Route optimisation using evolutionary approaches for on-demand pickup problem. (2nd December 2009) - Mukai, Naoto; Watanabe, Toyohide
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.