Compromise ratio with weighting functions in a Tabu Search multi-criteria approach to examination timetabling

2016 ◽  
Vol 72 ◽  
pp. 160-174 ◽  
Author(s):  
Paula Amaral ◽  
Tiago Cardal Pais
Keyword(s):  
Tabu Search ◽  
Examination Timetabling ◽  
Weighting Functions

scholarly journals Genetic Algorithm and Tabu Search Memory with Course Sandwiching (GATS_CS) for University Examination Timetabling

2020 ◽  
Vol 26 (3) ◽  
pp. 385-396
Author(s):  
Abayomi-Alli Adebayo ◽  
Sanjay Misra ◽  
Luis Fern醤dez-Sanz ◽  
Abayomi-Alli Olusola ◽  
A. R. Edun
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Examination Timetabling ◽  
University Examination

scholarly journals Application of Heuristic Combinations in Hyper-Heuristic Framework for Exam Scheduling Problems

2020 ◽  
Vol 4 (4) ◽  
pp. 664-671
Author(s):  
Gabriella Icasia ◽  
Raras Tyasnurita ◽  
Etria Sepwardhani Purba
Keyword(s):  
Tabu Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Combinatorial Problems ◽  
Hill Climbing ◽  
Tabu Search Algorithm ◽  
Scheduling Problems ◽  
Examination Timetabling ◽  
Timetabling Problem

Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.

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