(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 11, 2022 554 | Page www.ijacsa.thesai.org Optimizing Faculty Workloads and Room Utilization using Heuristically Enhanced WOA Lea D. Austero 1 , Ruji P. Medina 4 Graduate Programs Technological Institute of the Philippines Quezon City, Philippines Ariel M. Sison 2 School of Engineering and Technology Emilio Aguinaldo College Manila, Philippines Junrie B. Matias 3 College of Computing and Information Sciences Caraga State University Butuan City, Philippines Abstract—The creation and generation of schedules that are free of conflicts manually every academic semester present higher education institutions with a duty that is laborious and demanding of their resources. The course timetabling optimization, as an education timetabling problem, is a popular example of an NP-hard combinatorial problem. Numerous attempts have been made over the course of the past few decades to find a solution to this problem, but no one has yet developed a foolproof approach that can examine all alternatives to find the best method. The promising swarm-based optimization algorithm called Whale Optimization Algorithm was heuristically enhanced in the present study and is called HEWOA. It was designed as a solution to the course timetabling problem discussed in the current study. HEWOA was able to generate an efficient timetable for the large dataset of 1700 events for an average time of 14.92 seconds only, with an average generation of 7.2 and a best time of 8.38 seconds. These results reveal that the performance of HEWOA was better than that of various hybrids of the Genetic Algorithm that was compared in the present study. Keywords—Heuristics; mutation; optimization; swarm; timetabling; whale optimization algorithm I. INTRODUCTION The application of automated procedures to a time- consuming and resource-intensive task often leads to increased efficiency and productivity, as well as time and cost savings. Among these processes are the preparation and creation of academic schedules. Timetabling problem was solved manually through trial and error, but this was not the greatest option. At present, scientific methods are used to address the problem [1], [2]. Timetabling problems, better known as the university course timetabling problem (UCTTP), are known to be NP-hard, meaning the problem cannot be solved exactly in polynomial time as its size and complexity increase exponentially [3]. It involves allocating non-overlapping classes to given resources such as classrooms and teachers in space-time [4]. The number of courses, the average number of lectures per day, the desired free timeslots each day, and the targeted off-days in a week are a few of the constraints that influence the design of the educational timetable [1], [5]. In the scheduling problem, there are two types of constraints: hard constraints and soft constraints. Hard constraints are rules or restrictions that cannot be broken. Soft constraints are requirements that, if not violated, can improve the effectiveness of the timetable. A timetable is considered efficient if it is able to solve the problem while adhering to all of the hard constraints specified [6]. The process of scheduling classes is often carried out with the assistance of specialized models that are adapted to meet the requirements of the particular educational establishment in question. A significant amount of work devoted to scheduling makes use of streamlined models to investigate and evaluate the performance of various scheduling strategies. The vast majority of research on course scheduling focuses on modeling and computational results, with very little attention paid to actual implementation in the real world [7]. Several strategies have been used to solve course timetabling using benchmark and real-world datasets. This problem was solved over decades using optimization approaches. Heuristic approaches helped resolve timetabling’s complex behavior and model [8]. Evolutionary methods are frequently used in solving course timetabling; however, these existing methods were not able to quickly tests all alternatives to find the best solution [8]–[10]. Recently, various research has employed the Whale Optimization Algorithm (WOA), which is appreciated as a simple, flexible, and competitive swarm-based metaheuristic algorithm [11][12][13]. Despite its potential, it has inherent flaws that must be addressed before it can effectively address optimization issues such as course timetabling. WOA, like most metaheuristic algorithms, struggles to have a balanced local and global search. The present study is an application and enhancement of the algorithm used in the previous work of WOA [14] in solving course timetabling. In this work, WOA was integrated with heuristic mutation to improve further the performance of WOA in solving optimization problems such as timetabling. The aim of the present work is to introduce HEWOA as a heuristically enhanced variant of the WOA, which is used in solving course timetabling problems. A literature review is presented in the next section of this paper, which contains a discussion on timetabling, solutions for solving UCTTP, and WOA. The third section presents the particulars of the methodology, which includes the problem definition, the architecture, and the HEWOA. Section IV presented the observations, results, and discussions on the experiments conducted in solving the timetabling problem. Finally, the conclusion of this study is presented in Section V.