Browsing by Subject "Alternative fuel vehicles"

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Open Access
A branch and price approach for routing and refueling station location model
(Elsevier, 2016) Yıldız, B.; Arslan, O.; Karaşan, O. E.
The deviation flow refueling location problem is to locate p refueling stations in order to maximize the flow volume that can be refueled respecting the range limitations of the alternative fuel vehicles and the shortest path deviation tolerances of the drivers. We first provide an enhanced compact model based on a combination of existing models in the literature for this relatively new operations research problem. We then extend this problem and introduce the refueling station location problem which adds the routing aspect of the individual drivers. Our proposed branch and price algorithm relaxes the simple path assumption generally adopted in the existing studies and implicitly takes into account deviation tolerances without the pregeneration of the routes. Therefore, the decrease in solution times with respect to existing models is significant and our algorithm scales very efficiently to more realistic network dimensions.
Open Access
A branch-and-cut algorithm for the alternative fuel refueling station location problem with routing
(INFORMS, 2019) Arslan, O.; Karaşan, Oya Ekin; Mahjoub, A. R.; Yaman, Hande
Because of the limited range of alternative fuel vehicles (AFVs) and the sparsity of the available alternative refueling stations (AFSs), AFV drivers cooperatively deviate from their paths to refuel. This deviation is bounded by the drivers’ tolerance. Taking this behavior into account, the refueling station location problem with routing (RSLP-R) is defined as maximizing the AFV flow that can be accommodated in a road network by locating a given number of AFSs while respecting the range limitation of the vehicles and the deviation tolerance of the drivers. In this study, we develop a natural model for the RSLP-R based on the notion of length-bounded cuts, analyze the polyhedral properties of this model, and develop a branch-and-cut algorithm as an exact solution approach. Extensive computational experiments show that the algorithm significantly improves the solution times with respect to previously developed exact solution methods and extends the size of the instances solved to optimality. Using our methodology, we investigate the tradeoffs between covered vehicle flow and deviation tolerance of the drivers and present insights on deviation characteristics of drivers in a case study in California.