Designing a green forward and reverse logistics network with an IoT approach considering backup suppliers and special disposal for epidemics management

• Presentation of a new model for designing a GLN during epidemics. • Integration of green forward and reverse logistics with an IoT approach. • Focus on backup suppliers to address material shortages in emergencies. • Emphasis on various types of waste (both infectious and non-infectious) and special disposal considerations. • Model optimization illustrated through CO 2 emissions analysis. This paper proposes a mathematical model for the green forward and reverse logistics network (LN), examining the impact of epidemics such as coronavirus (COVID-19) and human metapneumovirus (HMPV) on this network. Alongside managing the network, a new support center and dedicated infectious waste recycling and disposal facilities have been established. A mixed-integer linear programming (MOMILP) approach is employed for modeling a green forward and reverse LN during epidemics. This study presents two problem-solving techniques: the LP-metric method for small problems and the whale optimization algorithm (WOA) for medium and large-scale issues. The positive and negative effects of epidemics on environmental and economic aspects of the objective functions were assessed. This study's contribution and novelty, compared to previous research, lie in the introduction of backup supply centers, particularly waste disposal centers, and the comparison of normal and epidemic conditions for disaster management using the IoT approach.

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Publication Details

Journal
Results in Engineering
Published
2025-04-04
DOI
https://doi.org/10.1016/j.rineng.2025.104770
Citations
10
Primary Topic
Supply Chain Resilience and Risk Management
Type
article
Field-Weighted Citation Impact
22.26
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article

Designing a green forward and reverse logistics network with an IoT approach considering backup suppliers and special disposal for epidemics management

Sina Abbasi, Yiğit Kazançoğlu, Sara Damavandi, Amirhossein RadmanKian et al.
10 citations
Results in Engineering
Supply Chain Resilience and Risk Management
22.26
article

Designing a green forward and reverse logistics network with an IoT approach considering backup suppliers and special disposal for epidemics management

Sina Abbasi, Yiğit Kazançoğlu, Sara Damavandi, Amirhossein RadmanKian, Kian Zeinolabedinzadeh
article en
10 citations

Abstract

• Presentation of a new model for designing a GLN during epidemics. • Integration of green forward and reverse logistics with an IoT approach. • Focus on backup suppliers to address material shortages in emergencies. • Emphasis on various types of waste (both infectious and non-infectious) and special disposal considerations. • Model optimization illustrated through CO 2 emissions analysis. This paper proposes a mathematical model for the green forward and reverse logistics network (LN), examining the impact of epidemics such as coronavirus (COVID-19) and human metapneumovirus (HMPV) on this network. Alongside managing the network, a new support center and dedicated infectious waste recycling and disposal facilities have been established. A mixed-integer linear programming (MOMILP) approach is employed for modeling a green forward and reverse LN during epidemics. This study presents two problem-solving techniques: the LP-metric method for small problems and the whale optimization algorithm (WOA) for medium and large-scale issues. The positive and negative effects of epidemics on environmental and economic aspects of the objective functions were assessed. This study's contribution and novelty, compared to previous research, lie in the introduction of backup supply centers, particularly waste disposal centers, and the comparison of normal and epidemic conditions for disaster management using the IoT approach.

Results in EngineeringVol. 26
Southern Methodist University (US), The University of Texas at Arlington (US), Islamic Azad University, Lahijan Branch (IR), University of Chieti-Pescara (IT), Yaşar University (TR)
Openalex Percentile: Top 1%
Supply Chain Resilience and Risk Management
22.26
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