Variable Neighborhood Search Based Algorithm for University Course Timetabling Problem

TL;DR

This paper introduces a variable neighborhood search algorithm tailored for university course timetabling, demonstrating its effectiveness through real-world data and comparisons with other algorithms.

Contribution

It develops a novel variable neighborhood search method specifically for university course timetabling, improving solution quality over existing approaches.

Findings

The algorithm effectively solves real university timetabling problems.

It outperforms other algorithms on the same datasets.

Results show improved scheduling efficiency and quality.

Abstract

In this paper a variable neighborhood search approach as a method for solving combinatoric optimization problems is presented. A variable neighborhood search based algorithm for solving the problem concerning the university course timetable design has been developed. This algorithm is used to solve the real problem regarding the university course timetable design. It is compared with other algorithms that are tested on the same sets of input data. The object and the methodology of study are presented. The main objectives of the experiment are formulated. The conditions for conducting the experiment are specified. The results are analyzed and appropriate conclusions are made. The future trends of work in this field are presented.