In this paper, a memory-based simulated annealing algorithm called the Dual Memory Simulated Annealing Algorithm (DMSA) is presented to solve multi-line facility layout problems. The objective is to minimize the total material handling cost. Two memory buffers and a restart mechanism are considered. Two benchmark problems were selected from the literature review papers and solved using the standard simulated annealing (SA) algorithm and the DMSA. The obtained results show that solutions provided by the DMSA algorithm are cost-effective compared to the standard SA algorithm and the other algorithms used for solving these test cases. Moreover, to further evaluate the performance of the DMSA algorithm in large scale problems, eleven test cases were selected from the benchmark library of the quadratic assignment problem (QAP). According to the results, the performance of the algorithm in finding solutions to complex problems is exemplary.

Outbreak response can be viewed as a project for which the window of opportunity for planning is often quite limited. Therefore, regular evaluation for sharing the lessons learned is crucial, especially in the cases of national and global crises. As one of the major global concerns, the novel coronavirus, also known as COVID-19, has infected and killed many people in several countries worldwide. Hence, it is worth analyzing the performance of different countries in this regard. In this paper, the confirmed Case Fatality Rate (cCFR), and the confirmed Case Recovery Rate (cCRR) are considered as the main performance criteria, and the data are analyzed utilizing statistical confidence intervals (CIs) implemented in Python. The results identify the regions with high cCFR and low cCRR, as well as the regions with low cCFR and high cCRR. Finally, it is suggested to systematically transfer the knowledge and lessons learned from the high performing countries to where such information and knowledge is needed.