Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering

Abstract

In this paper, we are concerned with the problem of the ‘helicopters and vehicles’ intermodal transportation of medical supplies in response to large-scale disasters. To deal with the disadvantages of the use of classic Fuzzy C-Means (FCM) in the intermodal transportation optimization, two balanced FCM methods, i.e. FCM with capacity constraints and FCM with number constraints, are formulated to select emergency distribution centers (EDCs) and assign medical aid points, which could construct balanced ‘helicopters and vehicles’ intermodal transportation network. Then, considering helicopter travel time, transfer time and vehicle delivery time, a clustering-based intermodal routes optimization model is presented to produce intermodal transportation routes. Numerical experiments are presented to show the effectiveness and advantage of the developed approach, and observe the impact of number of EDCs and transfer efficiency at EDCs on the performance of intermodal transportation. This paper could provide methodological and operational supports for the ‘helicopters and vehicles’ intermodal transportation of medical supplies in response to large-scale disasters.

Keywords:

• large-scale disasters
• medical supplies
• intermodal transportation
• balanced fuzzy clustering

Acknowledgements

The authors gratefully acknowledge the associate editor and reviewers for their constructive comments which greatly improved the work.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 71471025], [grant number 71471158], [grant number 71531002]; the Hong Kong Scholars Program Mainland-Hong Kong Joint Postdoctoral Fellows Program [No. G-YZ87]; Fundamental Research Funds Program from Northwest A & F University [No. 2452015230]; Doctoral Scientific Research Foundation of Northwest A & F University [No. 2452015325].