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Heuristic algorithms for siting alternative-fuel stations using the Flow-Refueling Location Model

机译:使用流量加油位置模型对替代燃料站进行选址的启发式算法

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This paper presents three heuristic algorithms that solve for the optimal locations for refueling stations for alternative-fuels, such as hydrogen, ethanol, biodiesel, natural gas, or electricity. The Flow-Refueling Location Model (FRLM) locates refueling stations to maximize the flow that can be refueled with a given number of facilities. The FRLM uses path-based demands, and because of the limitations imposed by the driving range of vehicles, longer paths require combinations of more than one station to refuel round-trip travel. A mixed-integer linear programming (MILP) version of the model has been formulated and published and could be used to obtain an optimal solution. However, because of the need for combinations of stations to satisfy demands, a realistic problem with a moderate size network and a reasonable number of candidate sites would be impractical to generate and solve with MILP methods. In this research, heuristic algorithms—specifically the greedy-adding, greedy-adding with substitution and genetic algorithm—are developed and applied to solve the FRLM problem. These algorithms are shown to be effective and efficient in solving complex FRLM problems. For case study purposes, the heuristic algorithms are applied to locate hydrogen-refueling stations in the state of Florida.
机译:本文提出了三种启发式算法,这些算法可为替代燃料(例如氢,乙醇,生物柴油,天然气或电力)的加油站求解最佳位置。流量加油位置模型(FRLM)可以定位加油站,以最大程度地利用给定数量的设施加油。 FRLM使用基于路径的需求,并且由于车辆行驶距离的限制,较长的路径需要多个站的组合才能为往返行程加油。该模型的混合整数线性规划(MILP)版本已制定并发布,可用于获得最佳解决方案。但是,由于需要结合站来满足需求,因此使用MILP方法生成和解决具有中等规模网络和合理数量的候选站点的现实问题是不切实际的。在这项研究中,开发了启发式算法,特别是贪婪添加,贪婪替换和遗传算法,并将其应用于解决FRLM问题。这些算法在解决复杂的FRLM问题上是有效的。为了进行案例研究,将启发式算法应用于佛罗里达州的加氢站的定位。

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