Open AccessTravel route scheduling based on user’s preferences using simulated annealing
Author(s) -
Z. K. A. Baizal,
Kemas Muslim Lhaksmana,
Aniq A. Rahmawati,
Mizanul Kirom,
Zidni Mubarok
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i2.pp1275-1287
Subject(s) - travelling salesman problem , simulated annealing , computer science , tourism , operations research , scheduling (production processes) , schedule , ant colony optimization algorithms , travel time , mathematical optimization , heuristics , air travel , simulation , transport engineering , mathematics , artificial intelligence , algorithm , aviation , geography , engineering , archaeology , aerospace engineering , operating system
Nowadays, traveling has become a routine activity for many people, so that many researchers have developed studies in the tourism domain, especially for the determination of tourist routes. Based on prior work, the problem of determining travel route is analogous to finding the solution for travelling salesman problem (TSP). However, the majority of works only dealt with generating the travel route within one day and also did not take into account several user’s preference criteria. This paper proposes a model for generating a travel route schedule within a few days, and considers some user needs criteria, so that the determination of a travel route can be considered as a multi-criteria issue. The travel route is generated based on several constraints, such as travel time limits per day, opening/closing hours and the average length of visit for each tourist destination. We use simulated annealing method to generate the optimum travel route. Based on evaluation result, the optimality of the travel route generated by the system is not significantly different with ant colony result. However, our model is far more superior in running time compared to Ant Colony method.