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A Heuristic Multi-Objective Optimization Algorithm for Solving the Carpool Services Problem Featuring High-Occupancy-Vehicle Itineraries

An intelligent carpool system provides convenience to both drivers and passengers who access carpool services to enjoy their ridesharing. The foundation of carpool-related issues is defined as the carpool service problem. This paper investigates a solution for this fundamental problem, more specifically described as the carpool service problem featuring high-occupancy-vehicle itineraries, which takes into consideration the enhancement of vehicular space occupancy for each portion of the ridesharing itinerary involving multiple objectives. As such, we propose the heuristic multi-objective optimization algorithm to solve the carpool service problem featuring high-occupancy-vehicle itineraries. This approach is based on the non-dominated sorting genetic algorithm and comprised of two modules: heuristics search operation and multi-objective individual selection. In the experimental section, the non-dominated sorting genetic algorithm without the heuristics mechanism is considered an important competitor to the proposed heuristic multi-objective optimization algorithm (HMO-CSPHI). Experimental results demonstrate that HMO-CSPHI can generate superior performance to other compared methods in terms of quantitative analysis and visualization comparison.