University Course Timetabling Problem Research Articles

A collaborative approach for solving the university course timetabling problem

This work proposes a collaborative approach for solving the university course timetabling problem (UCTP). A prototype was developed and used for a computer sciencecourse at the Federal Fluminense University in Brazil. The main idea is that students, professors, and course coordinators contribute collaboratively to course timetabling through an app. These contributions employ heuristics, which is responsible for timetabling to improve the solution to the problem. Results and future works are described herein.

Solving a University Course Timetabling Problem Based on AACSB Policies

The purpose of this research is to solve the university course timetabling problem (UCTP) that consists of designing a schedule of the courses to be offered in one academic period based on students’ demand, faculty composition and institutional constraints considering the policies established in the standards of the Association to Advance Collegiate Schools of Business (AACSB) accreditation. These standards involve faculty assignment with high level credentials that have to be fulfilled for business schools on the road to seek recognition and differentiation while providing exceptional learning. A new mathematical model for UCTP is proposed. The model allows the course-section-professor-time slot to be assigned for an academic department strategically using the faculty workload, course overload, and the fulfillment of the AACSB criteria. Further, the courses that will require new hires are classified according to the faculty qualifications stablished by AACSB. A real-world case is described and solved to show the efficiency of the proposed model. An analysis of different strategies derived from institutional policies that impacts the resulting timetabling is also presented. The results show the course overload could be a valuable strategy for helping mitigate the total of new hires needed. The proposed model allows to create the course at the same time the AACSB standards are met.

University Course Timetabling Problem with Professor Assignment

One of the decision problems in many organizations and institutions is to decide how to schedule different tasks, in particular, in higher education institutions. One of the main problems is the university course timetabling problem (UCTP): this problem consists of the allocation of events (courses, professors, and students) to a number of fixed time slots and rooms, this at the beginning of each academic period of the universities. The existent formulations include particular requirements from different educational levels and institutions, as in our case. In this paper, we focus on the university course timetabling problem with the assignment of professor-course-time slot for an institution in Mexico. Timetabling is constructed for the disciplinary courses that are offered by one of the academic departments. The main characteristics are as follows: (1) there are full-time and part-time professors; (2) a mandatory fixed number of courses has to be assigned to each full-time professor according to their academic profile; (3) there is a maximum number of courses assigned to part-time professors; (4) a professor-course matrix that specifies the valid assignation is defined; and (5) mandatory time periods for courses in different semesters are established and other traditional constraints. We present the integer linear programming model proposed to solve the case studied. The optimal solution was obtained with low computational effort through the classical branch-and-bound algorithm. We describe the complete timetable to show the model effectiveness.

A Survey of University Course Timetabling Problem: Perspectives, Trends and Opportunities

The timetabling problem is common to academic institutions such as schools, colleges or universities. It is a very hard combinatorial optimisation problem which attracts the interest of many researchers. The university course timetabling problem (UCTTP) is difficult to address due to the size of the problem and several challenging hard and soft constraints. Over the years, various methodologies were proposed to solve UCTTP. The purpose of this survey paper is to provide the most recent scientific review of the methodologies applied to UCTTP. The paper unveils a classification of methodologies proposed in recent years based on chronology and datasets used. Perspectives, trends, challenges and opportunities in UCTTP are also presented. It is observed that meta-heuristic approaches are popular among researchers. This is followed closely by hybrid methodologies. Hyper-heuristic approaches are also able to produce effective results. Another observation is that the state-of-art methodologies in the scientific literature are not fully utilised in a real-world environment perhaps due to the limited flexibility of these methodologies.

A Mathematical Model for Course Timetabling Problem With Faculty-Course Assignment Constraints

University course timetabling problem (UCTP) includes the challenging task of generating an automated timetable for courses under resource limitations. Manual generated timetables might hold some errors and consume a long time to create feasible solutions. Thus, there is a need to find optimal and fast solutions for this problem. The difficulty of the timetabling problem is further increased when tackling faculty-related constraints, according to their requirements, preferences, and availability. Thus, student-related constraints are usually the focus of UCTP generated solutions, in which faculty constraints are limited to their teaching load or preferences. This paper proposes a multi-objective mixed-integer programming model for a preregistration UCTP, combined with faculty-related constraints. The goal is to maximize events assignments and faculty-members preferences satisfaction while balancing the university requirements. At the same time, student learning days and unassigned events are minimized. The model is tested with eight real-world instances. Computational experiments are carried out to show the efficiency of the model. The proposed method can generate optimal timetables for all problem instances that satisfy faculty constraints.

A hybrid algorithm for the university course timetabling problem using the improved parallel genetic algorithm and local search

Scheduling is one of the problems that has attracted the attention of many researchers over the years. The University Course Timetabling Problem (UCTP) is a highly constrained real-world combinatorial optimization task. Designing course timetables for academic institutions has always been challenging, because it is a non-deterministic polynomial-time hardness (NP-hard) problem. This problem attempts to assign specific timeslots and rooms to the events considering a number of hard and soft constraints. All hard constraints must be satisfied to achieve a feasible solution, whereas satisfying all soft constraints is not necessary. Although the quality of the solution is directly related to the number of soft constraints that are satisfied. One of the recent innovative methodologies for solving UCTP is the hybrid algorithm, which attempts to automate the timetabling design process so that it would be able to work with different instances of problem domains. In this paper, we present a hybrid method based on the Improved Parallel Genetic Algorithm and Local Search (IPGALS) to solve the course timetabling problem. The Local Search (LS) approach is used to strengthen the Genetic Algorithm (GA). The IPGALS has applied a representation of the timetable, which ensure the hard constraints would never be violated. Hard constraints are measured by Distance to Feasibility (DF) criterion. In fact, applying the DF criterion leads to achieving feasible solutions and promotes the performance of our algorithm. Due to the wide range of problem constraints, the proposed algorithm is performed in parallel to improve the GA searching process. The IPGALS algorithm is tested over BenPaechter and ITC-2007 standard benchmarks and compared with the state-of-the-art techniques in this literature. The experimental results confirm the effectiveness and the superiority of the proposed algorithm compared to other prominent methods for solving UCTP.

Performance improvement strategies on Cuckoo Search algorithms for solving the university course timetabling problem

The university course timetabling problem (UCTP) arises every academic year and must be solved by academic staff with/without a course timetabling tool. A Hybrid Self-adaptive Cuckoo Search-based Timetabling (HSCST) tool has been developed for minimising the total university operating costs. The HSCST tool was applied to solve eleven problem instances obtained from the Faculty of Engineering, Naresuan University. The performance improvements of the Cuckoo Search (CS) algorithm embedded within the proposed tool were demonstrated using three strategies: parameter setting approaches (static and adaptive), movement strategies (Lévy flights and Gaussian random walks), and local search hybridisation techniques. Sequential computational experiments were designed and conducted to investigate the efficiency of the three proposed strategies. The statistical analysis on the computational results suggested that the proposed algorithms significantly outperformed the conventional CS, Particle Swarm Optimisation (PSO), and hybrid PSO for all problem instances.

A Tabu search algorithm with controlled randomization for constructing feasible university course timetables

University Course Timetabling Problem (UCTP) is one of the most studied timetabling problems. In this work, the problem of finding a feasible solution for the UCTP, i.e., a solution that satisfies all hard constraints, is addressed. To cater for the algorithm design, the problem is firstly reformulated into a version where only one hard constraint needs to be considered in the minimization procedure. Then a novel Tabu search algorithm is proposed, which integrates the controlled randomization strategy and threshold mechanism into the original Tabu search framework. Experiments on a set of 60 well-known Lewis benchmark instances show that the proposed algorithm achieves competitive results compared with eight reference algorithms. In particular, when the stop condition is relaxed, the proposed algorithm can find feasible solutions for all the 60 instances, two of which are missed in previous study.

Solving University Course Timetabling Problem Using Multi-Depth Genetic Algorithm

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. The constraints could be hard (encouraged to be satisfied) or soft (better to be fulfilled). This problem becomes complicated for universities which have an immense number of students and lecturers. Moreover, several universities are implementing student sectioning which is a problem of assigning students to classes of a subject while respecting individual student requests along with additional constraints. Such implementation enables students to choose a set of preference classes first then the system will create a timetable depend on their preferences. Subsequently, student sectioning significantly increases the problem complexity. As a result, the number of search spaces grows hugely multiplied by the expansion of students, other variables, and involvement of their constraints. However, current and generic solvers failed to meet scalability requirement for student sectioning UCTP. In this paper, we introduce the Multi-Depth Genetic Algorithm (MDGA) to solve student sectioning UCTP. MDGA uses the multiple stages of GA computation including multi-level mutation and multi-depth constraint consideration. Our research shows that MDGA could produce a feasible timetable for student sectioning problem and get better results than previous works and current UCTP solver. Furthermore, our experiment also shows that MDGA could compete with other UCTP solvers albeit not the best one for the ITC-2007 benchmark dataset.

Automated university course timetable generator

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.

HYBRID VNS-TS HEURISTICS FOR UNIVERSITY COURSE TIMETABLING PROBLEM

Goal: Propose hybrid heuristics combining VNS and Tabu Search (TS) for adding adaptability to University Course Timetabling Problem (UCTTP) resolution.
 Design / Methodology / Approach: VNS and TS metaheuristics were used isolated and in combination in order to verify which shape acquired the best solutions. Those heuristics were verified using constraints found at two undergraduate courses of Federal Fluminense University. Effectiveness is verified by comparing them with solutions manually elaborated by undergraduate course coordinators.
 Results: The computational results showed the efficiency of the hybrid VNS-TS heuristics, with emphasis on the heuristic VNS-TS1, which uses tabu search as local search method.
 Practical implications: The hybrid heuristic VNS-TS1 has been in use since the first half of 2019 by some of UFF undergraduate courses coordinations. Even though each university has specific constraints on its timetable, the use of the proposed heuristics is possible because it has been developed using the framework FINESS that allows for the easy insertion and removal of constraints.
 Originality / Value: We have the following innovative contributions: Development of heuristics with a high degree of adaptation to the needs of educational institutions; Two proposed VNS-TS heuristics; Proposed VND-TS improvement method.

Automated University Course Timetable Generator

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.

Solving university course timetabling problem using localized island model genetic algorithm with dual dynamic migration policy

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning a teaching event in a certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. This problem becomes complicated for universities with a large number of students and lecturers. Moreover, several universities are implementing student sectioning, which is a problem of assigning students to classes of a subject while respecting individual student requests, along with additional constraints. Such implementation also implies the complexity of constraints, which is larger accordingly. However, current and generic solvers have failed to meet the scalability and reliability requirements for student sectioning UCTP. In this paper, we introduce the localized island model genetic algorithm with dual dynamic migration policy (DM‐LIMGA) to solve student sectioning UCTP. Our research shows that DM‐LIMGA can produce a feasible timetable for the student sectioning problem and get better results than previous works and the current UCTP solver. Our proposed solution also consistently yield lower violation number than other algorithms, as evidenced by UCTP benchmark experiment results. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Modified Event Grouping Based Algorithm for The University Course Timetabling Problem

This paper presents a study of a modified event grouping based algorithm (MEGB) for university course timetabling problem (UCTP). Multiple models to describe the problem and multiple approaches to solving it are pointed out. The main idea of the modification is that through reduction on the generated solutions the execution time of the standard event grouping based algorithm (EGB) will be reduced, too. Also, an implementation of the modified algorithm based on the described approach is presented. The methodology, conditions and the aims of the experiment are described. The experimental results are analyzed and conclusions are made. When increasing the number of the groups, the execution time of the MEGB algorithm increases and equates with the execution time of the EGB algorithm. The best results are obtained with the first 30% of the groups formed. In these groupings, the execution time of the MEGB algorithm is much less than the execution time of the EGB algorithm. This is because, in the EGB algorithm, every change in the event ordinance creates a new timetable, and all events are repositioned on it. This process is optimized by creating a partial timetable, whereby the ordinance of events in groups before the current does not change. In addition, a comparative analysis between the MEGB algorithm and two other algorithms for UCTP, respectively a genetic algorithm with the local search method (GALS) and a local search algorithm based on chromatic classes (CCLS) is made as well. The obtained results show that the MEGB algorithm and the CCLS algorithm generate better solutions for smaller input data sets, while the GALS algorithm generates better solutions for larger input data sets. However, in terms of the execution time, it was ascertained that the GALS algorithm runs slowest among the others.

University course timetabling problem considering day and time pattern

This study aimed to propose a new formulation for the university course timetabling problem (UCTTP) when a day and time pattern is introduced. The day and time pattern would be set by the university and all assigned courses should follow this pattern. The different points of the proposed formulation would structure the model using starting and ending timeslots instead of a single timeslot to deal with courses with consecutiveness, periodic repeat, and multi-period sessions. To present the effectiveness of the proposed model, three test cases were solved and the results showed that the number of variables and constraints reduced with less computational time. Then, the case study was solved with multiple objectives based on goal programming (GP). The results of the case study show that the number of assignments during the undesirable time was minimised and that the total satisfaction score of all the lecturers was improved.

University course timetabling problem considering day and time pattern

This study aimed to propose a new formulation for the university course timetabling problem (UCTTP) when a day and time pattern is introduced. The day and time pattern would be set by the university and all assigned courses should follow this pattern. The different points of the proposed formulation would structure the model using starting and ending timeslots instead of a single timeslot to deal with courses with consecutiveness, periodic repeat, and multi-period sessions. To present the effectiveness of the proposed model, three test cases were solved and the results showed that the number of variables and constraints reduced with less computational time. Then, the case study was solved with multiple objectives based on goal programming (GP). The results of the case study show that the number of assignments during the undesirable time was minimised and that the total satisfaction score of all the lecturers was improved.

Reinforced Island Model Genetic Algorithm to Solve University Course Timetabling

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, departments, etc. This problem becomes complicated for universities which have immense number of students and lecturers. Therefore, a scalable and reliable timetabling solver is needed. However, current solvers and generic solution failed to meet several specific UCTP. Moreover, some universities implement student sectioning problem with individual student specific constraints. This research introduces the Reinforced Asynchronous Island Model Genetic Algorithm (RIMGA) to optimize the resource usage of the computer. RIMGA will configure the slave that has completed its process to helping other machines that have yet to complete theirs. This research shows that RIMGA not only improves time performance in the computational execution process, it also oers greater opportunity to escape the local optimum trap than previous model.

Performance Comparison of Linear and Non-Linear Great Deluge Algorithms in Solving University Course Timetabling Problems

Different institutions may have their own requirements in course timetabling for every semester and thus it is difficult to produce a general methodology to solve all the problems in every institution. This research compares both linear and nonlinear Great Deluge (GD) algorithms in solving university course timetabling problem (UCTP) and the sample dataset is obtained from the Universiti Malaysia Sabah, Labuan International Campus (UMSLIC), Malaysia. In this paper, the violation of soft constraints is minimized and the performances of both linear and non-linear GD are compared. This research does not focus on hard constraints involved as the initial solution is solved based on Constraint Programming algorithm. The GD algorithm is tested over three benchmark datasets: testing dataset; semester 2 session 2014/2015 test set; semester 1 session 2015/2016 test set. Based on the experiment’s results obtained, it shows that linear GD is able to produce better solutions in one of the datasets and the same applies to non-linear GD. Hence, it can be deduced that these results able to satisfy the “No Free Lunch (NFL)” theorem, where existing optimization algorithms might not be able to perform well in all datasets. The reason may be due to the constraints involved varied for each dataset as the optimization problems solved by any algorithms are uniformed in relation to the NFL theorems.

A Constraint Programming Approach to Solving University Course Timetabling Problem (UCTP)

A Constraint Programming Approach to Solving University Course Timetabling Problem (UCTP)

Comparison of Simulated Annealing and Great Deluge Algorithms for University Course Timetabling Problems (UCTP)

Comparison of Simulated Annealing and Great Deluge Algorithms for University Course Timetabling Problems (UCTP)