Stratified delivery aid plans for humanitarian aid distribution centre selection. (September 2022)

• Record Type: Journal Article
• Authors: Nawazish, Mohammed; Padhi, Sidhartha S.; Edwin Cheng, T.C.
• Is Part Of: Computers & industrial engineering. Volume 171(2022)
• Publication Date: 2022-09
• Subjects: Humanitarian aid distribution centre -- Delivery aid plans -- Stratification -- Multi-criterion decision-making -- Contingency theory; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108451
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A holistic approach integrating delivery aid plans in HADC selection decision. Prioritization by relief items, speed of delivery, and disaster-affected locations. Contingency theory to view a coherent structure of the HADC selection decision-making. We propose an algorithm for the stratified MCDM model for HADC selection decision. The model is depicted in the context of Cyclone Fani. Abstract: Humanitarian aid distribution centres (HADCs) are essential for bridging the gap between stranded beneficiaries and relief aid during a disaster. We incorporate three delivery aid plans (DAPs), namely prioritization by relief items, speed of delivery, and disaster location, into the decision to select HADCs. While anticipating decentralized relief aid supplies, humanitarian practitioners face uncertainties in HADC selection. Grounded in Contingency Theory, DAPs assist in anticipating the uncertain relief aid supplies contingent on the external environment. Hence, HADC selection must incorporate DAPs in pursuit of three performance criteria, namely efficiency, effectiveness, and equity. We propose a stratified multi-criterion decision-making (MCDM) approach for HADC selection in the post-disaster planning phase to counter the uncertainty of decentralized relief aid supplies. We perform numerical studies of the proposed dynamic model using the data on Cyclone Fani. The results show that HADC selection incorporating DAPs is more robust and impactful. We also conduct sensitivity analysis to examine the trade-offs between the performance criteria.