Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach

Abstract

The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed approach generates solutions into two phases (initial and improvement). A new LLH named “least possible rooms left” has been developed and proposed to schedule events. Both datasets of international timetabling competition (ITC) i.e., ITC 2002 and ITC 2007 are used to evaluate the proposed method. Experimental results indicate that the proposed low-level heuristic helps to schedule events at the initial stage. When compared with other LLH’s, the proposed LLH schedule more events for 14 and 15 data instances out of 24 and 20 data instances of ITC 2002 and ITC 2007, respectively. The experimental study shows that HH PSO gets a lower soft constraint violation rate on seven and six data instances of ITC 2007 and ITC 2002, respectively. This research has concluded the proposed LLH can get a feasible solution if prioritized.

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...…”

“…Iqbal et al, [25] proposed a Particle Swarm Optimiser-based Hyper Heuristic (HH-PSO) to solve UCTP with PSO work as a higher-level methodology that employs low-level heuristics to find the best solution. According to the results of the study, the proposed low-level heuristic is effective for organising events during the initial phase.…”

“…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…”

An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks

“…The PSO algorithm will retain two sets of information, the personal optimum and the global optimum, to guide the particle-in search during the run 16 . This search method has a stronger local search capability compared with the CS's double stochastic solution as a difference search method.…”

A Heuristic Approach to the Consecutive Ones Submatrix Problem

“…Heuristic methods have been applied to enormous problems such as 20, 21 . The paper is arranged as follow.…”