A variable neighborhood search algorithm for the optimization of a dial-a-ride problem in a large city

On-demand transportation is becoming a new necessary service for modern (public and private) mobility and logistics providers. Large cities are demanding more and more share transportation services with flexible routes, resulting from user dynamic demands. In this study a new algorithm is proposed for solving the problem of computing the best routes that a public transportation company could offer to satisfy a number of customer requests. In this problem, known in the literature as the dial-a-ride problem, a number of passengers has to be transported between pickup and delivery locations trying to minimize the routing costs while respecting a set of pre-specified constraints (maximum pickup time, maximum ride duration and maximum load per vehicle). For optimizing this problem, a new variable neighborhood search has been developed and tested on a set of 24 different scenarios of a large-scale dial-a-ride problem in the city of San Francisco. The results have been compared against two state-of-the-art algorithms of the literature and validated by means of statistical procedures proving that the new algorithm has obtained the best overall results.