ABSTRACT
Developing a vaccine supply chain (VSC) is an intricate process due to product perishability issues and cross-border supply complexities. On top of that, developing a pandemic-driven VSC is more challenging due to having significant operational, infrastructural, and policy-related disruptions. From the perspective of a developing economy such as Bangladesh, handling the global COVID-19 pandemic through the proper establishment of a VSC has been disrupted by a multitude of organizational, economic, and policy barriers. This has hindered the process of establishing a resilient VSC let alone ensuring the sustainability of the supply chain (SC). Therefore, this study strives to identify the key VSC strategies and their interrelationships under four groups: Intra-organizational, Inter-organizational, Legislative, and Environmental, based on previous literature and the expert opinions of industrial practitioners and policymakers. 20 strategies are ranked, and their causal relationships are discussed using the fuzzy DEMATEL method. This study utilizes the fuzzy set theory to deal with the vagueness of human beings' perceptions, and the DEMATEL method to form a structural model to find out the cause (influencing and independent) and effect (influenced and dependent) relationships among different strategies. The outcome of this study shows that ‘developing local production facilities for vaccines', ‘creating extensive governmental policy to ensure efficient distribution of vaccines', ‘ensuring sustainable investment in vaccine manufacturing and distribution', ‘integrating advanced data analytics for robust and resilient demand prediction' and ‘promoting public-private-people partnership for sustainable investment' are the most prominent strategies. The findings provide stakeholders and policymakers with a practical framework for developing a sustainable VSC prepared for any virus outbreak, such as COVID-19, while also achieving the Sustainable Development Goals (SDGs).
ABSTRACT
This study develops a vaccine supply chain (VSC) to ensure sustainable distribution during a global crisis in a developing economy. In this study, a multi-objective mixed-integer programming (MIP) model is formulated to develop the VSC, ensuring the entire network's economic performance. This is achieved by minimizing the overall cost of vaccine distribution and ensuring environmental and social sustainability by minimizing greenhouse gas (GHG) emissions and maximizing job opportunities in the entire network. The shelf-life of vaccines and the uncertainty associated with demand and supply chain (SC) parameters are also considered in this study to ensure the robustness of the model. To solve the model, two recently developed metaheuristics-namely, the multi-objective social engineering optimizer (MOSEO) and multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) methods-are used, and their results are compared. Further, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model has been integrated into the optimization model to determine the best solution from a set of non-dominated solutions (NDSs) that prioritize environmental sustainability. The results are analyzed in the context of the Bangladeshi coronavirus disease (COVID-19) vaccine distribution systems. Numerical illustrations reveal that the MOSEO-TOPSIS model performs substantially better in designing the network than the MOFEPSO-TOPSIS model. Furthermore, the solution from MOSEO results in achieving better environmental sustainability than MOFEPSO with the same resources. Results also reflect that the proposed MOSEO-TOPSIS can help policymakers establish a VSC during a global crisis with enhanced economic, environmental, and social sustainability within the healthcare system.