A Literature Review on AI and DSS for Resilient and Sustainable Humanitarian Logistics
DOI:
https://doi.org/10.21512/ijcshai.v2i1.13028Keywords:
Artificial Intelligence, Decision Support Systems, Disaster Response, Humanitarian LogisticsAbstract
Disaster response is a critical component of disaster management, requiring effective strategies to reduce exposure and vulnerability to hazards. Rising global temperatures and extreme weather events have intensified the need for adaptive disaster relief systems. Humanitarian logistics, a vital subset of the supply chain, plays a central role in disaster preparedness, response, and recovery phases but often faces challenges such as resource constraints, inefficient communication, and unpredictable crises. This study employs a systematic literature review (SLR) using the PRISMA methodology to explore the application of Artificial Intelligence (AI) and Decision Support Systems (DSS) in humanitarian logistics from 2019 to 2024. SCOPUS served as the primary database, identifying 1,171 documents, with 52 studies selected for in-depth analysis. These studies highlight the potential of AI techniques, including machine learning and clustering algorithms, and DSS implementations for resource allocation, stakeholder coordination, and real-time decision- making. Findings demonstrate that integrating AI and DSS can optimize emergency vehicle routing, improve relief distribution, and enhance stakeholder collaboration. Advanced technologies such as Radio Frequency Identification (RFID), the Internet of Things (IoT), and Digital Twins improve logistics efficiency and scalability. Despite these advancements, challenges like data integration and algorithmic reliability persist. The study recommends prioritizing transparent systems, hybrid simulations, and addressing algorithmic constraints to advance disaster management practices.
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