Improving the performance of humanitarian logistics by model optimizing of chosen subsystems https://phaidrabg.bg.ac.rs/o:35638 eng All rights reserved Mijušković, Veljko M. Aćimović, Slobodan Marković, Dušan Milošević, Nikola info:eu-repo/semantics/conferenceProceedings EBES 49th EBES CONFERENCE - ATHENS : PROCEEDINGS - VOLUME II startpage: 1388 endpage: 1400 Keywords: humanitarian logistics, model optimization, subsystems, performance improvement, mathematical modeling. Abstract: Efficient humanitarian logistics is crucial for timely and effective response during crises. This paper investigates the enhancement of humanitarian logistics performance through model optimization of selected subsystems. The goal is to identify key subsystems within humanitarian logistics and apply optimization techniques to improve their performance metrics such as response time, resource allocation, and cost efficiency. Through a comprehensive review of existing literature and case study analysis, this study identifies critical subsystems including inventory management, transportation networks, and information systems. Each subsystem is modeled using optimization methods tailored to address specific challenges in humanitarian contexts, such as unpredictable demand and limited resources. Key findings highlight significant improvements in response times and resource utilization when subsystems are optimized individually and integrated within a holistic framework. However, the implementation of optimization models faces challenges related to data availability, accuracy, and contextual variability across different humanitarian crises. This research contributes to the field by demonstrating the potential of mathematical optimization in enhancing humanitarian logistics effectiveness. The findings underscore the importance of tailored solutions and the need for adaptive strategies that account for the dynamic and diverse nature of humanitarian operations. Future research should focus on addressing data limitations and further validating these optimization models in real-world humanitarian settings. application/pdf 1547918 bytes 2024