Medical Supply Transportation Scheduling in Pandemic

Authors

  • Rahmin, N. A. A Universiti Putra malaysia
  • Shamsudin, W. A. W. A Universiti Putra Malaysia
  • Hasim, R.M Universiti Putra Malaysia https://orcid.org/0000-0002-5358-587X

DOI:

https://doi.org/10.58915/amci.v13iNo.1.252

Abstract

The COVID-19 pandemic has brought the world to its knees, with healthcare systems struggling to cope with the surge in demand for medical supplies. One of the major challenges faced by healthcare providers has been the transportation of essential medical supplies from manufacturers to hospitals and clinics. The pandemic has exposed the weaknesses in our supply chain systems and has highlighted the need for a more resilient and efficient transportation network. This project aims to investigate the medical supply problem during the pandemic, with a focus on transportation. It uses the Simple Heuristic Method and C programming language. The data generated using exponential distribution. The result shows that the application of the Simple Heuristic Method can minimize and optimize transportation time, providing a solution to the medical supply problem during pandemics. This project examine the challenges faced by healthcare providers in sourcing and transporting essential medical supplies and the impact of the pandemic on transportation networks. The results of this research provide valuable insights into the medical supply problem during pandemics and help inform the development of more effective transportation systems for the healthcare industry.

Author Biographies

Shamsudin, W. A. W. A, Universiti Putra Malaysia

This stiudent, contribute as a student final year  that done this research as a final year project at 2023.

Hasim, R.M, Universiti Putra Malaysia

Dr Risman is in the same group of this project, give the ideas for this problem

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Published

2024-02-14

How to Cite

Ab Rahmin, N. A., Wan Ahmad Shamsudin, W. A., & Mat Hashim, R. (2024). Medical Supply Transportation Scheduling in Pandemic. Applied Mathematics and Computational Intelligence (AMCI), 13(No.1), 84–108. https://doi.org/10.58915/amci.v13iNo.1.252