An Ant Path-Oriented Carpooling Allocation Approach to Optimize the Carpool Service Problem With Time Windows

• Published in: IEEE systems journal 2019-03, Vol.13 (1), p.994-1005
• Main Authors: Huang, Shih-Chia, Jiau, Ming-Kai, Liu, Yu-Ping
• Format: Article
• Language: English
• Subjects: Ant colony optimization; Automobiles; Car pools; carpool service problem (CSP); Computer simulation; Genetic algorithms; intelligent transportation system; Optimization; Resource management; Roads; Routing; Simulated annealing; Time factors; Windows (intervals)
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2018.2795255

An intelligent carpool system provides people the convenient use of carpool services. In this paper, we take into consideration the time factor of the carpool service problem (CSP) by both the appearance time and the endurance time. The carpool service problem with time windows disfavors carpool solutions in which people show up within a time out. An ant path-oriented carpooling allocation approach is proposed to solve this problem in the time domain. The experimental section presents the environment and results for the proposed approach and three compared approaches, including assignment-based ant colony optimization, genetic algorithm, and simulated annealing. Here, we are interested in comparing the performance of these approaches with path-based and Assignment-based representations. For each tested benchmark, we compare two objective functions: a primary objective that maximizes the total amount of matched passengers and seat usage rates (SURs), and a secondary objective that reduces users' distances as much as possible. The values are presented in the experimental section, and through them we demonstrate that our approach obtains notable performance results against the others.