Multiobjective evolutionary algorithms for the taxi sharing problem
by Renzo Massobrio; Gabriel Fagúndez; Sergio Nesmachnow
International Journal of Metaheuristics (IJMHEUR), Vol. 5, No. 1, 2016

Abstract: Transportation planning plays a central role in the design and development of smart cities. In particular, the concept of sharing economy applied to urban transportation is gaining massive public attention in recent years. This article presents the application of two multiobjective evolutionary algorithms to the problem of distributing passengers travelling from the same origin to different destinations in several taxis. A new problem formulation is presented, accounting for two quality of service metrics from the point of view of taxi users: the total cost of the trips and the delay experienced by each passenger. Two multiobjective evolutionary algorithms are proposed: a parallel microevolutionary algorithm (following a linear aggregation approach to combine the problem objectives) and one well-known algorithm from the literature (following a full multiobjective approach based on Pareto dominance). Both algorithms are compared against each other and against two greedy heuristics based on the ideas presented in the related literature. The experimental evaluation is performed over a set of 88 problem instances generated using real GPS taxi data. Results show that the proposed algorithms are able to efficiently reach significant improvements in both problem objectives over the greedy heuristics in short execution times.

Online publication date: Mon, 12-Sep-2016

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