Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic

Abstract

The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient's condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.

Keywords: COVID-19; Epidemic outbreaks; Industry 4.0; SARS-COV-2; Supply chain design.

Fig. 1

(a) Total number of confirmed cases worldwide and (b) Infectious rate of SARS-COV-2 among all known viruses (www.who.int).

Fig. 2

Relief supply chain network capabilities.

Fig. 3

Scale and phases of the proposed IoT system.

Fig. 4

Step one of the proposed methodology.

Fig. 5

Step two of the proposed methodology.

Fig. 6

Description of proposed prioritizing approach.

Fig. 7

The descriptions of the proposed allocating approach.

Fig. 8

Pseudo-code of rescheduling in a real-time situation in prioritizing approach.

Fig. 9

Pseudo-code of rescheduling in a real-time situation in the allocating approach.

Fig. 10

The proposed encoding and decoding scheme.

Fig. 11

The pseudocode of the SA algorithm.

Fig. 12

The pseudocode of the SEO algorithm.

Fig. 13

The pseudocode of the PSO algorithm.

Fig. 14

The pseudocode of the proposed SAPSO algorithm.

Fig. 15

The pseudocode of the proposed SASEO algorithm.

Fig. 16

The pseudocode of the proposed PSOSEO algorithm.

Fig. 17

The mean RPD of each algorithm for prioritizing approach.

Fig. 18

The mean RPD of each algorithm for allocating approach.

Fig. 19

The location of Babol in Mazandaran province, Iran.

Fig. 20

The map of identified suspected cases.

Fig. 21

The directions of eight ambulances, based on prioritizing approach.

Fig. 22

The directions of eight ambulances, based on allocating approach.

Fig. 23

The direction of eight ambulances, based on prioritizing approach.

Fig. 24

The directions of eight ambulances, based on allocating approach.

Fig. 25

The number of received calls in three weeks.

Fig. 26

The number of face-to-face visitations in three weeks.

Fig. 27

The number of participants in the PE in three weeks.

Fig. 28

The number of confirmed cases in three weeks.

Fig. 29

The number of ambulances trips in three weeks.

Fig. 30

The number of identified confirmed cases by the PE in three weeks.

Fig. 31

The number of identified confirmed cases by other ways in three weeks.

Fig. 32

The comparison of identified confirmed cases in the PE and other ways in three weeks.