TY - JOUR
T1 - Simulation-based genetic algorithm for optimizing a municipal cooperative waste supply chain in a pandemic
AU - Ghasemi, Peiman
AU - Goli, Alireza
AU - Goodarzian, Fariba
AU - Ehmke, Jan Fabian
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2025/1
Y1 - 2025/1
N2 - The quantity of medical waste produced by municipalities is on the rise, potentially presenting significant hazards to both the environment and human health. Developing a robust supply chain network for managing municipal medical waste is important for society, especially during a pandemic like COVID-19. In supply chain network design, factors such as the collection of non-infectious waste, transporting infectious waste from hospitals to disposal facilities, revenue generation from waste-to-energy initiatives, and the potential for pandemic outbreaks are often overlooked. Hence, in this study, we design a model incorporating COVID-19 parameters to mitigate the spread of the virus while designing an effective municipal medical waste supply chain network during a pandemic. The proposed model is multi-objective, multi-echelon, multi-commodity and involves coalition-based cooperation. The first objective function aims to minimize total costs, while the second objective pertains to minimizing the risk of a COVID-19 outbreak. We identify optimal collaboration among municipal medical waste collection centers to maximize cost savings. The COVID-19 prevalence risk level by the waste in each zone is calculated pursuant to their inhabitants. Additionally, we analyze a system dynamic simulation framework to forecast waste generation levels amid COVID-19 conditions. A metaheuristic based on the Non-dominated Sorting Genetic Algorithm II is used to solve the problem and is benchmarked against exact solutions. To illustrate our approach, we present a case study focused on Tehran, Iran. The results show that an increase in the amount of generated waste leads to an increase in the total costs of the supply chain.
AB - The quantity of medical waste produced by municipalities is on the rise, potentially presenting significant hazards to both the environment and human health. Developing a robust supply chain network for managing municipal medical waste is important for society, especially during a pandemic like COVID-19. In supply chain network design, factors such as the collection of non-infectious waste, transporting infectious waste from hospitals to disposal facilities, revenue generation from waste-to-energy initiatives, and the potential for pandemic outbreaks are often overlooked. Hence, in this study, we design a model incorporating COVID-19 parameters to mitigate the spread of the virus while designing an effective municipal medical waste supply chain network during a pandemic. The proposed model is multi-objective, multi-echelon, multi-commodity and involves coalition-based cooperation. The first objective function aims to minimize total costs, while the second objective pertains to minimizing the risk of a COVID-19 outbreak. We identify optimal collaboration among municipal medical waste collection centers to maximize cost savings. The COVID-19 prevalence risk level by the waste in each zone is calculated pursuant to their inhabitants. Additionally, we analyze a system dynamic simulation framework to forecast waste generation levels amid COVID-19 conditions. A metaheuristic based on the Non-dominated Sorting Genetic Algorithm II is used to solve the problem and is benchmarked against exact solutions. To illustrate our approach, we present a case study focused on Tehran, Iran. The results show that an increase in the amount of generated waste leads to an increase in the total costs of the supply chain.
KW - Cooperative game
KW - Simulation-based genetic algorithm
KW - Simulation-optimization model
KW - Supply chain network
KW - Urban waste management
UR - http://www.scopus.com/inward/record.url?scp=85208099390&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2024.109478
DO - 10.1016/j.engappai.2024.109478
M3 - Article
AN - SCOPUS:85208099390
SN - 0952-1976
VL - 139
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 109478
ER -