Particle Swarm Optimisation Variants and Its Hybridisation Ratios for Generating Cost-Effective Educational Course Timetables

Abstract

Due to the COVID-19 pandemic, many universities across the globe are unexpectedly accelerated to face another major financial crisis. An effective university course timetabling has a direct effect on the utilisation of the university resources and its operating costs. The university course timetabling is classified to be a Non-deterministic Polynomial (NP)-hard problem. Constructing the optimal timetables without an intelligence timetabling tool is extremely difficult task and very time-consuming. A Hybrid Particle Swarm Optimisation-based Timetabling (HPSOT) tool has been developed for optimising the academic operating costs. In the present study, two variants of Particle Swarm Optimisation (PSO) including Standard PSO (SPSO) and Maurice Clerc PSO (MCPSO) were embedded in the HPSOT program. Five combinations of Insertion Operator (IO) and Exchange Operator (EO) were also proposed and integrated within the HPSOT program aimed at improving the performance of the proposed PSO variants. The statistical design and analysis indicated that five combination results of IO and EO for hybrid SPSO and MCPSO were significantly better than those obtained from the original PSO variants for all eleven problem instances. The average computational times taken by the proposed hybrid methods were also faster than the conventional SPSO and MCPSO for all cases.

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...…”

“…Thepphakorn, Sooncharoen, and Pongcharoen [28] presented hybrid particle swarm optimization-based timetabling (HPSOT) that integrates Standard PSO (SPSO) and Maurice Clerc PSO (MCPSO) variants, alongside five combinations of Insertion Operator (IO) and Exchange Operator (EO) to enhance timetabling performance. The comparison results with Naresuan University datasets revealed that the hybrid SPSO and MCPSO variants with IO and EO combinations outperformed original PSO variants in terms of optimization quality across all problem instances with faster computational times and a ratio of 75%:25% being the best IO:EO combination in convergence speed.…”

“…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…”

COVID-19 Induced Economic Slowdown and Mental Health Issues

“…In the case of Australia, the higher education sector expected the revenue loss to be AUS$3-4.6 billion in the academic year 2019-2020. Besides, the number of international student enrollments in the United States was expected to reduce by at least a quarter, with a huge loss of income from the higher education sector (Thepphakorn et al, 2021). According to the estimation by United Nations Educational Scientific and Cultural Organization (UNESCO), up to 900 million learners were affected by the closure of educational institutions (Nicola et al, 2020).…”

Improved Genetic Algorithm for English Information Education and Teaching Quality Impact Analysis

“…e automatic class scheduling problem is proved to be a nondeterministic polynomial (NP) complete problem [4]. Due to the complexity of this kind of problem, more and more scholars have paid attention to and studied it.…”