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A new robust optimization model for relief logistics planning under uncertainty: a real-case study

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Abstract

As natural disasters often cause catastrophic damages, disaster management must be considered. Relief logistics planning in disaster management is a dynamic process that includes deliberate processes taken before and after a disaster. This paper presents a robust optimization model for relief logistics planning covering before and after disaster actions to minimize the total cost. In this model, demand and traveling time between nodes are non-deterministic parameters, and scenario-based robust optimization is applied to handle uncertainty. The model is an integrated multi-echelon, multi-period, and multi-commodity vehicle routing problem considering the fair distribution of goods and services in a way that social costs are minimized. Vehicles can enter nodes and exit them several times. In this paper, a genetic algorithm (GA) is considered the solution approach. To estimate the GA parameters, an experimental design based on the Taguchi method is considered. A roulette wheel method is applied to select a child from parents for the next generation. Single-point crossover and swap mutation operators are used to generate the new child. Test problems in different scales are generated and solved using GAMS and GA. The results indicate that GA solutions have 3.75% gaps in average with optimal solutions. Finally, the model is applied with the data of a real case (Tehran). The results also indicate that it is better to consider the appropriate capacity for supply centers and distributors and select vehicles.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy/ethical restrictions.

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Acknowledgements

The authors would like to thank the Editor-in-Chief and autonomous reviewers for their helpful comments and suggestions, which greatly improved the presentation of this paper. Also, the support of the Iranian Operations Research Society is highly acknowledged by the third author, as the board member of this society.

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Authors and Affiliations

  1. Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Abolfazl Aliakbari

  2. Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

    Alireza Rashidi Komijan

  3. School of Industrial Engineering, and Research Institute of Energy Management and Planning, College of Engineering, University of Tehran, Tehran, Iran

    Reza Tavakkoli-Moghaddam

  4. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Esmaeil Najafi

Authors
  1. Abolfazl Aliakbari
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  2. Alireza Rashidi Komijan
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  3. Reza Tavakkoli-Moghaddam
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  4. Esmaeil Najafi
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Contributions

All authors (AA, ARK, RT-M, EN) have contributed to all parts of this research including formal analysis; conceptualization; writing—original draft; visualization; methodology; software; validation; data collection and investigation; and writing—review and editing. Also, RT-M has the role of supervision and project administration.

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Correspondence to Reza Tavakkoli-Moghaddam.

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Aliakbari, A., Rashidi Komijan, A., Tavakkoli-Moghaddam, R. et al. A new robust optimization model for relief logistics planning under uncertainty: a real-case study. Soft Comput 26, 3883–3901 (2022). https://doi.org/10.1007/s00500-022-06823-4

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