Time Table Generation

Transcription

1 ISSN Time Table Generation #1 Vikas Gawade, #2 Kunal Deokar, #3 Prasanna Retherekar, #4 Shrinath Bhegade, #5 Prof A.D. Bhosale 1 gawadevikas13@gmail.com 2 kunaldeokar99@gmail.com 3 diprevprasanna@gmail.com 4 shrinath.bhegade@gmail.com 5 rajeabhaysinh1978@gmail.com #1234 Computer Enginering Department #5 Assistant Professor Computer Enginering Department, Pune University PVPIT Pune Maharashtra India ABSTRACT In this paper the proposed system presents a novel approach of solving School, College timetable scheduling using complex algorithms to design an effective model for scheduling with challenging constraints considerations. The objective of the research is to create a model using Genetic Algorithm, which can be effectively used to resolve difficult combinatorial optimization problems. Although, there has been an extensive research towards this field but majority of the research results are much in its nascent stage. The problem of timetable scheduling is described as a highly constrained NP-hard problem. Major issues of the hard and soft constraints required for scheduling are resolved rigorously, the scheduling solution presented in this paper is an adaptive one, with a primary aim of obtaining best the optimal solutions. ARTICLE INFO Article History Received :5 th May 2016 Received in revised form : 7 th May 2016 Accepted : 10 th May 2016 Published online : 12 th May 2016 Keywords:- Active Rules, Rule-Based agents, Genetic, Soft Constraints, Hard Constraints, Modules. I. INTRODUCTION Planning timetable is one of the most complex and error prone application. There are still serious problems like generation of high cost time table are occurring while scheduling and these problems are repeating frequently. Therefore there is a great requirement for an application distributing the course evenly and without collisions. The aim is here to develop a simple, easily understandable, efficient and portable application which could automatically generate good quality time table with in a second. The time table generation consist of main algorithm Genetic algorithm. Use of genetic techniques helps to produce a best schedule by regulating all the rules. II. LITERATURE SURVEY Paper name: Use of Active Rules and Genetic Algorithm to Generate the Automatic Time-Table.. This document proposes an optimized technique to automate time table generation system. Paper name: Automating class schedule generation in the context of university time tabling for information system.the thesis examine the university timetable generation problem. Paper Name: University Timetabling based on Hard Constraints using Genetic Algorithm. The proposed system presents a novel approach of solving University timetabling which is NP-hard problem using Genetic Algorithm. A Literature Review on Timetable generation algorithms based on Genetic Algorithm. The problem of timetable scheduling is described as a highly constrained NP-hard problem. III. PROPOSED SYSTEM In Genetic Algorithm: A population pool of chromosomes is maintained which is called strings. The chromosomes are strings of symbols or numbers. These are also called the genotype (the coding of the solution), whereas the solution itself is called the phenotype. These chromosomes need to 2015, IERJ All Rights Reserved Page 1

2 be evaluated for fitness. Poor solutions are ignored. After making small changes to remaining solutions "natural selection" is allowed to take its course. This helps evolve the gene pool so that better solutions are discovered. They have explained how Genetic algorithms (GA) work in a manner similar to Natural Selection. A population pool of chromosomes is maintained which is called strings. The chromosomes are strings of symbols or numbers. These are also called the genotype (the coding of the solution), whereas the solution itself is called the phenotype. These chromosomes need to be evaluated for fitness. Poor solutions are ignored. After making small changes to remaining solutions "natural selection" is allowed to take its course. This helps evolve the gene pool so that better solutions are discovered. Defining constraints and rules for generating standard efficient schedule: In this paper we have proposed an optimized technique to automate the time table generation. Set of constraints are defined which are divided into hard and soft constraints. Hard constraints constraints which need to be incorporated necessarily otherwise there is no guarantee of valid time tables generated. Soft constraints constraints that are obvious but fulfilling them is not so demanding. Solutions are considered to be better if these can be incorporated. Hard Constraints: 1.The system generates intermediate level as well many final reports including weekly time table, teacher timetable, room wise time table, student time table, department level time table etc. 2.User can set gap of the number of days among the lectures, it can dynamically be adjusted as well. 3.The time tabling algorithm tries to adjust courses to user customized slots according to specified time. 4.It depicts the progress of courses adjustment at intermediate report level and if clashes cannot be removed and impossible to adjust then displays that course and number of lectures, which cannot be adjusted. 5.No.of slots free, available are displayed. 6.Teacher must not clash in two different tables of a time table. 7.Certain lectures that may or may not appear at the same time in more than one class. 8.Classrooms must not be double booked. 9.Every class must be scheduled exactly once. 10.Classes of students must not have two bookings simultaneously. 11.A classroom must be large enough to hold each class booked to it. 12.Lecturers must not be double booked. 13.Each lecturer has their minimum and maximum limit of weekly working hours. Soft Constraints: 1.It prioritizes time slots according to customized priority. If lecture cannot be adjusted then it can be moved up in higher priority slot until adjusted accordingly. 2.All parameters are customized by the user. 3.Some classes require particular rooms. For example, experiments might be held in particular laboratories. 4.Some classes require classrooms to have particular equipment. For example audio visual equipment. 5.Some classes need to be held consecutively. Consider a six hour-long practical experiment. 6.System process for scheduling : The following system architecture shows working of the system: Fig 1: System Architecture. The paper shows module wise generation of time table. It includes: User Module. Database Module. Constraints Time table generation. structure of the automated time table generator : Parameters under consideration: 6 Branches Years in each Students in each Number Subjects of in 3 120/ , IERJ All Rights Reserved Page 2

3 each semester Number of Lecturers in each class rooms for each Laboratories for each Class capacity room Time duration for 1 Lecture Hour Working time 8: 30am- 3:30pm Lunch break 12:45pm- 1:30pm No Of 36 Faculties wise. Faculty Load 4-6 Syllabus work hours per subject 5-7 User Module: User module consist of User GUI. The GUI consists of Authorised User verification and input from the user. The authorized user should only be able to access the system. Much of the sensitive information as faculty work load, details shoud be known and have access to the authorized person. The valid user have authorities to accept or reject the generated time table. The input helps to generate schedule as per users need. The input accepted by the user is used to create a database of all the available data. The Inputs which will be needed by the system by users are: Detailed entries for no of faculties available. Faculty work load. Classrooms Details. Laboratory Details. Yearwise/ Semesterwise syllabus details. Work hours allotted in syllabus. Constraints and rules: The rule and constraints will be defined above given constraints will be used. The Hard constraints are the constraints which need to be incorporated necessarily otherwise there is no guarantee of valid time tables generated. Soft constraints are those that are obvious but fulfilling them is not so demanding. Solutions are considered to be better if these can be incorporated. The constraints will be verified by the system. System Module: The system module consists of : Database Handling. Rules Verification. Time table generation. Display generated schedules. Review/ Regenration. Database handling: Includes input details taken from the user. The details entries are used for creating a database and connectivity between system and database helps to smoothen our access. The database include Course database, Student Database, Lecture Database, Faculty Database, Rooms-Labs database. Rules verification: The rules and constraints defined are used for creating a valuable schedule which helps to cope up with the work load. Thus rules plays important role to generate optimized and user friendly schedule. So verifications of these rules are important to decide hard and soft constraints for generating schedule. Time table generation: Time table generation is the process of creating an efficient, bestfit, optimized, valuable, constraint satisfied schedule. In this paper, authors are using three combinatorial algorithm. The algorithms will be used generating schedule phase wise. Three algorithms will be used are genetic algorithm. The sequential use of algorithms helps to best optimized the results.the algorithm used will be a contributing to the schedule to outfit the best match. The main aim of these paper is to create a most effective, beneficial, estimable schedule that uses complex algortihm. Earlier paper had used single way approach for generating time table which included simulated annealing or else low moduled algorithm. The basic outline of a Genetic Algorithm is as follows: Initialize pool randomly for each generation { Select good solutions to breed new population Create new solutions from parents Evaluate new solutions for fitness Replace old population with new ones }The randomly assigned initial pool is presumably pretty poor. However, successive generations improve, for a number of reasons: Selection: During each successive generation, a proportion of the existing population is selected to breed a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions (as measured by a fitness function) are typically more likely to be selected. Mutation: It 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. Crossover:It combines the genetic material from parents order to produce children, during breeding. Since only the good solutions are picked for breeding, during the selection procedure, the crossover operator mixes the genetic material, in order to produce children with even greater fitness. 2015, IERJ All Rights Reserved Page 3

4 Review / Re-edit / Regeneration: The optimized time table generated will be reviewed by the authorized user if not satisfied with respect to constraints defined, inputs give, database change etc. Again an efficient, best fit optimized new time table would be generated to fulfill user s requirements. Output: Finally the satisfied time table would be accepted by the user and printed. Fig 3: Classroom Wise TT. IV. RESULT ANALYSIS Correctness refers to the validity of the final result obtained or the outcome of the experiments performed using the proposed approach, on the same hardware and software platform as compared to the original or base approach. The correctness is checked by capability of algorithm to optimally generate highest score for the timetable, comparing the no of requirements satisfied for number of lectures, subject wise, laboratory allocations, faculty wise allocations. Fig 4: Faculty Wise TT Fig 2: Create and View TT Screen Fig 5: Course Wise TT The above figures show the correctness of the proposed algorithm. It can be seen that the algorithm produces optimized scheduled for the college. The algorithm generates and maintains highest scores as possible which guarantees optimization of requirements. Thus, it is proved that the algorithm maintains the correctness and validity of the final result and thus can be applied to all situations. 2015, IERJ All Rights Reserved Page 4

5 The project generates schedules course wise for each.it generates schedule for all the respective faculties, it generates schedule for room s, Laboratories. V. CONCLUSION The Study has presented an approach of automating timetable generation by applying a new technique like genetic algorithm. The survey commences successful implementation of genetic technique to obtain best fit optimal solution than those with earlier strategies. There is nothing to prevent this architecture from being a subset of a rich and powerful event/action language. This paper described how set of active rules can be used to express the knowledge of intelligent and how a genetic algorithm can be used to dynamically prioritize rules in the face of dynamically evolving environments. The paper illustrated the applicability of the above method by using into optimize the performance. The advantages of this approach to optimizing the solution for time table are apparent: distributed solution, load balancing and fault situations.. VI. FUTURE SCOPE The paper implements complex algorithms to generate automated time table generation. Though the authors are attempting to implement genetic algorithms to best fit the schedule to get optimized efficient results. The future work can be to create upgradable, evolutionable algorithms which would result to improve the performance. Another scope is to make a dynamic time table for the institute by applying interactions with the staff, head and students. Faculty leaves can be managed at 11 th hour to take over by another staff. By developing the architecture for mobile connectivity(android apps) where the teacher could share notes with students. By posting notifications about changes in lectures schedules, exam schedules to students. pages Springer Verlag, Berlin-Heidelberg, Germany, [6]Jean-François Cordeau, Brigitte Jaumard, Rodrigo Morales, Efficient Timetabling Solution with Tabu Search, International Timetable Competition, [7]S.Abdennadher and H. Schlenker. Nursescheduling using constraint logic programming. In B. McKay, X. Yao, C. S. Newton, J.-H. Kim, and T. Furuhashi, editors, Proceedings of the Sixteenth National Conference on Artifcial Intelligence and the Eleventh Innovative Applications of Artifcial Intelligence Conference,99, Orlando, Florida, July 1999, pages 838{843. AAAI Press/MIT Press, [8]U. Aickelin and K. A. Dowsland. An indirect genetic algorithm for a nurse scheduling problem. Computers and Operational Research, forthcoming. [9]N. Brauner, R. Echahed, G. Finke, F. Prost, and W. Serwe. Integration des methods de reecriture et de recherche operationnelle pour la modelisation et la resolution de contraintes : application a la plani_cation de personnel medical. In GISEH 003, Lyon, January [10] E. K. BURKE, P. D. CAUSMAECKER, AND G. V. BERGHE. A HYBRID TABU SEARCH ALGORITHM FOR THE NURSE ROSTERING PROBLEM. IN B. MCKAY, X. YAO, C. S. NEWTON, J.-H. KIM, AND T. FURUHASHI, EDITORS, SIMULATED EVOLUTION AND LEARNING, SECOND ASIA-PACI_C CONFERENCE ON SIMULATED EVOLUTION AND LEARNING, SEAL `98, CANBERRA, AUSTRALIA, NOVEMBER 24{ , SELECTED PAPERS, VOLUME REFRENCES [1]J. J. Grefenstette, editor. Proceedings of the First International Conference on Genetic Algorithms and their Applications.Practice and Theory of Automated Timetabling VI Proceedings of The 6th International Conference on the Practice and Theory of Automa. [2]N. R. Jennings.Coordination Techniques for Distributed Artificial Intelligence. University of London Mile End Rd.London E1 4NS UK, [3]Om Prakash Shukla, Amit Bahekar, Jaya Vijayvergiya, Effective Fault Diagnosis and Maintenance Optimization by Genetic Algorithm [4]Ender Özcan, Alpay Alkan, A Memetic Algorithm for Solving a Timetabling Problem: An Incremental Strategy, MISTA [5]Luca Di Gaspero, Andrea Schaerf, Tabu search techniques for examination timetabling. In E. Burke and W. Erben, editors, Practice and Theory of Automated Timetabling, Vol. 2079, Lecture Notes in Computer Science, 2015, IERJ All Rights Reserved Page 5