An SHO-based approach to timetable scheduling: a case study

Abstract

University timetable scheduling, which is a typical problem that all universities around the world have to face every semester, is an NPhard problem. It is the task of allocating the right timeslots and classrooms for various courses by taking into account predefined constraints. In the current literature, many approaches have been proposed to find feasible timetables. Among others, swarm-based algorithms are promising candidates because of their effectiveness and flexibility. This paper investigates proposing an approach to university timetable scheduling using a recent novel swarm-based algorithm named Spotted Hyena Optimizer (SHO) which is inspired by the hunting behaviour of spotted hyenas. Then, a combination of SA and SHO algorithms also investigated to improve the overall performance of the proposed method. We also illustrate the proposed method on a real-world university timetabling problem in Vietnam. Experimental results have indicated the efficiency of the proposed method in comparison to other competitive metaheuristic algorithm such as PSO algorithm in finding feasible timetables.

Optimization in University Student Timetables: A Comprehensive Literature Review

“…Figure 1 below shows the flow diagram for the comprehensive review process. Reference Title Year Country [11] Developing a mobile-based application system to accelerate the efficiency of the course rescheduling process Malaysia [12] Modified and hybridised bi-objective firefly algorithms for university course scheduling Thailand [13] A general mathematical model for university courses timetabling: Implementation to a public university in Malaysia Malaysia [14] A genetic algorithm for the real-world university course timetabling problem Malaysia [15] Grouping and heuristics for a multi-stage class timetabling system Malaysia [16] Hybrid whale optimization algorithm for solving timetabling problems of ITC 2019 Indonesia [17] Investigation of heuristic orderings with a perturbation for finding feasibility in solving real-world university course timetabling problem Malaysia [18] Lecturer-course assignment model in national joint courses program to improve education quality and lecturers' time preference Indonesia [19] A compromise programming for multi-objective task assignment problem Vietnam [20] A hybrid of heuristic orderings and variable neighbourhood descent for a real-life university course timetabling problem Malaysia [21] An SHO-based approach to timetable scheduling: a case study Vietnam [22] Application of genetic algorithm to optimize lecture scheduling based on lecturers' teaching day willingness Indonesia [23] Automation and optimization of course timetabling using the iterated local search hyper-heuristic algorithm with the problem domain from the 2019 international timetabling competition Indonesia [24] Class scheduling framework using decorator and facade design pattern Philippines [25] Effective solution of university course timetabling using particle swarm optimizer based hyper heuristic approach Malaysia [26] Lecturer teaching scheduling that minimizes the difference of total teaching load using goal programming Indonesia [27] Multi-agent class timetabling for higher educational institutions using Prometheus platform Philippines [28] Particle swarm optimisation variants and its hybridisation ratios for generating cost-effective educational course timetables Thailand [29] Stemming the educational timetable problems Indonesia [18] University course timetabling model in joint courses program to minimize the number of unserved requests Indonesia [30] An effective hybrid local search approach for the post enrolment course timetabling problem Malaysia...…”

“…Table 4 shows the summary of the optimization approaches in this review. Answer set programming (ASP) [56] Hybrid Method Course Rescheduling Application System (CRAS) with implemented checking algorithm [11] Modified and hybridized bi-objective firefly algorithm (BOFA) with Pareto dominance approach [12] Multi-stage approach incorporating heuristics and grouping [15] Hybrid whale optimization algorithm that was a combination of the adapted whale optimization algorithm (WOA) and late acceptance hill climbing (LAHC) algorithm [16] Heuristic Ordering with a Perturbation technique (HO-P) [17] Lecturer-course assignment model developed by using integer linear programming and optimized by using cloud theory-based simulated annealing [18] Two stage heuristic algorithms with heuristics orderings, and hybrid of heuristic orderings and variable neighbourhood descent [20] Spotted Hyena Optimizer (SHO) and hybridization of SHO and Simulated Annealing (SA) [21] Iterated Local Search-Hill Climbing (ILS-HC) and Iterated Local Search-Simulated Annealing (ILS-SA) algorithms within hyper-heuristics [23] Class scheduling model using decorator and facade design patterns [24] Particle Swarm Optimizer based Hyper Heuristic (HH PSO)…”

“…Nguyen and Nguyen [21] presented Spotted Hyena Optimizer (SHO), a swarm-based algorithm inspired by spotted hyena hunting behaviour, and its hybridization with Simulated Annealing (SA) for university timetabling. Experimental results with Nong Lam University datasets revealed that the proposed approach, particularly the SHO-SA hybrid, outperformed other competitive metaheuristic algorithms like the PSO algorithm in finding feasible timetables.…”

“…Reference Dataset description [11] UCSI University, Malaysia [12] Eleven datasets obtained from Naresuan University, Thailand [13] Semester 1 2017/2018, Universiti Malaysia Terengganu (UMT), Malaysia [14] Semester 1 2018/2019, Universiti Malaysia Sabah Labuan International Campus (UMSLIC), Malaysia [15] Semester 2016/s6 and semester 2016/s2, i-Cats University College (ICATS), Malaysia [16] ITC 2019 [17] ITC 2007 Semester 1 2019/2020 and semester 2 2019/2020, Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), Malaysia [18] Lecturers-courses assignment simulation [19] Spring semester of 2020 for Computing Fundamental Department, FPT University, Vietnam [20] Semester 1 2019/2020 and semester 2 2019/2020, Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), Malaysia [21] Faculty of Information Technology, Nong Lam University (NLU), Vietnam [22] Even semester 2018/2019, Informatics Engineering Study Program of Universitas Catur Insan Cendekia Cirebon, Indonesia [23] ITC 2019 [24] Generate 4 datasets based on assumptions [25] ITC 2002 and ITC 2007 [26] Department of Mathematics XYZ University [27] Semester 1 2018/2019, semester 2 2017/2018, semester 1 2017/2018, semester 2 2016/2017, University of the East, Philippines [28] Eleven datasets obtained from Naresuan University, Thailand [29] Socha STKIP (a medium-size Indonesian college from the year 2014 to the year 2016) Lewis Carter ITC 2007 [30] ITC 2002 ITC 2007 Socha Hard [31] Semester 2 2014/2015 and semester 1 2015/2016, Universiti Malaysia Sabah Labuan International Campus (UMSLIC), Malaysia [32] Odd and Event semester, Operational Division of Universitas Teknologi Yogyakarta, Indonesia [33] Semester 1 2017/2018 and semester 2 2017/2018 from Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak (UNIMAS), Malaysia [35] Socha [36] Semester 1 2016/2017, Universiti Malaysia Sabah Labuan International Campus (UMSLIC), Malaysia [37] Semester 2 2014/2015 and semester 1 2015/2016, Universiti Malaysia Sabah Labuan International Campus (UMSLIC), Malaysia…”