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This study evaluated the influence of the coronavirus pandemic on the healthcare and non-cold pharmaceutical care distribution supply chain.

The model involves four objective functions to minimize the total costs, environmental impacts, lead time and the probability of a healthcare provider being infected by a sick person was developed. An improved version of the augmented e-constraint method was applied to solve the proposed model for a case study of a distribution company to show the effectiveness of the proposed model. A sensitivity analysis was conducted to identify the sensitive parameters. Finally, two robust models were developed to overcome the innate uncertainty of sensitive parameters.

The result demonstrated a significant reduction in total costs, environmental impacts, lead time and probability of a healthcare worker being infected from a sick person by 40%, 30%, 75% and 54%, respectively, under the coronavirus pandemic compared to the normal condition. It should be noted that decreasing lead time and disease infection rate could reduce mortality and promote the model's effectiveness.

Implementing this model could assist the healthcare and pharmaceutical distributors to make more informed decisions to minimize the cost, lead time, environmental impacts and enhance their supply chain resiliency.

This study introduced an objective function to consider the coronavirus infection rates among the healthcare workers impacted by the pharmaceutical/healthcare products supply chain. This study considered both economic and environmental consequences caused by the coronavirus pandemic condition, which occurred on a significantly larger scale than past pandemic and epidemic crises.

This paper aims to examine the integration of lateral transshipment and road vulnerability into the humanitarian relief chain in light of affected area priority to address equitable distribution and assess the impact of various parameters on the total average inflated distance traveled per relief item.

After identifying comprehensive critical criteria and subcriteria, a hybrid multi-criteria decision-making framework was applied to obtain the demand points’ weight and ranking in a real-life earthquake scenario. Direct shipment and lateral transshipment models were then presented and compared. The developed mathematical models are formulated as mixed-integer programming models, considering facility location, inventory prepositioning, road vulnerability and quantity of lateral transshipment.

The study found that the use of prioritization criteria and subcriteria, in conjunction with lateral transshipment and road vulnerability, resulted in a more equitable distribution of relief items by reducing the total average inflated distance traveled per relief item.

To the best of the authors’ knowledge, this study is one of the first research on equity in humanitarian response through prioritization of demand points. It also bridges the gap between two areas that are typically treated separately: multi-criteria decision-making and humanitarian logistics.

This is the first scholarly work in Shiraz focused on the equitable distribution system by prioritization of demand points and assigning relief items to them after the occurrence of a medium-scale earthquake scenario considering lateral transshipment in the upper echelon.

The paper clarifies how to prioritize demand points to promote equity in humanitarian logistics when the authors have faced multiple factors (i.e. location of relief distribution centers, inventory level, distance, lateral transshipment and road vulnerability) simultaneously.