Multi-resource scheduling and routing for emergency recovery operations

Area of Science:

• Operations Research
• Disaster Management
• Logistics

Background:

• Effective emergency resource delivery is vital during disasters, yet existing research often focuses on single resources.
• The complexity of delivering multiple resource types (expendable and non-expendable) requires specialized models.

Purpose of the Study:

• To formulate the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief operations.
• To develop and evaluate a solution framework for efficient multi-resource delivery during disaster recovery.

Main Methods:

• Formulation of the Multi-Resource Scheduling and Routing Problem (MRSRP).
• Development and benchmarking of six heuristic methods: Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and Clustering.
• Comparison against exact methods for small instances and genetic algorithms for large instances.

Main Results:

• All six developed heuristic methods demonstrated validity, achieving near or optimal solutions for small problem instances.
• For large-scale instances, the methods provided near-optimal solutions within practical computational time limits.
• The Monte Carlo algorithm was identified as the most effective among the tested methods.

Conclusions:

• The proposed MRSRP model offers a robust framework for planning emergency relief logistics.
• The Monte Carlo method is a highly effective tool for optimizing resource deployment in disaster recovery.
• Findings support improved decision-making for resource allocation during critical emergency operations.