Designing a new sustainable Test Kit supply chain network utilizing Internet of Things

Ali Navaei; Ata Allah Taleizadeh; Fariba Goodarzian

doi:10.1016/j.engappai.2023.106585

Designing a new sustainable Test Kit supply chain network utilizing Internet of Things

Eng Appl Artif Intell. 2023 Sep:124:106585. doi: 10.1016/j.engappai.2023.106585. Epub 2023 Jun 21.

Authors

Ali Navaei¹, Ata Allah Taleizadeh¹, Fariba Goodarzian^{2

3}

Affiliations

¹ School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
² Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259, Auburn, WA 98071, USA.
³ Organization Engineering Group, School of Engineering, University of Seville, Camino de los Descubrimientos s/n, 41092 Seville, Spain.

Abstract

The advent of COVID-19 put much economic pressure on countries worldwide, especially low-income countries. Providing test kits for Covid-19 posed a huge challenge at the beginning of the pandemic. Especially the low-income and less developed countries that did not have the technology to produce this kit and had to import it into the country, which itself cost a lot to buy and distribute these kits. This paper proposes a sustainable COVID-19 test kits supply chain network (STKSCN) for the first time to fill this gap. Distribution and transportation of test kits, location of distribution centers, and management of used test kits are considered in this network. A mixed integer linear programming Multi-Objective (MO), multi-period, multi-resource mathematical model is extended for the proposed supply chain. Another contribution is designing a platform based on the Internet of Things (IoT) to increase the speed, accuracy and security of the network. In this way, patients set their appointment online by registering their personal details and clinical symptoms. An augmented $ɛ$ -constraint2 (AUGMECON2) is proposed for solving small and medium size of problem. Also, two meta-heuristic algorithms, namely NSGA-II and PESA-II are presented to solve the small, medium and large size of the problem. Taguchi method is utilized to control the parameters, and for comparison between meta-heuristic, five performance metrics are suggested. In addition, a case study in Iran is presented to validate the proposed model. Finally, the results show that PESA-II is more efficient and has better performance than the others based on assessment metrics and computational time.

Keywords: COVID-19; Internet of Things; Inventory; Meta-heuristics; Sustainable healthcare supply chain.