Document Type : Research Paper
Authors
- Shahla Jahangiri 1
- Milad Abolghasemian 2
- Adel Pourghader Chobar 3
- Ahmadreza Nadaffard 4
- Vahid Mottaghi 5
1 Department of Industrial Engineering, Payame Noor University, Tehran, Iran.
2 Department of Industrial Engineering, Lahijan Branch, Islamic Azad university, Lahijan, Iran.
3 Department of Industrial Engineering, Faculty of Mechanic and Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
4 Department of Industrial Engineering, South Tehran Branch, Azad University, Tehran, Iran.
5 Department of IT Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran.
Abstract
China introduces a new strain of coronavirus as a causative of a new respiratory disease after several people contracted an unusual pneumonia in December 2019. The World Health Organization stated that the outbreak of the virus resulted in public health emergencies around the world. Humanitarian supply chain management is concerned with managing the efficient flow of aid materials, information and services and aim to reduce the impact of disaster on human lives. In this paper, provides a ranking for key resources in the humanitarian supply chain in the emergency department of Iranian hospital using hybrid decision-making method under COVID-19 conditions. According to the obtain results, nurses in RK 1, receptionists RK 2, general surgeon RK 3, heart residents RK 4 and pulmonologist RK 5. Hybrid decision-making method in this paper is an invaluable contribution to the emergency department and medical managers for evaluates of current situation Emergency Department when crisis occur.
Keywords
Main Subjects
- Kemp, E., Price, G. N., Fuller, N. R., & Kemp, E. F. (2020). African Americans and COVID-19: beliefs, behaviors and vulnerability to infection. International journal of healthcare management, 13(4), 303-311. https://doi.org/10.1080/20479700.2020.1801161
- Shirazi, H., Kia, R., & Ghasemi, P. (2020). Ranking of hospitals in the case of COVID-19 outbreak: a new integrated approach using patient satisfaction criteria. International journal of healthcare management, 13(4), 312-324. https://doi.org/10.1080/20479700.2020.1803622
- Kumar, S., Raut, R. D., & Narkhede, B. E. (2020). A proposed collaborative framework by using artificial intelligence-internet of things (AI-IoT) in COVID-19 pandemic situation for healthcare workers. International journal of healthcare management, 13(4), 337-345. https://doi.org/10.1080/20479700.2020.1810453
- Wald, G. (2020). Humanitarian logistics during corona times. Retrieved from https://www.researchgate.net/publication/343386531
- Altay, N., & Pal, R. (2014). Information diffusion among agents: implications for humanitarian operations. Production and operations management, 23(6), 1015-1027. https://doi.org/10.1111/poms.12102
- Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm's resilience to supply chain disruptions: scale development and empirical examination. Journal of operations management, 33, 111-122. https://doi.org/10.1016/j.jom.2014.11.002
- Ehrgott, M., & Wiecek, M. M. (2005). Mutiobjective programming. In Multiple criteria decision analysis: State of the art surveys (pp. 667-708). Springer, New York, NY. https://doi.org/10.1007/0-387-23081-5_17
- Yousefi, M., Yousefi, M., & Fogliatto, F. S. (2020). Simulation-based optimization methods applied in hospital emergency departments: a systematic review. Simulation, 96(10), 791-806. https://doi.org/10.1177/0037549720944483
- Bal, A., Ceylan, C., & Taçoğlu, C. (2017). Using value stream mapping and discrete event simulation to improve efficiency of emergency departments. International journal of healthcare management, 10(3), 196-206. https://doi.org/10.1080/20479700.2017.1304323
- Duguay, C., & Chetouane, F. (2007). Modeling and improving emergency department systems using discrete event simulation. Simulation, 83(4), 311-320. https://doi.org/10.1177/0037549707083111
- Ahmed, M. A., & Alkhamis, T. M. (2009). Simulation optimization for an emergency department healthcare unit in Kuwait. European journal of operational research, 198(3), 936-942. https://doi.org/10.1016/j.ejor.2008.10.025
- Abo-Hamad, W., & Arisha, A. (2013). Simulation-based framework to improve patient experience in an emergency department. European journal of operational research, 224(1), 154-166. https://doi.org/10.1016/j.ejor.2012.07.028
- Kadri, F., Chaabane, S., & Tahon, C. (2014). A simulation-based decision support system to prevent and predict strain situations in emergency department systems. Simulation modelling practice and theory, 42, 32-52. https://doi.org/10.1016/j.simpat.2013.12.004
- Zeinali, F., Mahootchi, M., & Sepehri, M. M. (2015). Resource planning in the emergency departments: A simulation-based metamodeling approach. Simulation modelling practice and theory, 53, 123-138. https://doi.org/10.1016/j.simpat.2015.02.002
- Feng, Y. Y., Wu, I., & Chen, T. L. (2017). Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm. Health care management science, 20(1), 55-75. https://doi.org/10.1007/s10729-015-9335-1
- Traoré, M. K., Zacharewicz, G., Duboz, R., & Zeigler, B. (2019). Modeling and simulation framework for value-based healthcare systems. Simulation, 95(6), 481-497.
- Daldoul, D., Nouaouri, I., Bouchriha, H., & Allaoui, H. (2018). A stochastic model to minimize patient waiting time in an emergency department. Operations research for health care, 18, 16-25. https://doi.org/10.1016/j.orhc.2018.01.008
- Chen, W., Guo, H., & Tsui, K. L. (2020). A new medical staff allocation via simulation optimisation for an emergency department in Hong Kong. International journal of production research, 58(19), 6004-6023. https://doi.org/10.1080/00207543.2019.1665201
- Aghapour, A. H., Yazdani, M., Jolai, F., & Mojtahedi, M. (2019). Capacity planning and reconfiguration for disaster-resilient health infrastructure. Journal of building engineering, 26, 100853. https://doi.org/10.1016/j.jobe.2019.100853
- Ordu, M., Demir, E., Tofallis, C., & Gunal, M. M. (2021). A novel healthcare resource allocation decision support tool: a forecasting-simulation-optimization approach. Journal of the operational research society, 72(3), 485-500. https://doi.org/10.1080/01605682.2019.1700186
- Pegoraro, F., Santos, E. A. P., & Loures, E. D. F. R. (2020). A support framework for decision making in emergency department management. Computers & industrial engineering, 146, 106477. https://doi.org/10.1016/j.cie.2020.106477
- Pegoraro, F., Santos, E. A. P., Loures, E. D. F. R., & Laus, F. W. (2020). A hybrid model to support decision making in emergency department management. Knowledge-based systems, 203, 106148. https://doi.org/10.1016/j.knosys.2020.106148
- Jahangiri, Sh., Abolghasemian, M., Ghasemi, P., & Pourghader Chobar, A. (2021). Simulation-based optimization: analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering, 1(1). 1504/IJISE.2021.10037641
- Yang, C. H., Hsu, W., & Wu, Y. L. (2021). A hybrid multiple-criteria decision portfolio with the resource constraints model of a smart healthcare management system for public medical centers. Socio-economic planning sciences, 101073. https://doi.org/10.1016/j.seps.2021.101073
- Ghorui, N., Ghosh, A., Mondal, S. P., Bajuri, M. Y., Ahmadian, A., Salahshour, S., & Ferrara, M. (2021). Identification of dominant risk factor involved in spread of COVID-19 using hesitant fuzzy MCDM methodology. Results in physics, 21, 103811. https://doi.org/10.1016/j.rinp.2020.103811
- Christopher, M. (2016). Logistics & supply chain management. Pearson Uk.
- Mangan, J., & Lalwani, C. (2016). Global logistics and supply chain management. John Wiley & Sons.
- Kovacs, G., & Spens, K. M. (2012). Relief supply chain management for disasters: humanitarian aid and emergency logistics. Hershey, PA: Information Science Reference. DOI: 4018/978-1-60960-824-8
- John, L., & Ramesh, A. (2012). Humanitarian supply chain management in India: a SAP‐LAP framework. Journal of advances in management research, 9(2), 217-235. https://doi.org/10.1108/09727981211271968
- Rezaei, J. (2016). Best-worst multi-criteria decision-making method: some properties and a linear model. Omega, 64, 126-130. https://doi.org/10.1016/j.omega.2015.12.001
- Hwung, C. L., & Yoon, K. (1981). Multiple attribute decision making: methods and applications a state-of-the-art survey (lecture notes in economics and mathematical systems. Springer-Verlag, New York
- Ertuğrul, İ., & Karakaşoğlu, N. (2009). Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert systems with applications, 36(1), 702-715. https://doi.org/10.1016/j.eswa.2007.10.014
- Wang, Y. J. (2008). Applying FMCDM to evaluate financial performance of domestic airlines in Taiwan. Expert systems with applications, 34(3), 1837-1845. https://doi.org/10.1016/j.eswa.2007.02.029
- Sánchez-Lozano, J. M., García-Cascales, M. S., & Lamata, M. T. (2016). Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain. Journal of cleaner production, 127, 387-398. https://doi.org/10.1016/j.jclepro.2016.04.005
- Onat, N. C., Gumus, S., Kucukvar, M., & Tatari, O. (2016). Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle technologies. Sustainable production and consumption, 6, 12-25. https://doi.org/10.1016/j.spc.2015.12.003
)