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Transportation Research Record ; 2677:313-323, 2023.
Article in English | Scopus | ID: covidwho-2316618
Annals of Operations Research ; : 1-73, 2023.
Article in English | Academic Search Complete | ID: covidwho-2315426
International Journal of Production Economics ; : 108899, 2023.
Article in English | ScienceDirect | ID: covidwho-2313343
International Journal of Industrial Engineering-Theory Applications and Practice ; 30(1):246-255, 2023.
Article in English | Web of Science | ID: covidwho-2309729
Omega ; 114: 102750, 2023 Jan.
Article in English | MEDLINE | ID: covidwho-2307317


The COVID-19 pandemic - as a massive disruption - has significantly increased the need for medical services putting an unprecedented strain on health systems. This study presents a robust location-allocation model under uncertainty to increase the resiliency of health systems by applying alternative resources, such as backup and field hospitals and student nurses. A multi-objective optimization model is developed to minimize the system's costs and maximize the satisfaction rate among medical staff and COVID-19 patients. A robust approach is provided to face the data uncertainty, and a new mathematical model is extended to linearize a nonlinear constraint. The ICU beds, ward beds, ventilators, and nurses are considered the four main capacity limitations of hospitals for admitting different types of COVID-19 patients. The sensitivity analysis is performed on a real-world case study to investigate the applicability of the proposed model. The results demonstrate the contribution of student nurses and backup and field hospitals in treating COVID-19 patients and provide more flexible decisions with lower risks in the system by managing the fluctuations in both the number of patients and available nurses. The results showed that a reduction in the number of available nurses incurs higher costs for the system and lower satisfaction among patients and nurses. Moreover, the backup and field hospitals and the medical staff elevated the system's resiliency. By allocating backup hospitals to COVID-19 patients, only 37% of severe patients were lost, and this rate fell to less than 5% after establishing field hospitals. Moreover, medical students and field hospitals curbed the costs and increased the satisfaction rate of nurses by 75%. Finally, the system was protected from failure by increasing the conservatism level. With a 2% growth in the price of robustness, the system saved 13%.

Applied Soft Computing ; 140, 2023.
Article in English | Scopus | ID: covidwho-2300249
Journal of Humanitarian Logistics and Supply Chain Management ; 13(2):140-156, 2023.
Article in English | ProQuest Central | ID: covidwho-2295632
Journal of Industrial and Management Optimization ; 19(4):3044-3059, 2023.
Article in English | Scopus | ID: covidwho-2269120
Journal of Transportation Engineering Part A: Systems ; 149(4), 2023.
Article in English | Scopus | ID: covidwho-2259160
International Journal of Computers and their Applications ; 29(4):215-228, 2022.
Article in English | Scopus | ID: covidwho-2252388
4th International Conference on Soft Computing and its Engineering Applications, icSoftComp 2022 ; 1788 CCIS:123-134, 2023.
Article in English | Scopus | ID: covidwho-2281697
International Journal of Production Research ; 61(8):2795-2827, 2023.
Article in English | ProQuest Central | ID: covidwho-2281578
25th International Conference on Interactive Collaborative Learning, ICL 2022 ; 633 LNNS:718-729, 2023.
Article in English | Scopus | ID: covidwho-2279878
Journal of Environmental Engineering (United States) ; 149(1), 2023.
Article in English | Scopus | ID: covidwho-2245005
Socioecon Plann Sci ; : 101439, 2022 Sep 22.
Article in English | MEDLINE | ID: covidwho-2236307


In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective mixed-integer linear programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.