Multi-resource scheduling and routing for emergency recovery operations

Abstract

Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and Clustering, are developed and benchmarked against the exact method (for small instances) and the genetic algorithm (for large instances). Results reveal that all six heuristics are valid and generate near or actual optimal solutions for small instances. With respect to large instances, the developed methods can generate near-optimal solutions within an acceptable computational time frame. The Monte Carlo algorithm, however, emerges as the most effective method. Findings of comprehensive comparative analysis suggest that the proposed MRSRP model and the Monte Carlo method can serve as a useful tool for decision-makers to better deploy resources during emergency recovery operations.

Keywords: Clustering algorithm; Coronavirus outbreak case; Emergency recovery operations; Expendable resources; Heuristics algorithms; Multi-resource scheduling; Non-expendable resources.

Fig. 1

k-Node Crossover algorithm.

Fig. 2

Percentage of improvement for developed heuristics.

Fig. 3

Comparison between the computational time of (a) heuristics, and (b) Procedure 1 (Greedy + Augmented Greedy + Min(K-Node Crossover) and Procedure 2 (Scheduling + Monte Carlo).

Fig. 4

Case study area- Greater Melbourne metropolitan area.