A bi-objective optimisation of post-disaster relief distribution and short-term network restoration using hybrid NSGA-II algorithm

Abstract

Humanitarian logistics research has recently received tremendous interest from researchers and practitioners due to its importance in assisting relief operations. While there is an increasing trend for mathematical models related to preparedness and response phases for disaster operations management, recovery-phase models are not as emphasised as other phases due to scarce data and model complication from NP-hard nature of the models. One particular approach that can provide a sufficiently good solution for the NP-hard problems is the metaheuristic approach. In this research, we explore the bi-criteria integrated response and recovery model for making strategic post-disaster decisions in the relief distribution and short-term network restoration. Next, with a focus on considering conflicting objectives between fairness and cost of this problem, we propose a hybrid approach with its evolutionary component based on the non-dominated sorting genetic algorithm-II (NSGA-II) called HNSGA-II. The proposed HNSGA-II is compared against the exact method using the approximate Pareto-front analysis. The proposed algorithm is verified using a case study from a risk assessment tool called Hazus to illustrate how to cope with the aftermath of an earthquake. Finally, results are evaluated using a Hypervolume-based technique and computation time to illustrate the efficiency of the proposed algorithm.

KEYWORDS:

• Humanitarian logistics
• short-term restoration
• bi-objective programming
• metaheuristic
• non-dominated sorting genetic algorithm-II
• hypervolume

Acknowledgement

The authors are grateful and would like to thank the three anonymous reviewers for their valuable comments and suggestions that help us improve our research presentation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Kasin Ransikarbum

Kasin Ransikarbum received the B.Eng., M.S., and Ph.D. Degrees in Industrial Engineering from King Mongkut's University of Technology Thonburi; Pennsylvania State University, U.S.A.; and Clemson University, U.S.A., respectively. He was also a postdoctoral researcher at Ulsan National Institute of Science and Technology, Republic of Korea. Currently, he works at the Industrial Engineering, Ubonratchathani University, Thailand. His research interest includes supply chain modelling, humanitarian logistics, and applied optimisation and simulation modelling.

Scott J. Mason

Scott J. Mason spent 20 years in academia at the University of Arkansas (2000–2010) and most recently at Clemson University (2010–2020) where he served as the inaugural Fluor Endowed Chair in Supply Chain Optimisation and Logistics and a Professor of Industrial Engineering. Dr Mason now uses operations research techniques to model and analyse large-scale supply chain and facility logistics challenges for Amazon. He received his Ph.D. in Industrial Engineering from Arizona State University after earning BS and MS degrees from The University of Texas at Austin. He is a Fellow of the Institute of Industrial and Systems Engineers and a member of INFORMS.