TY - JOUR
T1 - A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design
AU - Ala, Ali
AU - Goli, Alireza
AU - Mirjalili, Seyedali
AU - Simic, Vladimir
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2024/1
Y1 - 2024/1
N2 - Healthcare supply chains play a crucial role, which enables the implementation of optimization strategies that have rapidly emerged as highly effective means for improving the overall structure of pharmaceutical and healthcare supply chains. In the healthcare industry, parameters such as increasing the quality of service, as well as optimizing costs, environmental, and social factors play a unique role in supply chain management. To improve the healthcare supply chain network, this study proposed a novel optimization model to optimize multiple objectives, including minimizing the total costs and environmental impacts, while maximizing the social factors by creating jobs simultaneously. To address the effects of uncertain parameters, a fuzzy optimization method alongside the multi-objective gray wolf optimizer (MOGWO), non-dominated sorting genetic algorithm II (NSGA-II), multi-objective differential evolution algorithm (MODEA), and ε-constraint are applied to optimize the model. Also, a case study of the pharmaceutical industry demonstrates the model's efficacy in a real-life context. The numerical results show the MOGWO manages to create high-quality Pareto solutions with a good spread at the Pareto boundary within a short time compared to the ε-constraint approach. Further, it has shown a more robust performance compared to MODEA and NSGA-II, indicating the efficiency of MOGWO, among other solution methods and other objective indicators.
AB - Healthcare supply chains play a crucial role, which enables the implementation of optimization strategies that have rapidly emerged as highly effective means for improving the overall structure of pharmaceutical and healthcare supply chains. In the healthcare industry, parameters such as increasing the quality of service, as well as optimizing costs, environmental, and social factors play a unique role in supply chain management. To improve the healthcare supply chain network, this study proposed a novel optimization model to optimize multiple objectives, including minimizing the total costs and environmental impacts, while maximizing the social factors by creating jobs simultaneously. To address the effects of uncertain parameters, a fuzzy optimization method alongside the multi-objective gray wolf optimizer (MOGWO), non-dominated sorting genetic algorithm II (NSGA-II), multi-objective differential evolution algorithm (MODEA), and ε-constraint are applied to optimize the model. Also, a case study of the pharmaceutical industry demonstrates the model's efficacy in a real-life context. The numerical results show the MOGWO manages to create high-quality Pareto solutions with a good spread at the Pareto boundary within a short time compared to the ε-constraint approach. Further, it has shown a more robust performance compared to MODEA and NSGA-II, indicating the efficiency of MOGWO, among other solution methods and other objective indicators.
KW - Algorithm
KW - Fuzzy Mathematical Programming
KW - Healthcare Supply Chain
KW - Multi-objective Gray Wolf Optimizer
KW - Non-dominated Sorting Genetic Algorithm
KW - Optimization
KW - Sustainability
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85177573559&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2023.111012
DO - 10.1016/j.asoc.2023.111012
M3 - Article
AN - SCOPUS:85177573559
SN - 1568-4946
VL - 150
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 111012
ER -