Courses timetabling based on hill climbing algorithm

Abstract

In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots.

Bibliometric Analysis of University Timetabling Using Publish and Perish

“…In contrast, exam scheduling discusses a certain amount of time in a limited number of rooms. Research on course scheduling was carried out by [6], [7], [8], [9], [10]. Meanwhile, [11], and [12] discussed exam scheduling.…”

“…Especially when lectures or exams with different subjects meet institutional conditions or situations. Both students and teachers need to comply with them properly [10], [13].…”

Multi-objective fuzzy-based adaptive memetic algorithm with hyper-heuristics to solve university course timetabling problem

“…A Hybrid Immune Genetic Algorithm to Solve University Time Table Problems 2017) have performed an artificial immune system to initialize a population in the immune-genetic algorithm (IGA). Both algorithms perform well as compared with simple GA. Rjoub [26] claims the hill-climbing greedy algorithm performs better than a simple genetic algorithm and generates an optimal solution in less time.…”

“…Eludire and Akanbi [19] apply fuzzy logic with a genetic algorithm to schedule a timetabling problem by ranking different courses in order of difficulty level. Rjoub [26] proposes an application of the hill-climbing algorithm in various techniques of genetic algorithm for better performance of the genetic algorithm. Various intelligent techniques are used to customize mutation and crossover operators and it shows improvement in solution convergence [17].…”

The performance analysis of hyper-heuristics algorithms over examination timetabling problems

“…The algorithms that will be compared include hill climbing (HC), simulated an-❒ ISSN: 2252-8938 nealing (SA), great deluge (GD), and tabu search (TS). The HC algorithm is inspired by the strategies used by mountain climbers in finding the most efficient path to their destination [8]. The SA algorithm is a metaheuristic technique inspired by the annealing process, and modified to find global optimum solutions [9], [10].…”