A multi-commodity, multi-objective humanitarian supply chain model for disaster relief

• Main Authors: Averia, Paula Bianca C., Chan, Denis L., Felix, Nathaniel Isidore B.
• Format: text
• Language: English
• Published: Animo Repository 2008
• Subjects: Humanitarianism; Humanitarian assistance; Logistics; Disaster relief
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/7406
• Institution: De La Salle University

This study explores the modeling of a supply chain specifically designed for humanitarian relief. Several supply chain models are reviewed as well as literature on humanitarian supply chains. One of the many differentiating factors of other supply chain models with the model under study is that costs are not a major factor to be considered in a humanitarian supply chain. Cost minimization alone cannot be used in a humanitarian supply chain because lives of human beings cannot be measured by costs. Another unique feature of a humanitarian supply chain is the inapplicability of backorder costs since lives are at stake. Timely response is necessary for such a supply chain. There is also a need to develop scenarios to prepare for the highly unpredictable occurrences of disasters. A multi-period, multi-product strategic planning supply chain model for humanitarian disaster relief with stochastic demand was formulated. The model explores on the use of depots and staging areas while maximizing coverage area and at the same time minimizing costs. Focus is given to the magnitude and levels of disasters while taking into account different costs such as setup costs, holding costs, transportation costs, and costs of acquiring items. The model dictates the depots and staging areas to be utilized, the quantity of item to be delivered, amount to be held at depots, and time period to be delivered. The model was run using General Algebraic Mathematical Model System (GAMS). The model was able to capture the expected behavior, and was able to serve all relief camps even if doing so would be more costly due to its humanitarian nature. Significant factors found through Design of Experiments (DOE) were demand, safety stock levels, depot capacity, depot setup costs, lower response time limit, and coverage level weight. Response Surface Methodology (RSM) was used to evaluate these significant parameters. It was found that demand and safety stock levels both drive costs up. A higher percentage of safety stock level, higher coverage level weight, and higher lower response time limit results to a high coverage benefit. It is recommended that for future studies, heuristics for implementing the results of the model be developed. It is also recommended to incorporate a post-disaster strategy to the model. More sophisticated inventory policies may be explored for relief supply chains for quicker response. In this study, some input parameters were only estimates due to limitations. Future efforts in data collection would enhance the analysis of this study.