Introducing a novel revenue-sharing contract in media supply chain management using data mining and multi-criteria decision-making methods

Prioritizing and queueing the emergency departments' patients using a novel data-driven decision-making methodology, a real case study

One of the principal problems in epidemic disruptions like the COVID-19 pandemic is that the number of patients needing hospitals' emergency departments' services significantly grows. Since COVID-19 is an infectious disease, any aggregation has to be prevented accordingly. However, few aggregations cannot be prevented, including hospitals. To the best of our knowledge, COVID-19 is a life-threatening disease, especially for people in poor health conditions. Therefore, it sounds reasonable to optimize the health care queuing systems to minimize the infection rate by prioritizing patients based on their health condition so patients with a higher risk of infection will leave the queue sooner. In this paper, relying on data mining models and expert's opinions, we propose a method for patient classification and prioritizing. The optimal number of servers (treatment systems) will be determined by benefiting from a mixed-integer model and the grasshopper optimization algorithm.

Prioritizing Post-Disaster Reconstruction Projects Using an Integrated Multi-Criteria Decision-Making Approach: A Case Study

As the destructive impacts of both human-made and natural disasters on societies and built environments are predicted to increase in the future, innovative disaster management strategies to cope with emergency conditions are becoming more crucial. After a disaster, selecting the most critical post-disaster reconstruction projects among available projects is a challenging decision due to resource constraints. There is strong evidence that the success of many post-disaster reconstruction projects is compromised by inappropriate decisions when choosing the most critical projects. Therefore, this study presents an integrated approach based on four multi-criteria decision-making (MCDM) techniques, namely, TOPSIS, ELECTRE III, VIKOR, and PROMETHEE, to aid decision makers in prioritizing post-disaster projects. Furthermore, an aggregation approach (linear assignment) is used to generate the final ranking vector since various methods may provide different outcomes. In the first stage, 21 criteria were determined based on sustainability. To validate the performance of the proposed approach, the obtained results were compared to the results of an artificial neural network (ANN) algorithm, which was applied to predict the projects’ success rates. A case study was used to assess the application of the proposed model. The obtained results show that in the selected case, the most critical criteria in post-disaster project selection are quality, robustness, and customer satisfaction. The findings of this study can contribute to the growing body of knowledge about disaster management strategies and have implications for key stakeholders involved in post-disaster reconstruction projects. Furthermore, this study provides valuable information for national decision makers in countries that have limited experience with disasters and where the destructive consequences of disasters on the built environment are increasing.