University Timetabling Research Articles

A study on university course and exam timetabling problems and methods: an optimization survey

The objective of this paper was to retrieve the overview approaches that have been proposed and classification constraints related to previ-ous papers of timetabling problems. Optimisation and scheduling are essential problems in every type of timetabling that can be considered as a non-deterministic polynomial. The objective of this paper to investigate the course and exam timetabling problem by presented classifi-cation table of set of constraints and describes the most reliable method that has been used to solve university timetabling problem. The re-sult of study concerned the two most successfully method that widely used for optimising course and exam timetable. The contribution of this study also help to provide knowledge and idea for further surveys.

A fix-and-optimize matheuristic for university timetabling

University course timetabling covers the task of assigning rooms and time periods to courses while ensuring a minimum violation of soft constraints that define the quality of the timetable. These soft constraints can have attributes that make it difficult for mixed-integer programming solvers to find good solutions fast enough to be used in a practical setting. Therefore, metaheuristics have dominated this area despite the fact that mixed-integer programming solvers have improved tremendously over the last decade. This paper presents a matheuristic where the MIP-solver is guided to find good feasible solutions faster. This makes the matheuristic applicable in practical settings, where mixed-integer programming solvers do not perform well. To the best of our knowledge this is the first matheuristic presented for the University Course Timetabling problem. The matheuristic works as a large neighborhood search where the MIP solver is used to explore a part of the solution space in each iteration. The matheuristic uses problem specific knowledge to fix a number of variables and create smaller problems for the solver to work on, and thereby iteratively improves the solution. Thus we are able to solve very large instances and retrieve good solutions within reasonable time limits. The presented framework is easily extendable due to the flexibility of modeling with MIPs; new constraints and objectives can be added without the need to alter the algorithm itself. At the same time, the matheuristic will benefit from future improvements of MIP solvers. The matheuristic is benchmarked on instances from the literature and the 2nd International Timetabling Competition (ITC2007). Our algorithm gives better solutions than running a state-of-the-art MIP solver directly on the model, especially on larger and more constrained instances. Compared to the winner of ITC2007, the matheuristic performs better. However, the most recent state-of-the-art metaheuristics outperform the matheuristic.

Genetic Algorithm Analysis using the Graph Coloring Method for Solving the University Timetable Problem

The Timetable Problem is one of the complex problems faced in any university in the world. It is a highly-constrained combinatorial problem that seeks to find a possible scheduling for the university course offerings. There are many algorithms and approaches adopted to solve this problem, but one of the effective approaches to solve it is the use of meta-heuristics. Genetic algorithms were successfully useful to solve many optimization problems including the university Timetable Problem. In this paper, we analyse the Genetic Algorithm approach for graph colouring corresponding to the timetable problem. The GA method is implemented in java, and the improvement of the initial solution is exhibited by the results of the experiments based on the specified constraints and requirements.

COTTAPP: An Online University Timetable Application based on a Goal Programming Model

Preparing university course timetables is a challenging task as many constraints and requirements from the university and lecturers must be satisfied without overlapping courses for different student groups. Although many mathematical optimization models have been proposed to automate this task, a wider use of these models have been limited as deep technical understanding of mathematical and computer programming are required in order to use and implement them. This paper proposes a simple and flexible course timetabling application that is based on a weighted binary goal programming model with a powerful solver. Our application enables the users to modify and run this model by using a simple web and spreadsheet interface. Consequently, the model does not require deep technical understanding of the underlying models from its users even though it is based on a complex mathematical model. The web application and the underlying optimization model is illustrated by using a case study of an undergraduate program of industrial engineering.

Design and implementation of an android and web-based university timetable customization system

Timetabling is an essential component of scheduling in all sectors of education. University timetabling entails the scheduling of times and venues for lectures and examinations. These activities are logically structured in a time-wise manner and presented on papers and placed on notice boards in order to avoid conflicts of event. However, timetable schedule forgetfulness, misinterpretation, miscopying, cost of rescheduling and slow time to reach for the rescheduled timetables are the challenges posed by the use of paper and notice board-based timetables; hence, the need for automated timetables. In this paper, we discuss the implementation of Android and web-based timetable customization systems for the Faculty of Computer Science and Information Technology, Bayero University, Kano-Nigeria. The web-based system provides an administrative module that facilitates scheduling and rescheduling of lecture, examination and invigilation timetables. In addition, the Android timetable customization system permits the user to view, customize, set reminders and provide rescheduled timetable updates. Keywords : Android, Customization, Timetable and Web based

A strategic view of University timetabling

University timetabling has traditionally been studied as an operational problem where the goal is to assign lectures to rooms and timeslots and create timetables of high quality for students and teachers. Two other important decision problems arise before this can be solved: what rooms are necessary, and in which teaching periods? These decisions may have a large impact on the resulting timetables and are rarely changed or even discussed. This paper focuses on solving these two strategic problems and investigates the impact of these decisions on the quality of the resulting timetables.The relationship and differences between operational, tactical and strategic timetabling problems are reviewed. Based on the formulation of curriculum-based course timetabling and data from the Second International Timetabling Competition (ITC 2007), three new bi-objective mixed-integer models are formulated. We propose an algorithm based on the ϵ-constraint method to solve them. The algorithm can be used to analyze the impact of having different resources available on most timetabling problems. Finally, we report results on how the three objectives – rooms, teaching periods, and quality – influence one another.

An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem

The increasing fuel price has led to high operational cost and therefore, advanced optimal dispatch schemes need to be developed to reduce the operational cost while maintaining the stability of grid. This study applies an improved heuristic approach, the improved Artificial Bee Colony (IABC) to optimal power flow (OPF) problem in electric power grids. Although original ABC has provided robust solutions for a range of problems, such as the university timetabling, training neural networks and optimal distributed generation allocation, its poor exploitation often causes solutions to be trapped in local minima. Therefore, in order to adjust the exploitation and exploration of ABC, the IABC based on the orthogonal learning is proposed. Orthogonal learning is a strategy to predict the best combination of two solution vectors based on limited trials instead of exhaustive trials, and to conduct deep search in the solution space. To assess the proposed method, two fuel cost objective functions with high non-linearity and non-convexity are selected for the OPF problem. The proposed IABC is verified by IEEE-30 and 118 bus test systems. In all case studies, the IABC has shown to consistently achieve a lower cost with smaller deviation over multiple runs than other modern heuristic optimization techniques. For example, the quadratic fuel cost with valve effect found by IABC for 30 bus system is 919.567 $/hour, saving 4.2% of original cost, with 0.666 standard deviation. Therefore, IABC can efficiently generate high quality solutions to nonlinear, nonconvex and mixed integer problems.

An integer program and a hybrid genetic algorithm for the university timetabling problem

The university timetabling problem (UTP) has been studied by numerous research groups for decades. In addition to addressing hard and soft constraints, we extend the UTP by considering consecutiveness and periodicity constraints of multi-session lectures, which are common in many eastern Asian universities. Because schedulers can decide the consecutiveness and periodicity constraints for the multi-session lectures within a limited ratio, we consider these novel decision variables in our model. We develop a mixed integer linear program for the UTP. For the analysis, we convert the UTP into the three-dimensional container packing problem (3DCPP) and create a hybrid genetic algorithm (HGA), which has been shown to be efficient in solving the 3DCPP. We also develop a tabu search algorithm based on the existing UTP literature and compare the findings with that of our HGA. The results show that our HGA obtains a better solution than the tabu search algorithm in a reasonable amount of time.

An information visibility-based university timetabling for efficient use of learning spaces (IVUT)

Academic institutions have limited resources that need judicious management. Building university timetables is a complex process that considers a big number of resources. Sometimes, allocating events takes place without seeing the full picture of the available resources. This leads to inefficiencies in utilization, especially with the absence of reevaluation techniques for timetables. Spaces could be underutilized or over-utilized but kept obscure due to lack of timely information. Providing updated information about spaces occupancy and utilization is important to construct resilient and adaptive timetables. Information visibility triggers re-scheduling and reallocation decisions. This paper introduces a new approach to construct resilient university timetables using genetic algorithms and data capturing technologies. The contributed approach adds a new dimension to solving the NP-hard timetabling and space allocation problems. It enables handling dynamic attendance patterns. By conducting periodical timetable assessments, appropriate spaces can be reallocated to suit the number of attendees in each event. This helps avoiding chaotic consequences resulting from space over-occupation as well as preventing the inability to use a space under the impression that is fully utilized. The developed solution relies on the use of RFID technology to enable information visibility. A case study was conducted at the Faculty of Commerce, Alexandria University, and the results are presented. Compared to current practices, the new concept proves to be an effective tool for managing timetables and resources in universities.

Memetic Algorithm and its Application to the Arrangement of Exam Timetable

This paper looks at Memetic Algorithm for solving timetabling problems. We present a new memetic algorithm which consists of global search algorithm and local search algorithm. In the proposed method, a genetic algorithm is chosen for global search algorithm while a simulated annealing algorithm is used for local search algorithm. In particular, we could get an optimal solution through the .NET with the real data of JiangXi Normal University. Experimental results show that the proposed algorithm can solve the university exam timetabling problem efficiently.

User Requirements Model for University Timetable Management System

Automated timetables are used to schedule courses, lectures and rooms in universities by considering some constraints. Inconvenient and ineffective timetables often waste time and money. Therefore, it is important to investigate the requirements and potential needs of users. Thus, eliciting user requirements of University Timetable Management System (TMS) and their implication becomes an important process for the implementation of TMS. On this note, this paper seeks to propose a model of requirements for the University Timetable Management System (TMS). Using quantitative and qualitative methods, this model provides developers a more effective and efficient platform for developing a high quality TMS.

A Utilization-based Genetic Algorithm for Solving the University Timetabling Problem (UGA)

Building university timetables is a complex process that considers varying types of constraints and objectives from one institution to another. The problem solved in this paper is a real one featuring a number of hard and soft constraints that are not very conventional. The pursued objective is also novel and considers maximizing resource utilization. This paper introduces a genetic algorithm that uses some heuristics to generate an initial population of feasible good quality timetables. The algorithm uses a simple weighted sum formula to respect professors’ preferences and handle conflicts. In order to reduce waste, a crossover type focusing on the utilization rates of learning spaces is introduced. A targeted mutation operator that uses a local search heuristic is also employed. The algorithm applies a composite fitness function that considers space utilization, gaps between events and a maximum number of lectures per day. A large dataset with real data from the Faculty of Commerce, Alexandria University in Egypt was used to test the contributed algorithm. The algorithm was also tested against two difficult benchmark problems from the literature. Testing proved that the developed algorithm is an effective tool for managing timetables and resources in universities. It performed remarkedly well on the large datasets of the two benchmark problems and it also respected more constraints than those stated in the initial problem statement of the two benchmark datasets.

TTGELS: a new approach for solving university exam timetabling problem by using gravitational emulation local search algorithm

University exam timetabling problem is one of the NP-hard problems that challenge universities all over the world. Thus, different methods have been proposed to solve this problem by using heuristic algorithm. In this paper, a new method named TTGELS has been proposed to solve the university exam timetabling problem by using gravitational emulation local search algorithm. The proposed method is compared with other methods and results indicate that the proposed method could produce better results but it needs more time to execute, that's because of using neighbourhood production process and multiple stepping method to produce better solutions.

University Timetable Generator Using Tabu Search

This report is based on University Timetable Generator by using Tabu Search algorithm. It helps to generate a course schedule and an exam schedule for a University. Every university faces a different set of problem while preparing course schedule and exam schedule. There are lots of constraints while making a scheduler. And for this reason, students suffer much as well as faculties. This report is based on discussion about an automated timetable generator for a University by using Tabu Search algorithm. Tabu Search is a meta-heuristic procedure for solving optimization problems. Tabu Search deals with a sub-optimal initial solution. By analyzing the search space and averts inessential exploration, it optimists this solution and keeps the list of recently visited area in a Tabu list. This helps to solve these problems within a reasonable time and gives a feasible solution than any manual system. For a University, we have found that preparing exam schedule, course schedule, student assessment, room assignment with required resources are quite complex. But for all of them, we analyze that the Tabu Search technique is an essential method for getting a feasible solution. In this paper, we describe how Tabu Search works and how to get a feasible solution by using this algorithm.

A multi-stage IP-based heuristic for class timetabling and trainer rostering

We consider a timetabling and rostering problem involving periodic retraining of large numbers of employees at an Australian electricity distributor. This problem is different from traditional high school and university timetabling problems studied in the literature in several aspects. We propose a three-stage heuristic consisting of timetable generation, timetable improvement, and trainer rostering. Large-scale integer linear programming models for both the timetabling and the rostering components are proposed, and several unique operational constraints are discussed. We show that this solution approach is able to produce good solutions in practically acceptable time.

A Hybrid Swarm-Based Approach to University Timetabling

This paper is concerned with the application of an automated hybrid approach in addressing the university timetabling problem. The approach described is based on the nature-inspired artificial bee colony (ABC) algorithm. An ABC algorithm is a biologically-inspired optimization approach, which has been widely implemented in solving a range of optimization problems in recent years such as job shop scheduling and machine timetabling problems. Although the approach has proven to be robust across a range of problems, it is acknowledged within the literature that there currently exist a number of inefficiencies regarding the exploration and exploitation abilities. These inefficiencies can often lead to a slow convergence speed within the search process. Hence, this paper introduces a variant of the algorithm which utilizes a global best model inspired from particle swarm optimization to enhance the global exploration ability while hybridizing with the great deluge (GD) algorithm in order to improve the local exploitation ability. Using this approach, an effective balance between exploration and exploitation is attained. In addition, a traditional local search approach is incorporated within the GD algorithm with the aim of further enhancing the performance of the overall hybrid method. To evaluate the performance of the proposed approach, two diverse university timetabling datasets are investigated, i.e., Carter’s examination timetabling and Socha course timetabling datasets. It should be noted that both problems have differing complexity and different solution landscapes. Experimental results demonstrate that the proposed method is capable of producing high quality solutions across both these benchmark problems, showing a good degree of generality in the approach. Moreover, the proposed method produces best results on some instances as compared with other approaches presented in the literature.

Hybrid meta-heuristics for combinatorial optimization

Combinatorial optimization problems arise, in many forms, in various aspects of everyday life. Nowadays, a lot of services are driven by optimization algorithms, enabling us to make the best use of the available resources while guaranteeing a level of service. Examples of such services are public transportation, goods delivery, university time-tabling, and patient scheduling. The fields of meta-heuristics, artificial intelligence, and operations research, have been tackling many of these problems for years without much interaction. However, in the last few years, such communities have started looking at each other’s advancements, in order to develop optimization techniques that are faster, more robust, and easier to maintain. This effort gave birth to the fertile field of hybrid meta-heuristics. In this thesis, we analyze some of the most common hybrid meta-heuristics approaches, and show how these can be used to solve hard real-world combinatorial optimization problems, and what are the major advantages of using such techniques. This thesis is based on results obtained by working together with many local and international researchers, and published in a number of peer-reviewed papers. School: University of Udine Supervisors: Luca Di Gaspero Graduated: Friday, April 4, 2014 Link to full text: http://www.a4cp.org/sites/default/files/tommaso_urli_-_hybrid_metaheuristics_for_combinatorial_optimisation.pdf Constraints (2015) 20:473 DOI 10.1007/s10601-015-9209-7 * Tommaso Urli Tommaso.Urli@nicta.com.au 1 NICTA, Canberra, Australia

A MILP model for the teacher assignment problem considering teachers’ preferences

The Teacher Assignment Problem is part of the University Timetabling Problem and involves assigning teachers to courses, taking their preferences into consideration. This is a complex problem, usually solved by means of heuristic algorithms. In this paper a Mixed Integer Linear Programing model is developed to balance teachers’ teaching load (first optimization criterion), while maximizing teachers’ preferences for courses according to their category (second optimization criterion). The model is used to solve the teachers-courses assignment in the Department of Management at the School of Industrial Engineering of Barcelona, in the Universitat Politècnica de Catalunya. Results are discussed regarding the importance given to the optimization criteria. Moreover, to test the model's performance a computational experiment is carried out using randomly generated instances based on real patterns. Results show that the model is proven to be suitable for many situations (number of teachers-courses and weight of the criteria), being useful for departments with similar requests.

Iterative optimization techniques using man-machine interaction for university timetabling problems.

We focus on a timetabling problem of university makeup classes and construct a scheduling system based on man–machine interaction which enables to reveal the essential and additional information of the problem domain. In this problem, makeup classes which are requested by the lecturers have to be scheduled to a specified time slot under the hard/soft constraints, e.g., schedules of the lecturers and the students. A constraint based scheduling model is newly introduced and several parameters of the model are settled through the repetition of the solution evaluation by the operators. Through the numerical experiment with the actual data, the potential of the proposed approach is examined.

Teaching the Feldenkrais Method® in UK higher education performer training

In recent years, there has been a growing interest within performer training institutions in using the Feldenkrais Method® when developing actors and dancers. However, introducing this type of experiential reflective practice into a university timetable is not without its challenges. This article discusses the practicalities of adapting the Feldenkrais Method® to a HE curriculum and examines the use and benefits of the Method for emerging performers and practitioners. Particular reference is made to recorded responses of theatre students who have experienced regular Feldenkrais Method classes as part of their undergraduate programme at University Centre Doncaster and the perceived effects this type of work has had not only in performance but also on their general personal development. This article further explores the culture of acceptance and exploration that the Method promotes as an aid to creativity. By challenging habitual practice and thinking, can we open up new approaches to problem solving within the wider area of learning?