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Alternative fuel station location model with demand learning

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  • Shahzad Bhatti
  • Michael Lim
  • Ho-Yin Mak

Abstract

In this paper, we study the optimal location decision for a network of alternative fuel stations (AFS) servicing a new market where the demand rate for the refueling service can be learned over time. In the presence of demand learning, the firm needs to make a decision, whether to actively learn the market through a greater initial investment in the AFS network or defer the commitment since an overly-aggressive investment often results in sub-optimal AFS locations. To illustrate this trade-off, we introduce a two-stage location model, in which the service provider enters the market by deploying a set of stations in the first stage under uncertainty, and has the option to add more stations in the second stage after it learns the demand. The demand learning time (length of the first stage) is endogenously determined by the service provider’s action in the first stage. To solve this problem, we develop an efficient solution method that provides a framework to achieve a desired error rate of accuracy in the optimal solution. Using numerical experiment, we study the trade-off between active learning and deferred commitment in AFS deployment strategy under different market characteristics. Further, we find that the lack of planning foresight typically results in an over-commitment in facility investment while the service provider earns a lower expected profit. Copyright US Government 2015

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  • Shahzad Bhatti & Michael Lim & Ho-Yin Mak, 2015. "Alternative fuel station location model with demand learning," Annals of Operations Research, Springer, vol. 230(1), pages 105-127, July.
  • Handle: RePEc:spr:annopr:v:230:y:2015:i:1:p:105-127:10.1007/s10479-014-1530-9
    DOI: 10.1007/s10479-014-1530-9
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    3. Trung Hieu Tran & Thu Ba T. Nguyen, 2019. "Alternative-fuel station network design under impact of station failures," Annals of Operations Research, Springer, vol. 279(1), pages 151-186, August.
    4. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Ekşioğlu, Sandra D. & Castillo-Villar, Krystel K., 2021. "Designing a reliable electric vehicle charging station expansion under uncertainty," International Journal of Production Economics, Elsevier, vol. 236(C).
    5. Derya Celik Turkoglu & Mujde Erol Genevois, 2020. "A comparative survey of service facility location problems," Annals of Operations Research, Springer, vol. 292(1), pages 399-468, September.
    6. Cilio, Luca & Babacan, Oytun, 2021. "Allocation optimisation of rapid charging stations in large urban areas to support fully electric taxi fleets," Applied Energy, Elsevier, vol. 295(C).
    7. Nie, Yu (Marco) & Ghamami, Mehrnaz & Zockaie, Ali & Xiao, Feng, 2016. "Optimization of incentive polices for plug-in electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 103-123.
    8. Ventura, Jose A. & Kweon, Sang Jin & Hwang, Seong Wook & Tormay, Matthew & Li, Chenxi, 2017. "Energy policy considerations in the design of an alternative-fuel refueling infrastructure to reduce GHG emissions on a transportation network," Energy Policy, Elsevier, vol. 111(C), pages 427-439.
    9. Tran, Trung Hieu & Nagy, Gábor & Nguyen, Thu Ba T. & Wassan, Niaz A., 2018. "An efficient heuristic algorithm for the alternative-fuel station location problem," European Journal of Operational Research, Elsevier, vol. 269(1), pages 159-170.

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