Designing a resilient humanitarian supply chain by considering viability under uncertainty: A machine learning embedded approach

Humanitarian supply chains (HSCs) play a crucial role in mitigating the impacts of natural disasters and preventing humanitarian crises. Designing resilient HSCs is critically important to ensure effective recovery and long-term sustainability during and after such events. This study addresses the design of resilient HSCs with viability consideration under known-unknown demand and capacity uncertainties by formulating a two-stage stochastic programming model. To solve this problem and achieve high-quality solutions, three solution approaches are developed and compared. The first approach introduces risk aversion into a genetic algorithm (GA) through chance constraints, termed GA with chance constraints (GAC). The other two approaches integrate the Random Forest (RF) algorithm with GAC, employing incremental learning (GACRFI) and non-incremental learning (GACRFNI). To evaluate the performance of these algorithms and provide insights into designing a resilient HSC, a full factorial design of experiments (DoE) is established using controllable factors. Problems are generated for three cases, each of which corresponds to a distinct disruption and ripple effect severity degree. Computational analysis shows that integrating the machine learning algorithm into the GA yields superior results across all risk level settings, leading to a win–win situation for all stakeholders in HSCs. This study provides valuable insights for designing resilient HSCs that ensure both short-term recovery and long-term sustainability by considering viability under varying risk levels and severity degrees.

• Record URL:
  • https://doi.org/10.1016/j.tre.2024.103943
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1366554524005349
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yılmaz, Ömer Faruk
  • 0000-0002-6202-4011
  • Guan, Yongpei
  • 0000-0002-6587-7730
  • Yılmaz, Beren Gürsoy
  • 0000-0001-7493-0639
• Publication Date: 2025-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 103943
• Serial:
  • Transportation Research Part E: Logistics and Transportation Review
  • Volume: 194
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1366-5545
  • Serial URL: http://www.sciencedirect.com/science/journal/13665545

Subject/Index Terms

• TRT Terms: Disaster relief; Disaster resilience; Genetic algorithms; Machine learning; Supply chain management; Uncertainty
• Subject Areas: Data and Information Technology; Freight Transportation; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 01943665
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 27 2025 11:34AM