Developing a bi-objective resilience relief logistic considering operational and disruption risks: a post-earthquake...

Area of Science:

• Operations Research
• Supply Chain Management
• Disaster Management

Background:

• Disasters worldwide necessitate improved relief logistics planning.
• Humanitarian Relief Chains (HRC) require resilience against operational and disruption risks.
• Existing models often overlook uncertainty and cascading disaster effects.

Purpose of the Study:

• To develop a robust framework for humanitarian relief chains (HRC).
• To enhance supply delivery to beneficiaries by considering resilience and risks.
• To incorporate uncertainty and multiple disaster scenarios into relief logistics.

Main Methods:

• Utilized the Best Worst Method (BWM) to determine resilience parameters.
• Incorporated uncertainty across all stages of the relief logistics model.
• Employed metaheuristic algorithms: NSGA-II, NRGA, and MOPSO for optimization.
• Compared algorithm performance using multi-objective metrics.

Main Results:

• The proposed model demonstrates robustness in humanitarian relief chain operations.
• Effectively manages supply delivery under conditions of uncertainty and multiple disasters.
• Metaheuristic algorithms provide viable solutions for complex relief logistics problems.

Conclusions:

• The developed framework significantly improves the resilience of humanitarian relief chains.
• The approach offers a practical solution for real-world disaster relief challenges.
• Future research should explore further enhancements in dynamic disaster response strategies.