University Course Timetabling Research Articles

AN APPLICATION OF INTEGER LINEAR PROGRAMMING TOWARDS UNIVERSITI MALAYSIA TERENGGANU COURSE SCHEDULING PROBLEM

University Course Timetabling (UCT) problem is a combinatorial optimization problem in which a set of events has to be scheduled in timeslots and located in a suitable room by considering all the constraint involved. A feasible timetable plays an important role in ensuring the tasks or events are carried out appropriately. However, producing one is not an easy task due to its large data size and variety of requirements involved. In this research, mathematical model using Mixed Integer Linear Programming is applied for the university course timetabling problem. To validate the MILP model, data from Universiti Malaysia Terengganu (UMT) Academic Management Department is obtained. The data consists of 27 programmes, 1261 classes of 265 core courses, 59 venues and 50 timeslots. Advanced Interactive Multidimensional Modelling System (AIMMS) mathematical software with CPLEX solver is used as a tool to solve the mathematical model. The research showed that the model developed is applicable to the university course timetabling problem. An optimized solution is achieved in which fulfils the preferences of the users involved. The outcome of this research would indirectly assist the administrative staff who will be in charge in producing an effective course timetable for the university.

Timetabling Problem Solving Based on Improved Meerkat Clan Algorithm (IMCA)

The university courses timetabling problem (UCTP) is a big topic among academics and institutions since it occurs every academic year. In general, UCTP is the distribution of events across slots time for each room based on a list of restrictions provided in one semester, such as (hard constraint and soft constraint), with the objective of avoiding conflicts in such assignments. Hard constraints should never be breached when striving to satisfy as many soft constraints as possible. There are many different methods used in automating the problems of the university timetabling course in higher education institutions. This paper presents an improved algorithm for the Meerkat Clan to solve the UCTP. This is done by studying the behavior of the Meerkat Clan Algorithm and Specifying the points that are able to improve without changing the main behavior of the Meerkat Clan Algorithm. And by testing with four datasets of different sizes, good results were obtained by optimizing this algorithm. Index Terms— University Courses Timetabling Problem UCTP, Meerkat Clan Algorithm MCA, Meta-Heuristics Search.

Decomposition of university course timetabling

Suppose we like to find non-overlapping periods for a set of events which may have multiple teachers assigned, is this easy or hard in terms of complexity? Or assume that only a single teacher is fixed per event, but we like to allocate rooms and periods simultaneously. What if a single teacher and a room is already given and we look for periods alone? And how do requests of teachers for specific rooms or additional student conflicts change the computational complexities of these questions from university course timetabling (UCT)? We provide a complete hard/easy-list of all UCT subproblems derived from a typical set of hard constraints. We obtain this list with a systematic study of the fine structure of UCT in terms of complexity w.r.t. the order in which rooms, periods and teachers are assigned to events. These kind of subproblems appear in practice when some entities in a timetable are fixed while the assignments of others are (re-)computed, and they also appear as necessary conditions for the existence of feasible timetables. Moreover, we identify which of the seemingly different subproblems are essentially the same computational tasks by reducing them to the same bipartite assignment problem, and we discuss some variations of constraints.

Solving University Course Timetabling Problem Using Memetic Algorithms and Rule-based Approaches

In this paper, we present the optimization of a university course timetabling (UCT) using memetic algorithms and rule-based approaches. The method will optimize the course timetables adjustment which includes: lecturers, classrooms, time slots, and courses in one week of activity for one semester. Some hard and soft constraints are also applied to test the performance of the algorithm. The results of the experimental tests prove that memetic algorithms combined with some ruled-based approaches can be used optimally for UCT. As for the occurrence of multiple violations of soft constraints, it can be handled by manually correcting the course timetables that have been generated. From some experiments conducted also known that the system has been able to produce satisfactory course timetables, which is to adjust the distribution of timetables needs between lecturers, classrooms, time slots, and courses.

Combined Multi-Agent Method to Control Inter-Department Common Events Collision for University Courses Timetabling

Abstract University course timetabling is the scheduling of courses at different time slots in a university. The two important issues in this process are (i) the allocation of all events (professors, courses, and students) to resources (time slots daily/weekly and theory/practical classes) in a semester, and (ii) maximizing the satisfaction of common events (professors, courses, and students) among multiple departments. Accumulating evidences in university course timetabling problems suggest dividing the problem into several sub-problems. This study attempted to investigate the appropriateness of using the genetic algorithm (GA) and the imperialist competitive algorithm (ICA). The proposed technique consists of two steps: (i) using the proposed manipulated GA for solving the timetabling problem of each department, and (ii) eliminating the interference of common events among multiple departments and satisfying the hard and soft constraints by using ICA. Finally, a report on the efficiency of the methodology used in this study was obtained from the University of Tabriz in Iran and University of Udine in Italy. In this paper, the results are revealed in two ways: (i) reduction in the problems due to shrinking of the database and solving of the problems in parallel and (ii) solving the different parts of the problem by using various criterion results, increasing the common events satisfaction in that sub-problem. Eventually, the proposed model provided successful satisfaction of the hard constraints in <700 iterations with GA and elimination of interference in 40 iterations with ICA in most of the cases.

Optimal Solution Strategy for University Course Timetabling Problem

This paper describes formulations of the University Course Timetabling Problem as used at Mkwawa University College of Education. University Course Timetabling is the Problem of scheduling resources such as lectures, courses and rooms to a number of timeslots over a planning horizon, normally a week, while satisfying a number of problem-specific constraints. In this study, we have developed three models and tested using real data from the stated University. It has been possible to get optimal solution for real problem instances through reformulations of models which involve a mixture of binary and time-indexed variables. Keywords- Timetabling Problem, Combinatorial Optimization, NP-hard problem, reformulations, scheduling.

An Automatic Course Scheduling Approach Using Instructors' Preferences

University Courses Timetabling problem has been extensively researched in the last decade. Therefore, numerous approaches were proposed to solve UCT problem. This paper proposes a new approach to process a sequence of meetings between instructors, rooms, and students in predefined periods of time with satisfying a set of constraints divided in variety of types. In addition, this paper proposes new representation for courses timetabling and conflict-free for each time slot by mining instructor preferences from previous schedules to avoid undesirable times for instructors. Experiments on different real data showed the approach achieved increased satisfaction degree for each instructor and gives feasible schedule with satisfying all hard constraints in construction operation. The generated schedules have high satisfaction degrees comparing with schedules created manually. The research conducts experiments on collected data gathered from the computer science department and other related departments in Jordan University of Science and Technology- Jordan.

The Development of Mathematical Model for a University Course Timetabling Problem

This research is to study the problem of classroom timetable generating due to an increase amount ofstudent. The generator of classroom timetable needs to arrange it properly according to the limited resources ,i.e., a number of classrooms, loading capacity of classroom, periods, and number of teachers. Importantly, thegenerating of this timetable construction has to be considered together-with the proportion between capacityof classroom and number of students; otherwise it will increase expense and extra-period requiring. Aninappropriate assignment between courses to classrooms will require more classroom utilization. The expensewill increase since fee is charged and forwarded to the faculty of Engineering for classroom utilization. Inaddition inappropriate assignment will also cause requiring extra periods between 3:45 and 6:25 PM. Thisoccurs often at present and leads to ineffective learning.Consequently, the generating of timetable needs to be concerned about expense and time. This researchis to study a course timetabling problem of Silpakorn University by building up mathematical model to findout an optimal solution. The main objectives are 1) the lowest expense generating; and 2) the least extraperiodspending. The programs used for the mathematical solution in this research are IBM ILOG CPLEX12.2. The result was found that solving the classroom timetable problem using a mathematical model couldreduce the costs of 8,115 baht / week and the extra-periods required could be reduced from 10 periods to 6periods. Keywords: Classroom Timetable; Mathematical Model; Integer Linear Programming