**5. Conclusions**

Our study is first of its kind to address disaster mitigation in general and resource allocation, in particular, using ABM for analysis of disaster management strategies in urban locality of Pakistan. We used two different algorithms; HSL and FCFS, both have slightly different performance based on allocation. We tested our proposed system on multiple union councils of Rawalpindi city. This scenario shows a significant result for both allocation algorithms. While comparing the results of the FCFS algorithm with the HSL algorithm, we can see that HSL provides better RA results. The degree to which demand requests and resources react to wait time is fundamental. If number of demands is higher, then overall wait time increases, and a lot of demands remain unallocated during their execution time. In future, more factors like damage due to late arrival, partial allocation, traffic, damage caused, public response, etc., are to be introduced in the implementation of this model to enhance the realization of simulation.

**Author Contributions:** Conceptualization, A.M.; methodology, A.M. and Z.u.A.U.; Software A.M. and Z.u.A.U.; validation, A.M. and F.A.; formal analysis, A.M. and Z.u.A.U.; investigation, A.M. and Z.u.A.U.; data curating, A.M., F.A. and A.R.; writing original draft, A.M. and Z.u.A.U.; writing review and editing, A.M., F.A. and A.R.; visualization, F.A. and A.R.; supervision, A.M.; project administration, A.M. and F.A.; funding acquisition, A.M., Z.u.A.U., F.A. and A.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This article is supported by National University of Science and Technology (NUST), Pakistan.

**Acknowledgments:** We thanks to anonymous referees for improving our paper.

**Conflicts of Interest:** We have no conflict of Interest.

## **References**


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