







Abstract
The manual system of preparing time table in colleges with large number of students is very time consuming and usually ends up with various classes clashing either at same room or with same teachers having more than one class at a time. to overcome all these problems, propose to make an automated system. The system will take various inputs like details of students, subjects and class rooms and teachers available, depending upon these inputs it will generate a possible time table, making optimal utilization of all resources in a way that will best suit any of constraints or college rules.
List of subjects may include electives as well as core subject.
Introduction
Time table scheduling has been in human requirements since they thought of managing time effectively. It is widely used in schools, colleges and other fields of teaching and working like crash courses. In early days, time table scheduling was done manually with a single person or some group involved in task of scheduling it with their hands which take lot of effort and time.
The college lecturetimetabling problem asks us to find some slots and classrooms which satisfy the constraints imposed on offered courses, lecturers, classrooms and so on. The problem is a combinatorial optimization problem belonging to NPhard class where the computational time grows exponentially as the number of variables increases. While scheduling even the smallest constraints can take a lot of time and the case is even worse when the number of constraints or the amount of data to deal with increases. Even the perfectly designed time table is reused for whole generation without any changes, proving to be dull in such situations. Other cases are caused because the problem is the number of employers or workers keeps changing, this result in rescheduling of time table urgently.
Institutions/Schools/Colleges are the regular users of such timetables. They need to schedule their course to meet the need of current duration and facilities that are available to them. However, their schedule should meet the requirement of new course addition and newly enrolled students to fresh batches with sections. This may result in rescheduling the entire time table once again for its entire batches and to be scheduled in shortest possible time before the batches course start. Another problem that occurs is while scheduling time table for exams.
Constraints cannot be violated under any circumstances. For example, two classes cannot be allocated to a single teacher at the same time period, two classes cannot be attended by a student at the same time, more than one class cannot be held at a room at the same time et cetera.
Problem identification
The timetabling problem can be described as follows. There is a finite set of courses C={c1,c2,ck}, a finite set of rooms in which the lectures can occur R={r1,r2,r?}, a finite set of potential time slots for the lecture T={t1,t2,tm}, and a finite set of instructors I={i1,i2,in}. An assignment is an ordered 4tuple (a,b,c,d) where a?C, b? R, c ? T and d ? I. An assignment has the straightforward interpretation: a lecture for course a starts at time b in room c and is taught by lecture d.
Given C, R, T, and I, the algorithm has to find a timetable (collection of assignments) which satisfies a set of constraints. Each constraint may be hard (must be satisfied) or soft (should be satisfied if possible).
The set of hard constraints that need to be satisfied in designing the proposed algorithm is:
 A course must not have two bookings simultaneously.
 Classrooms must not be double booked.
 A Classroom must be large enough to hold each class booked to it.
 Lecturers must not be double booked.
 A lecturer must not be booked when he/she is unavailable. ? Some courses require particular rooms. For example, experiments might be held in particular laboratories.
 Some courses need to be held consecutively. For example, four hourlong practical experiment.
The set of soft constraints that are considered in the algorithm is:
 Some lecturers do not wish to have classes assigned consecutively in time.
 There are preferred hours in which a lecturer’s course might be scheduled.
 Most students and some lecturers do not wish to have empty periods in their timetable.
 Course lecture should be booked before its tutorial or lab.
 More than three time period per day are not allowed for a class of students.
This problem is known to be NPcomplete and conventional bruteforce approach can only give a solution for a very small problem size. The running time grows exponentially as the problems size grows. A.Schaerf [7] presented a number of solution techniques for the timetabling problem and he stated that even for a specific class of timetabling problem, there are many possible solution techniques and some techniques are only good for some special cases.
Literature review
Mei Rui [1] In this paper, through the analysis and the summarization of the existing problems, a mathematical model for the course timetable system is proposed. At the same time, through the use of the pattern recognition technology in artificial intelligence, aiming at this mathematical model a new university course timetable system design program is proposed and realized. This program not only can well solve the shortages of the existing course timetable system, but also is simple and easy to operate, has strong versatility
Dipti Srinivasan Tian Hou Seow Jian Xin Xu [2] proposed that finding a feasible lecture/tutorial timetable in a large university department is a challenging problem faced continually in educational establishments. This paper presents an evolutionary algorithm (EA) based approach to solve a heavily constrained university timetabling problem. The approach uses a problemspecific chromosome representation. Heuristics and contextbased reasoning have been used for obtaining feasible timetables in a reasonable computing time. An intelligent adaptive mutation scheme has been employed for speeding up the convergence. But this system is difficult to implement since it considers entire university problem and evolutionary algorithm.
Shengxiang Yang, Member, IEEE, and Sadaf Naseem Jat [3] proposed that The university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NPhard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy which is used here is to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs is tested on two sets of benchmark problems in comparison with a set of stateof the art methods from the literature. The experimental results show that the proposed GAs is able to produce promising results for the UCTP.
Antariksha Bhaduri University Time Table Scheduling using Genetic Artificial Immune Network [4] in their article proposed that Scheduling is one of the important tasks encountered in real life situations. Various scheduling problems are present, like personnel scheduling, production scheduling, education time table scheduling etc. Educational time table scheduling is a difficult task because of the many constraints that are needed to be satisfied in order to get a feasible solution. Education time table scheduling problem is known to be NP Hard. Hence, evolutionary techniques have been used to solve the time table scheduling problem. Methodologies like Genetic Algorithms (GAs), Evolutionary Algorithms (EAs) etc. have been used with mixed success. In this paper, we have reviewed the problem of educational time table scheduling and solving it with Genetic Algorithm. We have further solved the problem with a mimetic hybrid algorithm, Genetic Artificial Immune Network (GAIN) and compare the result with that obtained from GA. Results show that GAIN is able to reach the optimal feasible solution faster than that of GA.
Proposed Method
After reviewing papers, We found that Local Search techniques is one of the best way to tackle this problem. The optimization problems and combinatorial search are tackled by Local Search metaheuristics are an emerging class of methods, which was recently proved to be very effective for a large number of combinatorial problems. The Local Search techniques are based on the iterative exploration of a solution space. At each repetition, the algorithm for Local Search moves from one solution to one of its “neighbors”, i.e., solutions that are (in some sense) close to the starting one.
One major drawback of this family of techniques is the lack of robustness on a wide variety of problems. In actual fact, in so many cases, these kinds of methods guarantee finding good results in practicable run times, while in other cases Local Search techniques are caught in the local minima. Combination of several neighborhood structures is one of the alternative approaches [5, 6] to cope with local minima. A set of neighborhood operators are given such that for given collection of basic neighborhoods, a new compound neighborhood is automatically created and prescribe the strategies for its exploration. This approach is based Multi – Neighborhood Search.
If there are a set of x courses,
C = {s1, . . . , sx},
a set of y periods, P = {1, . . . , y},
and a set of z rooms, R = {r1, . . . , rz}:
each course si consists of ci lectures to be scheduled in distinct time periods, and is attended by pi pupils.
Each room rj has a capacity cpj , expressed in terms of number of seats.
There are also m groups of courses, T1, ,Tm called curricula, such that there are common pupils for any pair of courses belonging to a curriculum Ti .
The output of the problem is an integervalued x & y matrix M , such that Mik = j (with 1 , j m) means that course si has a lecture in room rj at period k, and Mik = 0 means that course si has no class in period k. The matrix M is searched such that the hard constraints are compulsorily and the soft ones are almost satisfied and the violations are minimized.
By using Local Search to solve a Course Timetabling problem [7] , first the basic Local Search entities should be defined, namely the cost function, the search space and the strategy for generating the initial solution. Search space consists of all the assignment matrices for which the constraints (1) and (4) hold. States for which the hard constraints (2) and (3) do not hold are allowed, but are considerably penalized within the cost function. The cost function is thus a weighted sum of the violations of the aforementioned hard constraints plus the violations of the soft constraints (6) & (7). The weight of constraint type (5) is the number of students without a seat, whereas the weight of constraint types (5) and (7) is fixed to 5 and 2, respectively, to reflect their relative importance in our institution. Furthermore, as sketched above, in order to give precedence to feasibility over the objectives, hard constraints are assigned the weight 100, which is a value greater than the maximum value of soft constraints violations.
In detail, if we denote with fi(T) the measure of violation of the constraint (i) we choose
F(T) = 1000 * (f2(T) + f3(T )) + f5(T ) + f6(T ) + f7(T )
The initial solution is selected at random. A random matrix M is created which satisfies constrains (1) and (4). This is obtained by allotting each lecture of a course to an available period which is selected randomly, and to a random room, ignoring the fact whether the lectures are assigned to the same period [5].
Result
The final system should be able to generate time tables in completely automated way which will save a lot of time and effort of a department administration. Focus on optimization of resources i.e., teachers, classrooms etc. Provide a facility for everyone to view the time table. This application is provided with necessary details of faculty and subjects which are stored in database and then by making use of available data it generates timetable with minimum time when compared to manual generation of timetable.
Conclusion
It is complicated task that to handle many Faculty’s and allocating subjects for them at a time physically. So our proposed system will help to overcome this disadvantage. Thus we can produce timetable for any number of courses and multiple semesters. Separate timetable for the individual class, faculty and labs are generated automatically by this system. Various slot combinations can be acquired so that another timetable is generated as of need. The project reduces time consumption and the pain in framing the timetable manually. The project is developed in such a way that no slot clashes occur providing features to tailor the timetable as of wish.
The major benefit of this project is to store information at one place and it can be accessed via online transaction. Instead of tedious paper work, students can view the timetable with a quick turnaround.
VIII. References
 Mei Rue Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on (Volume:4 )
 Dipti Shrinivasan automated time table generation using multiple context reasoning for university modules Published in: evolutionary computation, 2002. cec ’02. proceedings of the 2002 congress on (volume:2)
 ] Sadaf N. Jat, Shengxiang Yang A mimetic algorithm for university course timetabling problem, 2008 20th IEEE International conference on tools with artificial intelligence.
 Antariksha Bhaduri University Time Table Scheduling using Genetic Artificial Immune Network 2009 International Conference on Advances in Recent Technologies in Communication and Computing
 L.DiGaspero Recolour, shake and kick are for the Examination Timetabling Problem by E.Burke and P. DeCausmaecker, editors, Proc. Of the 4th International Conference on the Practice and Theory of Automated Timetabling, August 2002,pages 404 , 407.
 L.DiGaspero Recolour, shake and kick are for the Examination Timetabling Problem by E.Burke and P. DeCausmaecker, editors, Proc. Of the 4th International Conference on the Practice and Theory of Automated Timetabling,August 2002, pages 128 , 132.
 A.Scharef,A survey of Automated TimetablingArtificial Intelligence Review, 1999, 13(2):87127
Cite this essay
Automated Time Table Generator. (2019, Nov 27). Retrieved from https://studymoose.com/majorieee1formergeexampleessay