A Model with Solution Algorithms for Taxi Fleet Scheduling Incorporating Electric Vehicles

• Main Authors: Yu-Wei Huang, 黃宇威
• Other Authors: Shangyao Yan
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/40494026334578183553

碩士 === 國立中央大學 === 土木工程學系 === 104 === Taxis, which using fossil fuels as power, produces lots of carbon dioxide (CO2) during daily operations. Under the environmental protection trend in reducing CO2 emissions, electric vehicles that powered by electricity have been valued, and the usage of electric vehicles has also been raised in general commercial fleet in recent years. If the taxi fleet use electric vehicles instead of gasoline vehicles, CO2 emissions will be enormously reduced. However, before transferring all vehicles in a taxi fleet to be purely electric vehicles, it may need to go through a transition period of a mixed use of electric vehicles and gasoline vehicles due to the high purchasing price of electric vehicles. Nowadays, domestic taxi fleet routing/scheduling in above-mentioned transition period is mainly arranged in a manual way without system optimization, making the taxi fleet routing/scheduling inefficient. To our best knowledge, no literature is found on the routing/scheduling of a taxi fleet with mixed electric vehicles and gasoline vehicles. Therefore, considering that the taxi fleet was mixed with electric vehicles and gasoline vehicles, this study developed a scheduling model of taxi fleet by utilizing the time-space network flow technique and mathematical programming method. All reserved passenger demands must be satisfied by taxi fleet, the related operating constraints are ensured. The model was aimed to minimize the total operating cost and expected to be an effective planning tool to assist the carrier in routing/scheduling. Mathematically, the model was formulated as an integer network flow problem with side constraints. This study developed two solution algorithms based on the problem properties to efficiently solve the problem. Computational result of case study were given for evaluating the performance of the proposed model and solution algorithms. Finally, conclusions and suggestions made based on the computational results were given.