| Summary: | 碩士 === 國立臺北科技大學 === 電子工程系研究所 === 104 === Effective space usage for vehicle seats by carpooling has been discussed as a promising opportunity to substantially reduce vehicle demand on the road. Therefore, traffic congestion can be mitigated, particular at peak traffic times. Previous literature [2] has referred it as an Intelligent Carpool System, which provides carpoolers the use of the carpool services. The state-of-the-art approaches in [2]–[4] solve the Carpool Service Problem, which is a combinatorial optimization problem, via elementary considerations including driver-passenger relationships and route planning. In this paper, we further take into consideration the time factor of the Carpool Service Problem by both the appearance time and the endurance time. This transforms the Carpool Service Problem into the Carpool Service Problem with Time Windows, which disfavors carpool solutions in which people show up with a time-out. An Ant Path-oriented Carpooling Allocation approach is proposed based on Ant Colony Optimization to solve the Carpool Service Problem with Time Windows in the time domain. The experimental section presents the experimental environment and computational results for the proposed Ant Path-oriented Carpooling Allocation approach and three compared approaches, including the Assignment-based Ant Colony Optimization, Genetic Algorithm, and Simulated Annealing. Here, we are interested in comparing the performance of Ant Path-oriented Carpooling Allocation approach, Assignment-based Ant Colony Optimization, Genetic Algorithm, and Simulated Annealing with two types of solution representations - i.e., Path-based and Assignment-based structure. For each tested benchmark and approach, we compare the values of two objective functions: primary and secondary objective functions. The values computed by each objective function are observed in the experimental section, and it will be apparent that our proposed Ant Path-oriented Carpooling Allocation approach obtains notable performance results against the others.
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