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A stochastic multi-objective optimization decision model for energy facility allocation: a case of liquefied petroleum gas station

  • Ming-Der Yang
  • Yi-Ping Chen
  • Chien-Tsung Wang
  • Ming-Jay Deng
  • Yu-Hao LinEmail author
  • Ho-Wen Chen
Original Paper
  • 14 Downloads

Abstract

To mitigate air pollution problem, the government has been planning to build more liquefied petroleum gas stations to motivate drivers to use liquefied petroleum gas vehicles in Taiwan. Such facility allocation problem is a multi-objective optimization process considering spatial variation in the need of refueling. This study presents a stochastic multi-objective optimization model for liquefied petroleum gas station allocation (SMOMLSA) that integrates a nondominated sorting genetic algorithm II with a Monte Carlo simulation to optimally allocate liquefied petroleum gas stations according to three trade-off objectives, including investment performance, energy conversion, and business opportunity. Monte Carlo simulation procedure generates the starting location of a taxicab car in need of refueling in the spatial grid based on a probability distribution. Nondominated sorting genetic algorithm II resolves the station location problem with these multi-objectives. The SMOMLSA was validated by conducting a real-world case study. Result depicts that the SMOMLSA can provide information on the optimal allocation of liquefied petroleum gas stations for minimizing construction costs, minimizing average refueling distance for vehicles, and maximizing potential customers.

Graphic abstract

Keywords

Monte Carlo simulation (MCS) Stochastic multi-objective optimization Nondominated sorting genetic algorithm II Alternative energy 

Notes

Acknowledgements

This work was supported in part by the Ministry of Science and Technology of Taiwan under Project MOST108-2218-E-224 -004 -MY3 and the Ministry of Education of Taiwan under the Higher Education Sprout Project. We thank to Bill Thornton who provided the editorial assistance and English language editing.

Compliance with ethical standards

Conflicts of interest

The authors declare no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil Engineering, and Innovation and Development Center of Sustainable AgricultureNational Chung Hsing UniversityTaichungTaiwan
  2. 2.Pervasive AI Research (PAIR) LabsHsinchuTaiwan
  3. 3.Department of Business AdministrationDa-Yeh UniversityChanghuaTaiwan
  4. 4.Department of Chemical and Materials EngineeringNational Yunlin University of Science and TechnologyDouliou, YunlinTaiwan
  5. 5.Bachelor Program in Interdisciplinary StudiesNational Yunlin University of Science and TechnologyDouliou, YunlinTaiwan
  6. 6.Department of Environmental Science and EngineeringTunghai UniversityTaichungTaiwan

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