A comparative study between a simulated annealing and a genetic algorithm for solving a university timetabling problem

Detta är en Kandidat-uppsats från KTH/Skolan för datavetenskap och kommunikation (CSC)

Sammanfattning: The university timetabling problem is an NP-complete problem which schools all over the world face every semester. The aim of the problem is to schedule sets of events such as lectures and seminars into certain time slots without violating numerous specified constraints. This study aimed to automate this process with the help of simulated annealing and compare the results with a genetic algorithm. The input data sets were inspired by the Royal Institute of Technology in Stockholm. The results showed a great run time difference between the two algorithms where the simulated annealing performed much better. They also showed that even though the simulated annealing algorithm was better during all stages, the genetic algorithm had a much better performance in early stages than it had in latter. This led to the conclusion that a more optimized, hybrid algorithm could be created from the two algorithms provided that the genetic algorithm could benefit from the improvements suggested in previous research.

  HÄR KAN DU HÄMTA UPPSATSEN I FULLTEXT. (följ länken till nästa sida)