An augmented estimation of distribution algorithm for multi-carpooling problem with time window

A multi-carpooling model is proposed for the multi-vehicle carpooling problem in a distributed parallel computing environment. A two-stage stochastic optimization of the estimation of distribution algorithm solves the optimum of the multi-carpooling problem with a carpooling probabilistic matrix. A ridable matrix initiates the carpooling probabilistic matrix, and the carpooling probabilistic matrix continues updating during the optimization. The carpooling model mines efficient and compromised ridesharing routes for shared riders by the optimization iterations. Experimental results indicate that the carpooling model has the characteristics of effective and efficient traffic including shorter waiting time, more passenger load, and less average riding distance