A location model for capacitated alternative-fuel stations with uncertain traffic flows. (July 2020)

• Record Type: Journal Article
• Authors: Cheng, Yu; Wang, Li
• Is Part Of: Computers & industrial engineering. Volume 145(2020)
• Publication Date: 2020-07
• Subjects: Stochastic chance-constrained programming model -- Alternative-fuel station -- Optimal location -- Road condition -- Fuel capacity; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106486
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A new method is presented to verify the feasibility of station combination. Road condition is first taken into account as a factor that influences vehicle range. A stochastic chance-constrained programming model is proposed. Abstract: To maximize the total amount of alternative fuel consumption in a traffic network and satisfy the travel demands to the greatest extent possible, this paper addresses the problem of finding the optimal locations in a transportation network in which to construct capacitated alternative-fuel stations with a limited construction budget. Different from most existing studies that treat traffic flows in each origin-destination pair as a constant, a stochastic chance-constrained programming model (SCCPM) is proposed to characterize the problem of interest in this paper, where random variables are employed to capture the uncertainty of traffic flows. Moreover, for solution convenience, a linear equivalent model of the proposed one is deduced. Finally, two sets of numerical experiments are implemented by the Cplex solver to test the performance of the model. Firstly, a series of experiments are conducted on the small scale network to analyze the sensitivities of the parameters in the proposed model. Then we find the optimal locations for alternative-fuel stations in the large-scale network and draw the conclusion that most of the alternative-fuel vehicles are located in demands cluster districts according to the thermodynamic diagram.