Abstract
In this paper, we have formulated a mixed-integer non-linear programming model for alternative-fuel station location problem in which each station can fail with a site-specific probability. The model aims to maximise the total expected traffic volume that can be refuelled by the unreliable alternative-fuel stations. Based on the linearisation techniques, i.e., probability chains and piecewise-linear functions, we linearise the non-linearity of compound probability terms in the non-linear model to solve this problem efficiently. An efficient Tabu search algorithm is also developed to solve the large-size instances. In addition, we extend the model to deal with reliable multi-period alternative-fuel station network design. Computational experiments, carried out on the well-known benchmark instances where the probability of station failures is uniformly generated, show that the proposed models and algorithm can obtain the optimal solutions within a reasonable computation time. Compared to a standard station location model that disregards the potential for station failures, our model designs more reliable alternative-fuel station network under risk of station failures. A sensitivity analysis of failure probabilities in the station network design is investigated to demonstrate the robustness of our model and study how variability in the probability of station failure affects solution robustness.
Highlights
The problem of alternative-fuel station location is a recent, but very applicable research topic within location science
In this paper, we have formulated a mixed-integer non-linear programming model for alternative-fuel station location problem in which each station can fail with a site-specific probability
We propose a mixed-integer non-linear programming (MINLP) model to optimally locate the alternative-fuel stations whose failure probability are given initially
Summary
Annals of Operations Research (2019) 279:151–186 importance of the alternative-fuel station location problem. Li et al (2016) develop a multi-period multi-path refueling location model, formulated as a mixed-integer linear program, to expand public electric vehicle charging network to serve growing intercity trips. Jung et al (2014) consider itinerary interception (instead of flow-interception), stochastic demand (known as dynamic service requests) and queuing delay for electric car recharging facility location problem in South Korea Their model and solution method are constructed on a bi-level, simulation-optimisation framework that combines an upper level multiple-server allocation model with queuing delay and a lower level dispatch simulation. Hosseini and MirHassani (2015) build a two-stage stochastic mixed-integer programming model for refueling station location problem (including uncapacitated and capacitated) under uncertainty of traffic flows. We propose a mixed-integer non-linear programming (MINLP) model to optimally locate the alternative-fuel stations whose failure probability are given initially.
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