COVID-19 has impacted various aspects of life and business. The economic panic and effects caused by the pandemic have led to disruptions in most supply chains (SCs) worldwide. Our research aims to analyse the impact of COVID-19 on SCs and enable organisations to prioritise solutions based on their relative importance. The research consists of two main phases. Phase I analyses the impact of the pandemic on SCs in terms of challenges, concerns, steps, and solutions with the goal of SC resilience. The second phase proposes a merged Analytic Network Process-Technique for Order Preference by Similarity to Ideal Solution framework (ANP-TOPSIS) to prioritise the solutions that considers the complicated interrelationships between the factors involved in decision-making. The proposed model increases the efficiency of the decision-making process and helps decision makers effectively select solutions based on their importance and impact on business. The results indicate that SCs should continuously utilise technology to withstand future competition and crises.

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth–Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes’ residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency.

Inadequate staffing and long waiting times in hospital emergency rooms are key concerns that can have a negative impact on patient safety and health, as well as the hospital’s overall performance. The purpose of this paper is to investigate the scope of combining the DMAIC (define, measure, analyze, improve, and control) methodology with discrete event simulation and to explore its successful deployment in the Jordanian healthcare sector. The study discussed in this paper is based on a case study conducted utilizing the DMAIC and simulation technique and its application in reducing waiting time and enhancing overall system efficiency in Jordan’s Princess Rahma hospital’s pediatric emergency department. The study shows improvements in the performance of the process and thus productivity in the emergency department through adapting the combined Six Sigma DES methodology. The cycle time of the process was reduced by 73% of the present value, while simultaneously enhancing the total performance of the emergency department by 83%.

With the great challenges that the latest pandemic (COVID-19) has imposed on manufacturing companies, the need to overcome and cope with such situations is becoming crucial. Supply chain resilience is one of the main aspects that enables manufacturers to cope with change and uncertainty; therefore, it is essential to develop the capabilities necessary to do so. This study aimed to ensure supply chain resilience in light of the COVID-19 pandemic through prioritizing main supply chain capabilities. After surveying (30) experts in supply chain from leading manufacturing companies in Jordan, a Fuzzy Analytic Hierarchy Process (FAHP) analysis was conducted to prioritize main supply chain capabilities that were derived from the related literature. The results of this study showed that proactive capabilities, followed by reactive capabilities, were the most dominant capabilities that could ensure supply chain resilience, while efficiency-based capabilities were the least significant. Therefore, manufacturing companies should place their focus and emphasis on reacting to this pandemic in a more systematic manner.