Route optimisation using evolutionary approaches for on‐demand pickup problem
Source: International Journal of Advanced Intelligence Paradigms, Volume 2, Number 1, 30 November 2009 , pp. 19-32(14)
Publisher: Inderscience Publishers
Abstract:The development of information technologies realises an on‐demand transport (pick‐up) system. In this paper, we simulate transport situations for the system based on multi‐agent model to find efficient strategies. We examine four types of driver agents; random agent, greedy agent, Q‐learning agent, and Genetic agent. Random agent and Greedy agents select the next pick‐up points from its surround without learning and optimisation. In contrast, Q‐learning agent estimates the expectation value of pick‐up quantity by Q‐learning, and Genetic agent optimises its travel routes by Genetic algorithm. Finally, we report our experimental results to evaluate the effect of the four strategies.
Document Type: Research Article
Affiliations: 1: Department of Electrical Engineering, Faculty of Engineering, Division 1, Tokyo University of Science, Kudankita, Chiyoda-ku, Tokyo 102-0073, Japan. 2: Department of Systems and Social Informatics, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan
Publication date: November 30, 2009
- The International Journal of Advanced Intelligence Paradigms fosters the exchange and dissemination of applications and case studies in the area of advanced intelligence paradigms among professionals in education and research. The thrust of the journal is to publish papers dealing with the design, development, testing, implementation and management of advanced intelligent systems and also to provide practical guidelines in the development and management of these systems. The International Journal of Advanced Intelligence Paradigms will publish both archival articles and broader assessments of current trends. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.