A survey of approaches for university course timetabling problem

This paper describes the effects of pre-processors on the solution quality of the university course timetabling problem. The University Course Timetabling Problem (UCTP) is regarded as both an NP-hard and NP-complete combinatorial optimization problem and is a difficult task since many constraints are needed to be satisfied in order to obtain a feasible solution. The constraints, in this case, are related to the characteristics and regulations of the particular institution. This paper describes the effects of pre-processors which consist of four matrices on the solution quality of the university course timetabling problem as they play a critical role and have significant impact on the solution. In order to evaluate the effects of the pre-processor matrices, the fitness score which is obtained without the pre-processor matrices are tabulated and compared against the fitness score that is obtained with the pre-processor matrices. Seven neighborhood structures are implemented and the experiment is conducted using the datasets obtained from Track 3 of the ITC-2007. The result indicates that there is a significant difference in terms of the fitness score between the solutions obtained without the pre-processors and with the pre-processors, with the latter showing promising results.

A multi-dimensional assignment problem in which Students, Faculty and Subjects together as events are scheduled to suitable timeslots and available classrooms, is termed to be University Course Timetabling Problem (UCTP). Previous literature studies reveal that getting the best fitness function value with generating an optimized timetable is not considered to a great extent. This paper proposes a novel method of solving the UCTP through various Hybrid Search Optimization algorithms combined with Particle Swarm Optimization (PSO). This approach is implemented for the real time data taken from the Faculty of Computer Science and Engineering of a typical Indian University. Our experimentation clearly confirms by giving promising results that, getting a near or sub optimal solution is highly feasible through various hybrid algorithms.

The University Course Timetabling Problem (UCTP) is a search problem that allocates a given number of rooms with given courses based on their scheduled time slots. UCTP belongs to the NP-complete class and can be defined as a constraint satisfaction problem (CSP). To solve the performance issue of CSP solvers, there are various local search methods. CSP solvers often use variable and value ordering heuristics to improve their search performance. Specific variable and value ordering heuristics can be even calculated by the help of a learning algorithm. Cluster-Specific Heuristics (CSH) are variable ordering heuristics which are learned based on clusters of CSPs. In this paper, to solve UCTP with user requirements, we propose a better performing CSH which is called Optimized Cluster-Specific Heuristics (OCSH). We have tested OCSH on generated time tabling problems with various user requirements and compared the runtime performances of variations of cluster-specific heuristics with OCSH. Finally, we show that OCSH is the best performing version of cluster specific heuristics to solve UCTP with user requirements.

Scheduling and planning are considered difficult combinatorial optimisation problems and belong to the NP-complete class. The university course timetabling problem (UCTP) is one such type of problem which is to be solved under various constraints. The purpose of this paper is to create a university course timetable generator to automate the university course scheduling process, which is one of the most challenging and cumbersome tasks for the administration. All modules of the proposed timetable generator are inter-linked in SQL database and implemented in C# programming on the .NET platform. The proposed university course timetable generator is tested on several randomly generated problem instances of various complexities to verify the efficiency. The outcomes of them signify the importance of the solver, as it is producing a conflict-free timetable by taking very less amount of time and primary memory.

As an important administrative task in the area of education, course timetabling is a complex optimization problem that is difficult to solve by conventional methods. The paper adapts a new nature-inspired metaheuristic called ecogeography-based optimization (EBO), which enhances biogeography-based optimization by equipping the population with a neighborhood structure and designing two new migration operators named global migration and local migration, to solve the university course timetabling problem (UCTP). In particular, we develop two discrete migration operators for efficiently evolving UCTP solutions based on the principle of global and local migration in EBO, and design a repair process for effectively coping with infeasible timetables. We test the discrete EBO algorithm on a set of problem instances from four universities in China, and the experimental results show that the proposed method exhibits a promising performance advantage over a number of state-of-the-art methods.

The article presents the problem of academic teachers' competences configuration in the context of the university course timetabling problem (UCTP). Usually when solving UCTP, the set of available academic teachers and the competences they have is defined. The sets of lecture rooms, subjects (courses), student groups, time-slots, etc. are also known. Problems can emerge when it is not possible to find a satisfactory UTCP solution due to the missing sufficient number of specific academic teachers' (lecturers') competences, which is reflected in the possibility to teach specific classes (courses). In order to detect early such a situation and effectively manage the available and required lecturers' competences, a mathematical model of lecturers' competences configuration has been formulated in the form of a MILP (Mixed Integer Linear Programming) problem. Its solution has a direct impact on UTCP. The article also presents the implementation of the model in the LINGO solver environment and computational experiments.

Scheduling educational institution courses for multiple departments presents a significant challenge due to the complexity of constraints and requirements. This paper addresses the University Course Scheduling Problem (UCSP) using a binary Integer Linear Programming (ILP) approach to generate semester schedules for winter or summer semesters. The approach incorporates both hard constraints - such as avoiding simultaneous sessions for the same semester, adhering to room capacities, and ensuring lecturers are not doublebooked - and soft constraints, including preferences for lecture times. While the whole methodology integrates tools for data management, optimization modeling, and a structured workflow to create detailed schedules for lecturers, students, and room allocations, this paper focuses on the ILP-formulation of these constraints. The construction of these matrices is systematically explained through practical examples, illustrating how the relationships between courses, lecturers, rooms, and time slots are encoded. To validate the proposed approach, a case study was conducted at the Niederrhein University of Applied Sciences. This study addresses the scheduling requirements of three engineering departments with more than 50 degree programs and 260 modules, demonstrating the applicability of the matrixdriven ILP method.

University course timetabling problem (UCTP) is important issue forever. Scheduling problems are considered and proved NP-Complete by different researchers. It is due to continuous changing of constraints to be fulfilled on rapidly changing data. Different hybrid state of art techniques and their use for university course timetable problem is investigated in this study. This paper also analyze occurrence of constraints and there ratio of similarity in recent research trend on university course timetabling problem. Constraints highly vary from department to department. This level of variance of constraints makes timetabling problem difficult to solve and NP-complete. In recent years, concept of hybridization of different methods increased. This study analyzed use of hybrid heuristic and meta-heuristic methods for university course timetabling problem. In this study, we categorize this hybridization into two main categories: local search hybridization with local search based approaches and population based hybridization with local search based approaches. It is observed that Population based methods (Genetic Algorithm (GA), Particle Swarm optimization (PSO) and Artificial Bee colony (ABC) etc.) are preferred in combination with local search (LS) based methods for university timetabling problem. Concept of hybridization of population based methods with local and other population based methods is adopted to eliminate demerits of both methods. Although, hybrid methods are difficult and also require more computational cost. Still hybridization is useful for finding better solutions.

In this work, we propose a Genetic Algorithm (GA) in addressing a post enrolment course timetabling (PE-CTT) problem for University Malaysia Sabah-Labuan International Campus (UMS-LIC). Tabu Search with Sampling and Perturbation (TSSP) is used to initiate a pool of feasible solutions in the GA. Experiments are conducted to set the best parameter values for the algorithm to operate optimally under a computation time limit. The proposed methodology is tested on a dataset based on semester 1, session 2018/2019 student registration. The automated timetables are compared with the one generated manually by the administrative staff of UMS-LIC. The former outperformed the latter in terms of hard and soft constraint violations (approximately 54% improvement). Experimental results are discussed.

Course scheduling is an optimization problem impacted by various constraints. In order to devise a course scheduling solution, the course structure should be modelled in terms of certain parameters like time, rooms, courses, instructors, etc. Several approaches and solutions exist for any given formulation of the course scheduling problem. Initially, the objective function along with hard and soft constraints were identified. A solution was obtained considering the accomplishment of hard constraints, which are mandatory to be accomplished. Further, the solution could be enhanced further by suitably handling various soft constraints, which are optional. Linear Programming approach used here is divided into two stages such as (1) Weekly lecture allocation and (2) Creating schedule so that further modifications could be easily addressed within a specific stage. Study reveals varying the number of days per week, number of periods per day and the number of medium rooms resulted in 37 to 44 solutions within a time frame of 0.57 to 0.86 s. Also, the utilization of the rooms largely varied between 60% to 80% at different time periods of the day. Room utilization reached more than 90% only when the number of medium rooms or the number of days is reduced.

We present an integer programming approach to the university course timetabling problem, in which weekly lectures have to be scheduled and assigned to rooms. Students’ curricula impose restrictions as to which courses may not be scheduled in parallel. Besides some hard constraints (no two courses in the same room at the same time, etc.), there are several soft constraints in practice which give a convenient structure to timetables; these should be met as well as possible.

Scheduling is an NP-hard problem which most universities are grappling with. For each academic semester, the timetabling process must be carried out regularly, which is an overwhelming and time-consuming activity. The main contribution of this study is developing an automated system based on a multi-agent (MA) approach and genetic algorithms (GA) to generate a university timetable. Three agents named capture agent (CA), processing agent (PA) and distributing agent (DA) have been worked collaboratively and cooperatively to develop the university timetable. The study has been applied in a real case study to perform the course schedules in the electrical and electronic engineering department of our university. The system implemented has considerably reduced the time and effort in the timetables realisation of our department from about ten days to only a few minutes. It has also significantly improved the quality of timetable by guaranteeing a satisfaction rate of over 95% of the constraints.

The current study presents Integer Programming (IP) search methodology approaches for solving Curriculum-Based University Course Timetabling problem (CB-UCT) on real-life problem instances. The problem is applied in University Malaysia Sabah, Labuan International Campus Labuan (UMSLIC). This research involves implementing pure 0-1 IP and further incorporates IP into a distributed Multi-Agent System (MAS) in which a central agent coordinates various cooperative IP agents by sharing the best part of the solutions and direct the IP agents towards more promising search space and hence improve a common global list of the solutions. The objectives are to find applicable solutions and compare the performance of sequential and cooperative IP search methodology implementations for solving real-life CB-UCT in UMSLIC. The results demonstrate both sequential and parallel implementation search methodologies are able to generate and improve the solutions impressively, however, the results clearly show that cooperative search that combines the strength of integer programming outperforms the performance of a standalone counterpart in UMSLIC instances.

University course timetabling is a NP-hard problem which is very difficult to solve by conventional methods. A highly constrained combinatorial problem, like the timetable, can be solved by evolutionary methods. In this paper, among the evolutionary computation (EC) algorithms, a genetic algorithm (GA) for solving university course timetabling problems is applied. Main goal is to minimize the number of conflicts in the timetable. For this purpose two approaches -Modified GA and Cooperative GA- are applied. Results show the modified GA (MGA) method was significantly enhanced algorithm performance with modified basic genetic operators. Intelligent operators improve overall algorithm’s behavior. In addition, algorithm performance is considerably improved by using cooperative genetic method.

In this paper, as a machine learning or system modeling, a novel genetic algorithm (GA) approach for solving university course timetabling problem is presented. The designed timetabling is free of any hard constraint violations and satisfies most of the soft constraints as much as realistically possible. When compared with other methods, the followings were the advantages: 1) the number of soft constraint violations is less, 2) the use of external rooms is eliminated, and 3) the number of instructors' overload is less.