Optimization of the Carpool Service Problem via a Fuzzy-Controlled Genetic Algorithm

Abstract

Carpooling is a means of vehicle sharing by which drivers share their cars with one or more riders whose travel itineraries are similar to their own. As such, carpooling can be an effective way to ease traffic congestion. In this paper, we first present an intelligent carpool system based on the service-oriented architecture. Second, we propose a fuzzy-controlled genetic-based carpool algorithm by using the combined approach of the genetic algorithm and the fuzzy control system, with which to optimize the route and match assignments of the providers and the requesters in the intelligent carpool system. In regard to the quality of the match solutions and processing time, the exhaustive algorithm, the random matching algorithm, and the standard genetic algorithm are applied and their results compared with those produced by our proposed algorithm. Our experimental results proved that the proposed fuzzy-controlled genetic-based carpool algorithm is capable of consistently finding carpool route and matching results that are among the most optimal solutions that can be obtained via the exhaustive algorithm and, thus, outperforming all other compared methods in regard to match quality. In addition, the proposed algorithm is also able to operate with significantly less computational time than does the exhaustive algorithm and random matching algorithm.