Harmonizing sustainability and resiliency: A novel robust-stochastic decomposition approach for effective mask distribution and recycling

This study aims to develop a novel graph theory-based clustering algorithm for large-scale logistics planning problems focusing on the distribution of face masks by various transportation modes under uncertainty. A robust multi-objective, mixed-integer linear programming model (MILPM) is utilized to handle imprecise parameters (e.g., demand and processing time). The proposed model supports decision-makers in designing a sustainable closed-loop supply chain network for the optimal face mask distribution under time window limitations. A sample average approximation methodology is applied to tackle the large-scale case study. Furthermore, a graph theory-based clustering algorithm is proposed to accelerate the scenario decomposition approach since it deals with less scenarios in comparison with sample average approximation and scenario decomposition. Silhouette analysis is conducted to measure the performance and accuracy of the generated clusters. Sensitivity analyses are implemented to validate the efficiency and applicability of the presented solution approach. A series of scenarios is set to represent supply chain network disruptions with unknown probabilities. The outcome of this study denotes the optimal flow of face masks and the optimum number of facilities at the time of the COVID-19 outbreak in Toronto, Canada.