Automated system for university timetabling

Quality of reading courses for students and pedagogical potential usage efficiency in universities depends to some extent on the level of the educational process. Scheduling courses as one of the components of this process regulates the rhythm of labor effects and affects the creative impact of instructors. The relevance of automation scheduling timetables is time consuming process, time-consuming in its drafting, as well as the need for highly qualified staff to produce high-quality schedulesКачество подготовки специалистов и эффективность использования научно-педагогического потенциала в вузах зависят в определенной степени от уровня организации учебного процесса. Расписание занятий, как одна из составляющих этого процесса, регламентирует трудовой ритм, влияет на творческую отдачу преподавателей. Актуальность автоматизации составления расписания обусловлена трудоёмкостью процесса, большими затратами времени при его составлении, а также необходимостью в использовании персонала высокой квалификации для составления качественного расписани

Combining the Min-Conflicts and Look-Forward Heuristics to Effectively Solve A Set of Hard University Timetabling Problems

University timetabling problems (UTPs) represent a class of challenging, high-dimensional and multi-objectives combinatorial optimization problems that are commonly solved by constructive search, local search methods or their hybrids. In this paper, we proposed to combine the min-conflicts and look-forward heuristics used in local search methods to effectively solve general university timetabling problems. Our combined heuristics when augmented with the k-reset operator, and appropriate heuristic variable ordering strategy achieved impressive results on a set of challenging UTPs obtained from an international timetabling competition. A preliminary analysis of the results was given. More importantly, our search proposal shed light on effectively solving other complex or large-scale scheduling problems.published_or_final_versio

Adapting Artificial Immune Algorithms For University Timetabling

Penjadualan kelas dan peperiksaan di universiti adalah masalah pengoptimuman berkekangan tinggi. University class and examination timetabling are highly constrained optimization problems

Agent based approach to University Timetabling Problem

A concept of agent-based approach to timetabling problem is presented. Based on the problem description and with its formalization the term agent is introduced. Agents act on behalf of entities taking part in the timetabling process (activities, rooms and students) and they interact to maximize their own utility. Also a brief overview of existing approaches is presented

Genetic algorithms with guided and local search strategies for university course timetabling

This article is posted here with permission from the IEEE - Copyright @ 2011 IEEEThe university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1