Design and Implementation of a Web-based Time-table Scheduling System in a Tertiary Institution
Chapter One
OBJECTIVES OF THE STUDY
The aim of this work is the generation of course schedules while demonstrating the possibility of building the schedules automatically through the use of computers in such a way they are optimal and complete with little or no redundancy through the development of a viable lecture and practical in timetabling software.
The primary objectives is to be able to optimize the algorithm used in today’s timetable systems to generate the best of timetabling data with fewer or no clashes.
The secondary objectives is to expand the scope of timetable automation systems by making it generic thereby bringing about uniformity in the creation of timetables as it applies to different universities, polytechnics or educational institutions i.e will be able to generate timetables that fit the requirement of any academic institution.
CHAPTER TWO
REVIEW OF RELATED LITERATURE
INTRODUCTION
A Timetable or schedule is an organized list, usually set out in tabular form, providing information about a series of arranged events: in particular, the time at which it is planned these events will take place. They are applicable to any institution where activities have to be carried out by various individuals in a specified time frame. From the time schools became organized environments, timetables have been the framework for all schools activities. As a result, schools have devoted time, energy and human capital to the implementation of nearly optimal timetables which must be able to satisfy all requirements constraints as specified by participating entities (Robertus, 2002).
The class lecture timetabling problem is a typical scheduling problem that appears to be a tedious job in every academic institute once or twice a year. The problem involves the scheduling of classes, students, teachers and rooms at a fixed number of time-slots, subject to a certain number of constraints. An effective timetable is crucial for the satisfaction of educational requirements and the efficient utilization of human and space resources, which make it an optimization problem. Traditionally, the problem is solved manually by trial and hit method, where a valid solution is not guaranteed. Even if a valid solution is found, it is likely to miss far better solutions. These uncertainties have motivated for the scientific study of the problem, and to develop an automated solution technique for it. The problem is being studied for last more than four decades, but a general solution technique for it is yet to be formulated (Datta et.al, 2006).
The automated timetabling lecture and practical and scheduling is one of the hardest problem areas already proven to be NP-Complete and it is worthy of note is that as educational institutions are challenged to grow in number and complexity, their resources and events are becoming harder to schedule, hence the choice of this project topic which entails investigating the performance of Genetic Algorithm on the optimality of timetabling problems under predefined constraints (Ossam, 2009).
REVIEW OF RELEVANT EXISTING THEORIES AND TECHNOLOGIES
Solutions to timetabling problems have been proposed since the 1980s. Research in this area is still active as there are several recent related papers in operational research and artificial intelligence journals. This indicates that there are many problems in timetabling that need to be solved in view of the availability of more powerful computing facilities and advancement of information technology (Deris et.al, 1997).
The problem was first studied by Gotlieb (1962), who formulated a class-teacher timetabling problem by considering that each lecture contained one group of students, one teacher, and any number of times which could be chosen freely. Since then the problem is being continuously studied using different methods under different conditions. Initially it was mostly applied to schools (de Gans, 1981; Tripathy, 1984). Since the problem in schools is relatively simple because of their simple class structures, classical methods, such as linear or integer programming approaches (Lawrie, 1969; Tripathy, 1984), could be used easily. However, the gradual consideration of the cases of higher secondary schools and universities, which contain different types of complicated class-structures, is increasing the complexity of the problem. As a result, classical methods have been found inadequate to handle the problem, particularly the huge number of integer and/or real variables, discrete search space and multiple objective functions.
This inadequacy of classical methods has drawn the attention of the researchers towards the heuristic-based non-classical techniques. Worth mentioning non-classical techniques that are being applied to the problem are genetic algorithms (Alberto et. al., 1992), neural network (Looi, 1992), and tabu search algorithm (Costa, 1994). However, compared to other non-classical methods, the widely used are the genetic/evolutionary algorithms (GAs/EAs). The reason might be their successful implementation in a wider range of applications. Once the objectives and constraints are defined, EAs appear to offer the ultimate free lunch scenario of good solutions by evolving without a problem solving strategy (Al-Attar, 1994). A few worth mentioning EAs, used for the school timetabling problem, are those of Abramson et. al. (1992), Piola R.(1994), and Bufe et. al. (2001). Similarly, EAs, used for the university class timetabling problem, are those of Carrasco et. al. (2001), Srinivasan et. al. (2002) and Datta et. al… Datta et. al. (2006) modeled the university class timetabling problem as a multi-objective optimization problem, considering different class-structures, such as single-slot, multi-slot, split, combined, open, and group classes. NSGA-II-UCTO, a version of EA-based multi-objective optimizer NSGA-II (Deb, 2001; Deb et. al., 2002), was also developed to handle the problem, and demonstrated successfully in two real problems. Using NSGA-II-UCTO, a number of trade-off solutions, in terms of multiple objectives of the problem, could be obtained very easily. Moreover, each of the obtained solutions has been found much better than a manually prepared solution which is in use.
CHAPTER THREE
SYSTEM ANALYSIS AND DESIGN
INTRODUCTION
This chapter deals with system analysis and design of the new system. System analysis is a structured process of collecting and analyzing facts with respect to the existing operating procedures in order to obtain a full appreciation of the situation prevailing. It is important to ensure that an effective computerized system can be designed, implemented even proved feasible. According to E.C Champion R.J, “system analysis is defined as the method of determining how best to use computer with other resources to perform tasks which meet the information needs of establishment. Before moving into the major design of the proposed system the existing system needs to be analyzed in order to identify their weaknesses.
SYSTEM ANALYSIS
It involves the analyzing and understanding a problem, then identifying alternative solutions, choosing the best course of action and then designing the chosen solution.
DETAILED DEFINITION OF THE PROBLEM
Lecture and practical Timetable allocation system is the whole process concerned with making a timetable having events arranged according to a time when they take place which must be subject to the timing constraints of each entity placed in the table. Institution timetable allocation system in this context refers to the rigorous task computer science department of the institution undergo to draw up timetables that satisfies various courses that should compulsorily be inherent in the final timetable solution.
These courses are usually taught by varied lecturers in computer science department who may also wish to specify some timing constraints on their courses. Given all the courses and course details, computer science department is charged with the responsibility of creating a near optimal timetable which would serve as a guide for academic activities in the institution.
CHAPTER FOUR
SYSTEM IMPLEMENTION
INTRODUCTION
System implementation is the process of putting into work, the newly designed and developed system. It involves the procedures taken to implant the newly developed system to the organization so as to serve its intended purposes. Here, the processes, steps, stages and requirements encountered in the process of implementing the new system are explained in details. Including the programming language used, program flowchart, maintenance details, etc.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
SUMMARY
Although this project work has come to completion, but due to the amount of capital available and difficulties on data collection which stems from the uncooperative attitude exhibition by some staff when asked questions or when inquired whether the rightful personnel in charge is available. They also feel reluctant to answer or give the researcher the correct information which was a sort of problem to the security of the following.
-Faculty
-Computer science
-Exams records and statistics department activities.
The computation of lectures and practical timetable but before this, certain basic requirement must be put into place.
- Computer main power and skilled computer personnel must be provided for successful take off of the program.
- Maintenance personnel must be made available for the effective operations of the
CONCLUSION
Having x-rayed the previous manual system with regards to my case study and with the hope that the proposed system will be implemented. I hereby conclude that the management with the assistance of the new system will facilitate fastness, sometimes in processing and publishing of computerized lectures and practical timetable.
RECOMMENDATION
With respect to the data analysis, it was discovered that the data application in processing lecture and practical timetable would improve efficiently. It is therefore recommended that the management that the management of higher institution or head of department of computer science should use this program.
REFERENCES
- Cornelissen, M.J. Sprengers and B.Mader (2010). “OPUS-College Timetable Module Design Document” Journal of Computer Science
- Abramson D. & Abela J. (1992). “A parallel genetic algorithm for solving the school timetabling problem.” In Proceedings of the 15th Australian Computer Science Conference, Hobart,.
- Adam Marczyk (2004). “Genetic Algorithms and Evolutionary Computation “.
- Al-Attar A. (1994). White Paper: “A hybrid GA-heuristic search strategy.” AI Expert, USA.
- Alberto Colorni, Marco Dorigo, Vittorio Manniezzo (1992). “A Genetic Algorithm to Solve the Timetable Problem” Journal of Computational Optimization and Applications.
- Bufe M., Fischer T., Gubbels H., Hacker C., Hasprich O., Scheibel C., Weicker K., Weicker N., Wenig M., & Wolfangel C. (2001). Automated solution of a highly constrained school timetabling problem – preliminary results. EvoWorkshops, Como-Italy.
