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


Carpooling is a suggests that of car sharing by which drivers share their cars with a number of riders whose travel itineraries are almost like their own. As such, carpooling will be a good means to ease hold up. In this paper, we tend to initial present an intelligent carpool system based mostly on the service-oriented design. Second, we tend to propose a fuzzy-controlled genetic-based mostly carpool algorithm by using the combined approach of the genetic algorithm and the fuzzy management system, with that to optimize the route and match assignments of the suppliers 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 created by our proposed algorithm. Our experimental results proved that the proposed fuzzy-controlled genetic-primarily based carpool algorithm is capable of consistently finding carpool route and matching results that are among the most optimal solutions that may be obtained via the exhaustive algorithm and, so, outperforming all other compared strategies in regard to match quality. Further, the proposed algorithm is also able to operate with significantly less computational time than will the exhaustive algorithm and random matching algorithm.

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