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• Main Authors: Ming-hung Wang, 王銘鴻
• Other Authors: Shang-yao Yan
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/93537128554978681951

碩士 === 國立中央大學 === 土木工程學系 === 102 === Due to the population growth and economic progress, the greenhouse effect is getting worse. In order to protect the environment, governments push not only the public transport policy, but also the bike-sharing system. In practice, the decision maker is used to deploy the rental bicycles based on his/her experience, which lakes the perspective of system optimization, in addition to wasting of resources. In actual operations, the demand is wildly changed leading to the deployment of rental bicycles that loses its optimality. Therefore, this research considers the stochastic demand occurring in actual operations, with the aim of maximizing profits and service volume, to construct two rental bike deployment models. With thesse models, the operator can effectively deploy the retinal bikes and improve the level of service and operating profits. In this research, the time-space network flow technique is used to show the potential movement of rental bikes under stochastic demand and to construct two stochastic demand and deployment models. We further consider the average demand to construct two deterministic demand models. These four models are formulated as integer multiple-commodity network flow problems, which are characterized as NP-hard. We utilize C computer language, coupled with the CPLEX mathematics programming solver, to solve the two deterministic models. For the two stochastic models, since their problem sizes are too huge to be directly solved by using mathematical programming software. Therefore, we developed a solution algorithm to efficiently solve the two stochastic models. We also utilized EVPI and VSS to evaluate the performance of these two stochastic models. Finally, we performed a case study using data from a bicycle rental corporation. The test results show that the proposed models and solution algorithm could be useful for deploying the rental bicycles.