@article{ref1,
title="Multi-resource scheduling and routing for emergency recovery operations",
journal="International journal of disaster risk reduction",
year="2020",
author="Bodaghi, Behrooz and Shahparvari, Shahrooz and Fadaki, Masih and Lau, Kwok Hung and Palaneeswaran, Ekambaram and Chhetri, Prem",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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). <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="2212-4209",
doi="10.1016/j.ijdrr.2020.101780",
url="http://dx.doi.org/10.1016/j.ijdrr.2020.101780"
}