Machine learning grows quickly, which has made numerous academic discoveries and is extensively evaluated in several areas. Optimization, as a vital part of machine learning, has fascinated much consideration of practitioners. The primary purpose of this paper is to combine optimization and machine learning to extract hidden rules, remove unrelated data, introduce the most productive Decision-Making Units (DMUs) in the optimization part, and to introduce the algorithm with the highest accuracy in Machine learning part. In the optimization part, we evaluate the productivity of 30 banks from eight developing countries over the period 2015-2019 by utilizing Data Envelopment Analysis (DEA). An additive Data Envelopment Analysis (DEA) model for measuring the efficiency of decision processes is used. The additive models are often named Slack Based Measure (SBM). This group of models measures efficiency via slack variables. After applying the proposed model, the Malmquist Productivity Index (MPI) is computed to evaluate the productivity of companies. In the machine learning part, we use a specific two-layer data mining filtering pre-processes for clustering algorithms to increase the efficiency and to find the superior algorithm. This study tackles data and methodology-related issues in measuring the productivity of the banks in developing countries and highlights the significance of DMUs productivity and algorithms accuracy in the banking industry by comparing suggested models.

During the past decade, applying nonparametric operation research problems such as Data Envelopment Analysis(DEA) has received significant consideration among researchers. In this paper, a new DEA-based SBM-FDH model is introduced. Finally, productivity evaluation for banking systems in Malmquist Productivity Index (MPI) based on the proposed model has been compared with Slack Based Measurement (SBM) and Free Disposal Hull (FDH). The obtained results confirm the high performance of the proposed model in comparison to the other models used in this paper.

The current COVID-19 pandemic is making a huge impact on society. Most projects are either abandoned or halted due to this pandemic, especially in developing countries. We have conducted this study to evaluate the impact of COVID-19 pandemic on construction projects by using the concept of rework projects. 'Rework project' is a class of projects that are initiated to achieve the intended objectives in the second attempt after failing to achieve the goals in the first attempt. People who were involved in the selected projects in different capacities were interviewed and analysis of the responses was performed. The unique challenges/risks such as time urgency, overburdened resources, and mobilization of contractors, inappropriate documentation gaps, and technological changes were highly significant in rework projects. By having clear recognition to these highly significant risks, organizations will be well equipped in devising strategies to manage and complete the rework projects in the post-pandemic world.

The underserved population could be at risk during the times of crisis, unless there is strong involvement from government agencies such as local and state Health departments and federal Center for Disease Control (CDC). The COVID-19 pandemic was a crisis of different proportion, creating a different type of burden on government agencies. Vulnerable communities including the elderly populations and communities of color have been especially hard hit by this pandemic. This forced these agencies to change their strategies and supply chains to support all populations receiving therapeutics. The National Science Foundation [National Science Foundation (NSF) Award Abstract # 2028612] funded RAID Labs to help federal agencies with strategies. This paper is based on a NSF funded grant to work on investigating supply chain strategies that would minimize the impact on underserved populations during pandemic. This NSF funded study identified the phenomena of last mile importance. The last mile transportation concept was critical in saving lives during the pandemic for underserved populations. The supply chain model then maximizes social goods by sending drugs or vaccines to the communities that need it the most regardless of ability to pay. The outcome of this study helped us prioritize the communities that need the vaccines the most. This informs our supply chain model to shift resources to these areas showing the value in real time prioritization of the COVID-19 supply chain. This paper provides information can be used in our healthcare supply chain model to ensure timely delivery of vaccines and supplies to COVID-19 patients that are the most vulnerable and hence the overall impact of COVID-19 can be minimized. The use of electrical vehicles for last mile transportation can help in significantly fighting the climate change.

The current COVID-19 pandemic is making a huge impact on society. Most projects are either abandoned or halted due to this pandemic, especially in developing countries. We have conducted this study to evaluate the impact of COVID-19 pandemic on construction projects by using the concept of rework projects. 'Rework project' is a class of projects that are initiated to achieve the intended objectives in the second attempt after failing to achieve the goals in the first attempt. People who were involved in the selected projects in different capacities were interviewed and analysis of the responses was performed. The unique challenges/risks such as time urgency, overburdened resources, and mobilization of contractors, inappropriate documentation gaps, and technological changes were highly significant in rework projects. By having clear recognition to these highly significant risks, organizations will be well equipped in devising strategies to manage and complete the rework projects in the post-pandemic world.