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A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty

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Abstract

Humanitarian relief logistics is one of the most important elements of a relief operation in disaster management. The present work develops a multi-objective robust stochastic programming approach for disaster relief logistics under uncertainty. In our approach, not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands might arise and the possibility that some of the pre-positioned supplies in the relief distribution center or supplier might be partially destroyed by the disaster. Our multi-objective model attempts to minimize the sum of the expected value and the variance of the total cost of the relief chain while penalizing the solution’s infeasibility due to parameter uncertainty; at the same time the model aims to maximize the affected areas’ satisfaction levels through minimizing the sum of the maximum shortages in the affected areas. Considering the global evaluation of two objectives, a compromise programming model is formulated and solved to obtain a non-dominating compromise solution. We present a case study of our robust stochastic optimization approach for disaster planning for earthquake scenarios in a region of Iran. Our findings show that the proposed model can help in making decisions on both facility location and resource allocation in cases of disaster relief efforts.

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Authors and Affiliations

  1. Department of Industrial Engineering, Iran University of Science and Technology, Narmak, 1684613114, Tehran, Iran

    Ali Bozorgi-Amiri, M. S. Jabalameli & S. M. J. Mirzapour Al-e-Hashem

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  1. Ali Bozorgi-Amiri
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  2. M. S. Jabalameli
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  3. S. M. J. Mirzapour Al-e-Hashem
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Correspondence to Ali Bozorgi-Amiri.

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Bozorgi-Amiri, A., Jabalameli, M.S. & Mirzapour Al-e-Hashem, S.M.J. A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum 35, 905–933 (2013). https://doi.org/10.1007/s00291-011-0268-x

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