Dynamic collaborative optimization for disaster relief supply chains under information ambiguity.

Area of Science:

• Operations Research
• Supply Chain Management
• Disaster Management

Background:

• Increasing frequency and severity of large-scale disasters worldwide present critical challenges for emergency supply chains.
• Existing optimization models often lack fault tolerance, hindering timely and effective distribution of relief materials.
• Uncertainty and ambiguity in disaster information complicate decision-making for relief operations.

Purpose of the Study:

• To develop a collaborative optimization model for disaster relief supply chains that addresses information uncertainty.
• To enhance the selection of optimal emergency material suppliers.
• To improve multi-objective fuzzy optimization for material distribution across different disaster phases.

Main Methods:

• Utilized Interval Type-2 Fuzzy Set (IT2TFS) to handle uncertainty and ambiguity in disaster relief information.
• Developed an integrative emergency material supplier evaluation framework using a multi-attribute group decision-making ranking method.
• Implemented multi-objective fuzzy optimization across three emergency phases: early, mid-, and late-disaster relief.

Main Results:

• The proposed model effectively optimizes emergency material planning and supplier selection.
• Ensured that reserve material safety inventory is consistently maintained at a reasonable level.
• Demonstrated the model's capability to prevent safety inventory shortages and minimize losses in disaster-affected areas through a case study in Yunnan Province.

Conclusions:

• The developed fuzzy optimization method provides a robust approach to managing disaster relief supply chains under uncertainty.
• The framework enhances decision-making for selecting suppliers and planning material distribution.
• The model contributes to minimizing losses by ensuring adequate safety inventory levels during critical relief phases.