Browsing by Author "Yan, Shangyao"

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A Bilevel Model With a Solution Algorithm for Locating Weigh-In-Motion Stations
(2018-02-01)
Lu, Chung-Cheng
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Yan, Shangyao
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Ko, Hao-Chih
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Chen, Hui-Ju
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運輸與物流管理系註：原交通所+運管所
The proper location of weigh-in-motion (WIM) stations in road networks is critical to effectively reduce the impact of overweight trucks. Truckers may quickly learn the locations of WIM stations, and take detours to bypass these checkpoints, with the aid of advanced navigation systems. This reaction needs to he considered in the location models for the deployment of WIM systems. This paper proposes a bilevel model to deal with the WIM location problem in a road network. The model includes an upper level and multiple lower level models respectively, represent the decision-making behavior of the law enforcement officials and truckers for different origin?destination pairs. The upper level model determines the optimal number and locations of WIM stations so as to minimize the damage due to evasive overweight trucks, considering the truckers' route choices. The lower level models simulate the truckers' route choices in response to the WIM locations determined by the upper level model. A heuristic is developed to efficiently solve the bilevel model, as it is difficult to obtain the exact solution. The proposed model and heuristic are evaluated using a test instance generated based on the Nevada road network. The results show that the heuristic outperforms a classical approach based on k shortest paths.
A Dynamic and Flexible Berth Allocation Model with Stochastic Vessel Arrival Times
(2019-09-01)
Yan, Shangyao
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Lu, Chung-Cheng
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Hsieh, Jun-Hsiao
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Lin, Han-Chun
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運輸與物流管理系註：原交通所+運管所
This study proposes a berth-flow network modeling approach to deal with the dynamic berth allocation problem (DBAP) with stochastic vessel arrival times. In this approach, uncertain vessel arrival times are represented using discrete probability distributions and a flexible berth allocation scheme based on the blocking plan concept is incorporated into the model to effectively utilize wharf space. This new model is referred to as the stochastic dynamic (vessel arrival) and flexible (berth space) berth allocation problem (SDFBAP) model. The aim is to minimize the sum of the expected values of unanticipated schedule delay costs and the penalties for being unable to service all vessels within the planning horizon. The proposed model is formulated as an integer multi-commodity network flow problem which can be solved with off-the-shelf solvers. Computational experiments are conducted using a real example to demonstrate the effectiveness and efficiency of the SDFBAP model. A simulation-based approach is adopted to evaluate the SDFBAP model. A number of scenario analyses are also conducted to gain insight into important model parameters.
Optimal scheduling of a taxi fleet with mixed electric and gasoline vehicles to service advance reservations
(2018-08-01)
Lu, Chung-Cheng
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Yan, Shangyao
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Huang, Yu-Wei
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
This study addresses the problem of scheduling a fleet of taxis that are appointed to solely service customers with advance reservations. In contrast to previous studies that have dealt with the planning and operations of a taxi fleet with only electric vehicles (EVs), we consider that most taxi companies may have to operate with fleets comprised of both gasoline vehicles (GVs) and plug-in EVs during the transition from GV to (complete) EV taxi fleets. This paper presents an innovative multi-layer taxi-flow time-space network which effectively describes the movements of the taxis in the dimensions of space and time. An optimization model is then developed based on the time-space network to determine an optimal schedule for the taxi fleet. The objective is to minimize the total operating cost of the fleet, with a set of operating constraints for the EVs and GVs included in the model. Given that the model is formulated as an integer multi-commodity network flow problem, which is characterized as NP-hard, we propose two simple but effective decomposition-based heuristics to efficiently solve the problem with practical sizes. Test instances generated based on the data provided by a Taiwan taxi company are solved to evaluate the solution algorithms. The results show that the gaps between the objective values of the heuristic solutions and those of the optimal solutions are less than 3%, and the heuristics require much less time to obtain the good quality solutions. As a result, it is shown that the model, coupled with the algorithms, can be an effective planning tool to assist the company in routing and scheduling its fleet to service reservation customers.
Stochastic fleet deployment models for public bicycle rental systems
(2018-01-01)
Yan, Shangyao
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Lu, Chung-Cheng
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Wang, Min-Hung
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運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This paper presents two stochastic bike deployment (SBD) models that determine the optimal number of bicycles allocated to each station in a leisure-oriented public bicycle rental system with stochastic demands. The SBD models represent the stochastic demands using a set of scenarios with given probabilities. A multilayer bike-flow time-space network is constructed for developing the models, where each layer corresponds to a given demand scenario and effectively describes bicycle flows in the spatial and temporal dimensions. As a result, the models are formulated as the integer multi-commodity network flow problem, which is characterized as NP-hard. We propose a heuristic to efficiently obtain good quality solutions for large-size model instances. Test instances are generated using real data from a bicycle rental system in Taiwan to evaluate the performance of the models and the solution algorithm. The test results show that the models can help the system operator of a public bicycle system make effective fleet deployment decisions.