Ghaith M. Jaradat

Hybrid Ant Colony systems for course timetabling problems

The University Course Timetabling is a complex optimization Problem which is difficult to solve for optimality. It involves assigning lectures to a fixed number of timeslots and rooms; while satisfying some constraints. The goal is to construct a feasible timetable and satisfy soft constraints as much as possible. In this study, we apply two hybrids Ant Colony Systems, namely the Simulated Annealing with Ant Colony System (ACS-SA), and Tabu Search with Ant Colony System (ACS-TS) to solve the university course timetabling, a number of ants in the ACS construct a complete assignment of courses to timeslots. Based on a pre-ordered list of courses, the ants probabilistically choose the timeslot for the given course, guided by heuristic information and stigmergic information. We test both ACS algorithms over the Sochas benchmark course timetabling problem. We also compare our results with those obtained by other methodologies recent literature has illustrated. Experimental results showed that both ACS-SA and ACS-TS produces good quality solutions and outperforms previously applied Ant algorithms; they also outperform other methodologies tested on Sochas benchmark test instances, and approaches on some benchmark instances. We believe that these hybrid ACS algorithms are also valid for other types of combinational optimization problems.

An Elitist-Ant System for Solving the Post-Enrolment Course Timetabling Problem

Ant System algorithms are nature-inspired population-based metaheuristics derived from the field of swarm intelligence. Seemingly, the ant system has a lack of search diversity control since it has only a global pheromone update that intensifies the search. Hence, one or more assistant mechanisms are required to strengthen the search of the ant system. Therefore, we propose, in this study, an elitist-ant system to strike a balance between search diversity and intensification while maintaining the quality of solutions. This process is achieved by employing two diversification and intensification mechanisms to assist both pheromone evaporation and elite pheromone updating, in order to gain a good control over the search exploration and exploitation. The diversification mechanism is employed to avoid early convergence, whilst the intensification mechanism is employed to exploore the neighbors of a solution more effectively. In this paper, we test our algorithm on post-enrolment course timetabling problem. Experimental results show that our algorithm produces good quality solutions and outperforms some results reported in the literature (with regards to Socha’s instances) including other ant system algorithms. Therefore, we can conclude that our elitist-ant system has performed an efficient problem’s specific knowledge exploitation, and an effective guided search exploration to obtain better quality solutions.

Big Bang-Big Crunch optimization algorithm to solve the course timetabling problem

In this study, we present the Big Bang-Big Crunch (BB-BC) method to solve the post-enrolment course timetabling problem. This method is derived from one of the evolution of the universe theories in physics and astronomy. The BB-BC theory involves two phases (Big Bang and Big Crunch). The Big Bang phase feeds the Big Crunch phase with many inputs and the Big Crunch phase is the shrinking destiny of the universe into singularity. Generally, the BB-BC algorithm generates a population of random initial solutions in the Big Bang phase and shrinks those solutions to a single good quality solution represented by a centre of mass. Experimental results show that the BB-BC can produce good quality solutions and comparable with some of the methods applied in the literature (with regards to Sochas benchmark instances).

On the performance of Scatter Search for post-enrolment course timetabling problems

This study presents an investigation of enhancing the capability of the Scatter Search (SS) metaheuristic in guiding the search effectively toward elite solutions. Generally, SS generates a population of random initial solutions and systematically selects a set of diverse and elite solutions as a reference set for guiding the search. The work focuses on three strategies that may have an impact on the performance of SS. These are: explicit solutions combination, dynamic memory update, and systematic search re-initialization. First, the original SS is applied. Second, we propose two versions of the SS (V1 and V2) with different strategies. In contrast to the original SS, SSV1 and SSV2 use the quality and diversity of solutions to create and update the memory, to perform solutions combinations, and to update the search. The differences between SSV1 and SSV2 is that SSV1 employs the hill climbing routine twice whilst SSV2 employs hill climbing and iterated local search method. In addition, SSV1 combines all pairs (of quality and diverse solutions) from the RefSet whilst SSV2 combines only one pair. Both SSV1 and SSV2 update the RefSet dynamically rather than static (as in the original SS), where, whenever a better quality or more diverse solution is found, the worst solution in RefSet is replaced by the new solution. SSV1 and SSV2 employ diversification generation method twice to re-initialize the search. The performance of the SS is tested on three benchmark post-enrolment course timetabling problems. The results had shown that SSV2 performs better than the original SS and SSV1 (in terms of solution’s quality and computational time). It clearly demonstrates the effectiveness of using dynamic memory update, systematic search re-initialization, and combining only one pair of elite solutions. Apart from that, SSV1 and SSV2 can produce good quality solutions (comparable with other approaches), and outperforms some approaches reported in the literature (on some instances with regards to the tested datasets). Moreover, the study shows that by combining (simple crossover) only one pair of elite solutions in each RefSet update, and updating the memory dynamically, the computational time is reduced.

The effect of elite pool in hybrid population-based meta-heuristics for solving combinatorial optimization problems

This work investigates the effect of elite pool that has high-quality and diverse solutions in three hybrid population-based meta-heuristics with an elite pool of a hybrid Elitist-Ant System, a hybrid Big Bang-Big Crunch optimization, and a hybrid scatter search. The purpose of incorporating an elite pool in population-based meta-heuristics is to maintain the diversity of the search while exploiting the solution space as in the reference set of the scatter search. This may guarantee the effectiveness and efficiency of the search, which could enhance the performance of the algorithms and generalized well across different datasets. To test the generality of these meta-heuristics via their consistency and efficiency, we use three classes of well-known combinatorial optimization problems as follows: symmetric traveling salesman problem, 0-1 multidimensional knapsack problem, and capacitated vehicle routing problem. Experimental results showed that the performance of our hybrid population-based meta-heuristics, compared to the best known results, is competitive in many instances. This finding indicates the effectiveness of utilizing an elite pool in our hybrid meta-heuristics in diversifying the search and subsequently enhances their performance over different instances and problems.

Scatter search for solving the course timetabling problem

Scatter Search (SS) is an evolutionary population-based metaheuristic that has been successfully applied to hard combinatorial optimization problems. In contrast to the genetic algorithm, it reduces the population of solutions size into a promising set of solutions in terms of quality and diversity to maintain a balance between diversification and intensification of the search. Also it avoids using random sampling mechanisms such as crossover and mutation in generating new solutions. Instead, it performs a crossover in the form of structured solution combinations based on two good quality and diverse solutions. In this study, we propose a SS approach for solving the course timetabling problem. The approach focuses on two main methods employed within it; the reference set update and solution combination methods. Both methods provide a deterministic search process by maintaining diversity of the population. This is achieved by manipulating a dynamic population size and performing a probabilistic selection procedure in order to generate a promising reference set (elite solutions). It is also interesting to incorporate an Iterated Local Search routine into the SS method to increase the exploitation of generated good quality solutions effectively to escape from local optima and to decrease the computational time. Experimental results showed that our SS approach produces good quality solutions, and outperforms some results reported in the literature (regarding Sochas instances) including population-based algorithms.

Looking Inside and Outside the System: Examining the Factors Influencing Distance Learners Satisfaction in Learning Management System

In the last few years, the use of educational technology, particularly the concept of Learning Management System (LMS), has increased rapidly. With this fast development, the question arises as how to manage the LMS to obtain success and efficiency in online courses. One of the important factors that have received many citations in literature studies (and has a special position in information system research) is the user satisfaction. It is a crucial factor that can predict the success or failure of any LMS. In relation, this research examined the success factors that affect the user satisfaction and outcomes of LMS. This paper discusses the conceptual User Satisfaction Evaluation Model (USEM) employed to measures LMS success. In particular, it seeks to examine “the relationship between: Service quality, system quality, ease of use, perceived usefulness, information quality and students satisfaction, as well as to measure the outcomes of the LMS.” Results from the data analysis indicate that all proposed factors have a positive effect on student satisfaction. The result also concludes that a higher rate of user satisfaction will lead to greater benefits for the students.

Solving the viva presentations timetabling problem: A case study at FTSM-UKM

There is a growing need to automatically timetable viva presentations for postgraduate candidates due to the increasing number of students enrolled each year, and hence, requiring additional personnel effort. The automatic timetabling process involves the assignment of the people involved in the viva timetable into a limited number of timeslots and rooms. In order to produce a feasible timetable, we must satisfy some regulations (hard constraints), while attempting to accommodate as much as possible some preferences (soft constraints). In this work, we tackle the problem of scheduling viva presentations for the Masters degree students at FTSM-UKM as a case study. Each presentation must be attended by a chair of the school (or representative), a chair of the viva presentation, a technical committee member, a student (presenter), an internal examiner and supervisor(s). The presentation must be scheduled into a room and timeslot. In this work, we propose a new objective function to model the problem and to evaluate the quality of the timetable (schedule). We also introduce a greedy constructive heuristic to construct a valid timetable that satisfies all of the hard constraints and tries to satisfy the soft constraints as much as possible. The heuristic will assign the committee and students into an empty timetable based on a pre-ordered list of prioritized elements. These elements are ordered based on the largest enrolment: specifically a technical person who has the largest number of students enrolled under his/her supervision and examination will be ordered first in the list and is first to be assigned into the timetable. Results show that the automated timetabling solver can efficiently produce good quality timetable in reasonable time.