Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters
Socio-Economic Planning Sciences, cilt.74, 2021 (SCI-Expanded, SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 74
- Basım Tarihi: 2021
- Doi Numarası: 10.1016/j.seps.2020.100933
- Dergi Adı: Socio-Economic Planning Sciences
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, International Bibliography of Social Sciences, Business Source Elite, Business Source Premier, EconLit, Educational research abstracts (ERA), INSPEC, Political Science Complete, Public Affairs Index, Social services abstracts, Sociological abstracts, Worldwide Political Science Abstracts
- Anahtar Kelimeler: Disaster relief logistics, Grasshopper optimization algorithm, Humanitarian supply chain, Optimization, Transportation and distribution planning
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Hayır
Özet
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.