Designing a disaster relief supply chain network for pandemics

Pandemics, such as the COVID-19 pandemic (2020-2022), have exposed critical vulnerabilities in supply chains and logistics systems, emphasizing the need for resilient and sustainable solutions. Efficient and fair distribution of essential resources, effective waste management, and prioritization of vulnerable populations are critical to pandemic responses. Existing supply chains often struggle with challenges such as spikes in demand for essential items and inequitable resource distribution. This dissertation addresses these gaps by developing deterministic and stochastic mathematical models, advanced optimization techniques, and integrating new technologies, such as Internet of Things (IoT) platforms, to improve supply chain resilience, sustainability, and adaptability during crises, with a specific focus on managing critical resources and waste. Building on this foundation, this dissertation is organized into four studies, each addressing methods and solutions to tackle challenges in pandemic logistics. In the first study, presented in Chapter 2, a bi-objective mathematical model was developed to optimize the distribution of personal protective equipment (PPE) during the COVID-19 pandemic. The model optimizes both cost minimization and demand fulfillment, employing advanced metaheuristics such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a hybrid metaheuristic approach. Results demonstrate enhanced efficiency and equity in PPE allocation compared to traditional distribution methods. Chapter 3 presents the second study, which develops an Internet of Things (IoT)-enabled reverse supply chain (RSC) for managing COVID-19-related medical waste during the COVID-19 pandemic. The network aims to enhance safety, minimize costs, and promote sustainability in the collection, transportation, treatment, and recycling of waste. IoT devices provide real-time data to estimate waste generation and optimize network operations. A metaheuristic framework addresses the computational complexity of the model, delivering efficient solutions. The model is validated using real-world data from Puebla, Mexico. The third study, presented in Chapter 4, explores the challenges pandemics pose to supply chains, highlighting the need for robust relief networks to distribute supplies effectively and manage disruptions. A multi-objective sustainable network model is proposed to streamline the flow of relief supplies and manage waste. This model aligns with the Sustainable Development Goals (SDGs) by emphasizing energy-efficient production and transportation. The model incorporates real-time data through the Internet of Medical Things (IoMT), which connects medical devices to enhance data collection and sharing for improved network coordination. Due to the NP-hard nature of the problem, tuned metaheuristics are employed to validate the model across five test scenarios, ensuring its adaptability to diverse conditions. Finally, the fourth study, presented in Chapter 5, introduces a system dynamics model to simulate demand varying by age groups during pandemics. A stochastic mathematical model is also developed to prioritize vulnerable populations, such as the elderly, within sustainable humanitarian supply chain networks. By integrating system dynamics with Improved Epsilon-Constraint Methods, these models address uncertainty to ensure equitable resource distribution while managing costs and emissions to align with sustainability principles. Together, these studies offer insights and practical tools to enhance the resilience, agility, and sustainability of supply chains during pandemics, delivering actionable strategies for policymakers and practitioners.

https://orcid.org/0000-0002-9988-2626