On Integrating Simulated Annealing Within Parallel Genetic Algorithm: An On-Demand Transportation Case Application

• Published in: Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) Vol. 552; pp. 153 - 163
• Main Authors: Chebbi, Olfa, Fatnassi, Ezzeddine, Kaabi, Hadhami
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Advances in Intelligent Systems and Computing
• Subjects: Artificial intelligence; Empty vehicle redistribution; Management of public transportation; Personal Rapid Transit; Vehicle routing problem
• ISBN: 9783319529400; 3319529404
• ISSN: 2194-5357; 2194-5365
• DOI: 10.1007/978-3-319-52941-7_16

The increased use of private vehicles, especially in urban areas, has led to problems with pollution and congestion. In response, new transportation systems, such as Personal Rapid Transit (PRT), have been developed. In this paper, we propose to deal with a routing problem related to PRT that consists of satisfying a known set of passenger requests using a set of homogeneous PRT vehicles with limited battery capacity. Our primary goal is to test the effect of integrating simulated annealing algorithm within parallel genetic algorithm in this particular case of on-demand transportation system. For that purpose, this paper proposes an hybrid simulated annealing genetic algorithm for the case of energy minimization in PRT. In this paper, we demonstrate the efficiency of the proposed algorithm by testing it on a large number of PRT instances adapted from a real case study. Our method is shown to produce good results as we found an average gap relative to a linear relaxation of 2.131%.