Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Operations Research and Management

Scheduling timetable is a practically significant problems, which is concerned in solving various numbers of domains such as (production, project management, education, transportation, sport events, etc). Scheduling timetable is elementary to manage and follow the activities and the tasks for any filed. The primary faction of developing the timetable is to allocate the available resources for the required activities in a definite time. This allocation is restricted by a set of requirements which need to be satisfied as much as possible. University course timetable problem is one of education problems. All academic enterprises should build and follow a timetable. Since, the timetable affects on the stability of the education process. Course timetable problem can be described as the assignment problem which allocates a set of (teachers, courses, and group of students) to a specific number of resources (rooms, days, and timeslots). The assignment process is surrounded by as set of hard and soft constraints. All hard constraints must be satisfied to make the timetable feasible. The soft constraints should be satisfied as much as possible which is effecting on the quality of the timetable. Many researchers have been published for solving this problem using Artificial Inelegance (AI) and Operations Research (OR) methods. The objective of this research is to propose an approach for solving the university timetable problem depending on the aspects of the metaheuristics approaches, Genetic Algorithm integrated with Hill Climbing Optimization and with Simulating Annealing Optimization to achieve this objective. The proposed approaches are tested by a known benchmark named Hard timetable (HDTT). The experiments start with Relative Percentage Deviation (RPD) which is used to determine the suitable mutation type over 16 types of mutation

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