Abstract
Examination timetabling is one of 3 critical timetabling jobs besides enrollment timetabling and teaching assignment. After a semester, scheduling examinations is not always an easy job in education management, especially for many data. The timetabling problem is an optimization and Np-hard problem. In this study, we build a multi-objective optimizer to create exam schedules for more than 2500 students. Our model aims to optimize the material costs while ensuring the dignity of the exam and students' convenience while considering the rooms' design, the time requirement of each exam, which involves rules and policy constraints. We propose a programmatic compromise to approach the maximum tar-get optimization model and solve it using the Genetic Algorithm. The results show the effectiveness of the introduced algorithm.
Highlights
Timetabling problems arise in various forms including educational timetabling, sport timetabling, transportation timetabling
We have designed a new approach to examination timetabling
Proposing the optimal model, but we use a hybrid approach for the multi-objective programming (MOP) problem
Summary
Timetabling problems arise in various forms including educational timetabling, sport timetabling, transportation timetabling. In the training institutions, Timetabling is a difficult process faced every semester. It basically is arranging timeslot for a resource such as students, classes, lectures. University Course Timetabling: Schedule courses into timeslots and assign students, time, rooms to each course. A non-trivial timetabling problems are normally NP-Hard problems [5] This kind of problem is not always possible to reach one global optimal solution. Hard constrains: Examination timetabling problem is assigning exams to examination periods and rooms so that the following constraints are respected. 1. Only one exam can be placed in a room at any period. Soft constrains / Objectives: Besides searching for an optimal solution that satisfies all hard constraints mentioned above, the following criteria are optimized
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