Surrogate-based optimization approach for capacitated hub location problem with uncertainty

Abstract

CJ Logistics has started to consider opening single new hub facility to expand the current transportation network system. This naturally leads to formulating a research question “where should the new hub facility be located in South Korea to minimize total transportation cost of the network system operated by the company?”. This research aims to answer the question by proposing a surrogate-based optimization approach. In addition to finding an optimal location of the new hub facility, this research performs sensitivity analysis to study the correlation between hub capacity (i.e., the source of uncertainty) and transportation cost. The results indicate that (1) total transportation cost after the establishment of the new hub facility at the optimal location is reduced by approximately 14% compared to the current transportation network system and (2) the currently operated hub facility located in Daejeon has the greatest influence on total transportation cost; while the existing hub facilities located in Cheongwon, Yongin, and Gunpo have little impact on total transportation cost after the construction of the new hub facility. It is expected that the outcome of this research helps the company systematically manage the transportation network system when the new hub facility is constructed.

Keywords:

• Capacitated hub location problem
• uncertainty
• design of experiment
• linear programming
• machine-learning-based surrogate modeling
• Monte-Carlo simulation
• sensitivity analysis

Acknowledgements

This research is an extension of a Ph.D. summer internship project that was done at CJ Logistics in 2018. We would like to thank Byungdo Lee, Wansik Kim, and Junghoon Kim for their feedback on this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data are not publicly available due to restrictions (e.g., they contain company-sensitive information).

Additional information

Funding

No specific funding is granted for this research.

Notes on contributors

Junghyun Kim

Junghyun Kim is an assistant professor in the School of Applied Artificial Intelligence at Handong Global University and the director of Engineering Systems Design Laboratory. Prior to joining Handong Global University, he worked at American Airlines located in Dallas, Texas, as a full-time operations research analyst. He earned his Ph.D. in Computational Science and Engineering from the Georgia Institute of Technology. His research focuses on integrating three different areas of specialization (i.e., machine learning, optimization, and advanced design methods) and utilizing them to solve real-world problems in various engineering fields.