Computerised Faculty Course Timetabling with Student Sectioning

This research focuses on a faculty course timetabling at the Faculty of Computer Science and Information Technology (FCSlT), Universiti Malaysia Sarawak (UNIMAS). In this case study, course pre-registration is not a practice. Therefore, there is no precise estimation on course registration and causes faculty's experienced planners to arrange the timetable by curriculum-based. Curriculum-based timetable will create a lot of changes after the semester has started, where "repeaters" students tend to face clashes in their course timetable. Besides, students are increasing consistently from semester to semester although the number of venue resources remains the same. Due to all these issues, the objective of this study is to develop a computerised aIgoritrull to minimise the clashes issue and increase venue utilisation. A two-stage heuristic method is proposed to solve the faculty course timetabJing problem by student-based. Data pre-processing algorithm was carried out to predict course registration based on students' curriculum course plan and previous examination result. "Repeaters" are taken into account in order to solve the problem comprehensively. The two-stage heuristic method divided courses into course groups in first stage to ease the timeslot-venue allocation in the second stage. Student sectioning was also considered to solve venue inadequacy by dividing large size courses into several course sections. Different course sections will have lecture in different venues either during the same or different timeslots. The simulator was tested with three real semesters' data from FCSIT. Three data sets were varied in problem size. The computational time of the simulator ranging from less than minute to about 10 minutes depend on the problem instances. All the timetable solutions generated by the simulator are no-clash solution with minimum unallocated courses. In ten11 of venue utilisation, two-stage heuristic solution manages to allocate exactly with the demand up to 98% but real solution can perfon11 best at only 75%. For timetable distribution, statistic graphs based on student clusters have been plotted on number of lecture days per week, number of lecture hours in a timetable day and number of highest continuous lecture hours in a timetable day. On average, 86% timetables have more than three lecture days per week, more than 93% of timetable days have acceptable range of 5 lecture hours and more than 90% of timetable days have less than 5 highest continuous hours. Moreover, two-stage heuristic timetabling also provides the convenience in editing the data attached to the problem and regenerate solutions in seconds. The computational result demonstrates that proposed heul;stic algorithm outperfoJ111 current practices in all datasets. A sets of sensitivity analyses have been conducted with different 'scenario of student size, number of timeslots and number of venues. All the sensitivity analyses results demonstrate that the proposed solution is effective and robust in solving FCSIT course timetabling problem for different scenario

Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

A memetic algorithm for the university course timetabling problem

This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size. This process involves lots of constraints that must be respected and a huge search space to be explored, even if the size of the problem input is not significantly large. On the other hand, the problem itself does not have a widely approved definition, since different institutions face different variations of the problem. This paper presents a memetic algorithm that integrates two local search methods into the genetic algorithm for solving the university course timetabling problem (UCTP). These two local search methods use their exploitive search ability to improve the explorative search ability of genetic algorithms. The experimental results indicate that the proposed memetic algorithm is efficient for solving the UCTP

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Copyright @ Springer Science + Business Media. All rights reserved.The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe 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 NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used 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 are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

Exam Timetabling Using Graph Colouring Approach

Nowadays, many methods exist for the scheduling problem but it only perform well in particular isolated environments and cannot cope with the changing requirements of large educational institutions. At this time, exam timetabling at Foundation Studies and Extension Education (FOSEE), MMU has been departmentally led and the construction of the timetables is done manually. The purpose of this research is to investigate the current exam timetable system in order to understand the current processes and problems involved during preparing the timetables and to propose a cluster heuristic and graph colouring heuristic approach to solve exam timetabling problem in FOSEE, MMU. Semi-structured interview and literature review are the method that used for data gathering. Semi-structured interview help in collecting data and information about the current system and the problem faces by the user. While literature review help in search and analyze the best approach that can help to solve the problem in the exam timetable including the cluster heuristic, sequential heuristic, cased-based approach, meta-heuristic, integer programming approach, knowledge base approach and graph colouring. This study presents a solution method for exam timetable problem in FOSEE, MMU. The method of solution is a heuristic approach that include graph colouring, cluster heuristic and sequential heuristic

Automated university lecture timetable using Heuristic Approach

There are different approaches used in automating course timetabling problem in tertiary institution. This paper present a combination of genetic algorithm (GA) and simulated annealing (SA) to have a heuristic approach (HA) for solving course timetabling problem in Federal University Wukari (FUW). The heuristic approach was implemented considering the soft and hard constraints and the survival for the fittest. The period and space complexity was observed. This helps in matching the number of rooms with the number of courses. Keywords: Heuristic approach (HA), Genetic algorithm (GA), Course Timetabling, Space Complexity

Heuristic Algorithm for Multi-Location Lecture Timetabling

This paper studies a real faculty timetabling problem with multi-location consideration. Faculty of Cognitive Sciences and Human Development, UNIMAS offers a master's program by coursework to postgraduate students. The construction of timetables for all the courses offered is a tedious process due to constraints such as team-teaching allocation, unavailability dates of lecturers, and multi-location considerations. Therefore, a manually designed timetable is not as practical as it is time consuming when operational constraints must be fulfilled. In this paper, a two-stage heuristic algorithm is proposed to solve this postgraduate coursework timetable problem. This is because the heuristic algorithm is easy to apply and able to generate a feasible solution in a short time. The proposed two-stage heuristic algorithm consists of Lecturer Grouping Stage and Group Allocation Stage. In Stage I, the lecturers are assigned into four lecturer groups with the condition of no identical lecturers in each of the groups. Then, in Stage II, these groups are allocated into a set of academic weeks throughout the semester. The timeslot for each course can be allocated, and the team-teaching slot for the lecturers can be assigned in this stage. The result from the two-stage heuristic algorithm shows remarkable improvement over the real timetables solution by analyzing the distribution of lecture sessions of the courses

DEM Timetabling Project ? Development/implementation of an algorithm to support the creation of timetables

This work presents the development of an algorithm to support the process of creating academic timetables, specifically aimed at solving the University Course Timetabling Problem. To date, this problem is solved manually in Instituto Superior de Engenharia do Porto, where professors and engineers face the complex task of creating timetables based on schedules from previous years. The proposed solution aimed to support the process of creating timetables at ISEP, reducing the time and human resources required for this task. The developed algorithm uses an integer programming approach and can consider a variety of constraints and preferences of both faculty and students. It was designed to adapt and optimize the timetable creation process as needs evolve, ensuring future demands can be easily accommodated. The algorithm implementation was based on the Python programming language and the Pyomo library, offering a flexible and efficient approach to optimizing resource allocation. Additionally, the system is designed to import data from real-world sources, simplifying the integration of crucial information. The result assigned all the 128 one-hour classes among the week, presenting the faculty member, the classroom assigned and the type of class according to each course. This research presents feasible solutions that need improvement on the demanding conditions and restrictions imposed by ISEP. The computational results obtained offered a significantly decrease in the time resource used, compared to the manual work previously done

Cyclic transfers in school timetabling

In this paper we propose a neighbourhood structure based on sequential/cyclic moves and a cyclic transfer algorithm for the high school timetabling problem. This method enables execution of complex moves for improving an existing solution, while dealing with the challenge of exploring the neighbourhood efficiently. An improvement graph is used in which certain negative cycles correspond to the neighbours; these cycles are explored using a recursive method. We address the problem of applying large neighbourhood structure methods on problems where the cost function is not exactly the sum of independent cost functions, as it is in the set partitioning problem. For computational experiments we use four real world data sets for high school timetabling in the Netherlands and England.We present results of the cyclic transfer algorithm with different settings on these data sets. The costs decrease by 8–28% if we use the cyclic transfers for local optimization compared to our initial solutions. The quality of the best initial solutions are comparable to the solutions found in practice by timetablers