omputer +cience % "4!45"4!( with /3 3till *rd year) from Raypee Institute of Information Technology,
T%BNIA@ !CI@@!>
•
Unowledge of 6 language, 1ython , 1M1 ;orked on various project based on above languages ;eb Technologies $ MTAL,6++
•
asic Mardware Unowledge
• •
P%#!"NA@ !CI@@!>
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• • • • •
#xcellent Team worker and team facilitator. Mighly optimistic, ability to work efficiently even under pressure. Maving a peaceful mind while working. +elf 6onfidence. 1ositive attitude and punctual.
%DT#A U##IU@A# ATI,ITI%!> • • • •
1articipated in skating and table5tennis competition. ;as part of scout and guide team 'ctive participation in many dramatics, music and other cultural events at school. 1articipated in Inter +chool +cience Vuiz 6ontest at +chool Level.
B"==I%!> • • •
Listening Ausic. Interacting with people. 1laying table5tennis, Oolleyball and many outdoor sports.
P%#!"NA@ %TAI@!> •
0atherEs Name
•
ate of =irth !e 6arital !tatus Nationality @anguages Cno2
• • • •
> Ar.
> Ruly "0, !33" Aale > +ingle > Indian > #nglish P Mindi
%@A#ATI"N> I hereby declare that the information given above are true and to the best of my knowledge and belief.
