Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters
| Autor: | Seyed Reza Abazari, Masoud Rabbani, Amir Aghsami |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Economics and Econometrics
021103 operations research Humanitarian Logistics Flood myth Operations research Computer science Total cost business.industry Strategy and Management Supply chain 05 social sciences Geography Planning and Development 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Software 0502 economics and business Damages 050207 economics Statistics Probability and Uncertainty Natural disaster Fixed cost business |
| Zdroj: | Socio-Economic Planning Sciences. 74:100933 |
| ISSN: | 0038-0121 |
| DOI: | 10.1016/j.seps.2020.100933 |
| Popis: | Humans tackle natural disasters all over the world. Humanitarian supply chain plays an important role to mitigate damages occurred after a disaster. This research formulates a multi-objective Mixed-Integer Non-Linear programming (MINLP) with uncertain parameters considering Relief Centers (RC), Demand Points (DP) in affected areas, transportation methods to deliver Relief Items (RI) and different types of RIs namely, perishable and imperishable. In pre-disaster stage, location and number of RCs with their prepositioned inventory level are determined. After disaster strikes, based on a distribution plan the amount of RIs that should be transported to DPs and number of needed vehicles are determined. The objective functions minimize the total distance traveled by RIs, total costs (including RC establish cost, inventory cost, fixed cost for each vehicle type and acquisition cost for RIs), maximum traveling time between RCs and DPs and number of perished items respectively. The proposed model is solved by GAMS software for small size test problems and Grasshopper Optimization Algorithm (GOA) as a meta-heuristic approach for large size problems. Numerical and computational results are provided to prove the efficiency and feasibility of the presented model. Finally, the developed model is implemented to Iran's flood in 2019 as a case study. |
| Databáze: | OpenAIRE |
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