Route optimisation using evolutionary approaches for on-demand pickup problem. (2nd December 2009)

• Record Type: Journal Article
• Authors: Mukai, Naoto; Watanabe, Toyohide
• Is Part Of: International journal of advanced intelligence paradigms. Volume 2:Number 1(2010)
• Page Start: 19
• Page End: 32
• Publication Date: 2009-12-02
• Subjects: on-demand buses -- multi-agent simulation -- evolutionary approach -- Q-learning -- genetic algorithms -- multi-agent systems -- MAS -- agent-based systems -- on-demand transport -- pick-up systems -- route optimisation -- intelligent transport systems -- ITS; Artificial intelligence -- Periodicals; Machine theory -- Periodicals; Fuzzy logic -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-0386

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.