During epidemics,controlling the patients’congestion is a way to reduce disease spreading.Raising medical demands converts hospitals into one of the sources of disease outbreaks.The long patient waiting time in queue...During epidemics,controlling the patients’congestion is a way to reduce disease spreading.Raising medical demands converts hospitals into one of the sources of disease outbreaks.The long patient waiting time in queues to receive medical services leads to more casualties.The rise of patients increases their waste,which is another source of disease outbreak.In this study,a mathematical model is developed to control patients’congestion in a medical center and manage their waste,considering environmental issues.Besides a queueing system controlling the patients’congestion in the treatment center,another queue is considered for vehicles.An inventory model is employed to prevent waste accumulation.The developed model is solved and reaches an exact solution in small size,and obtains an acceptable solution in large size using the Grasshopper algorithm.A case study is considered to demonstrate the model’s applicability.Also,Sensitivity analysis and valuable managerial insights are presented.展开更多
This article presents an in-depth exploration of classical and Bayesian inference methods to estimate the traffic intensity parameter,providing a comprehensive comparison of these two statistical paradigms in a novel ...This article presents an in-depth exploration of classical and Bayesian inference methods to estimate the traffic intensity parameter,providing a comprehensive comparison of these two statistical paradigms in a novel multiserver Markovian queueing model(M/M/s)incorporating the phenomenon of reverse balking.The classical inference relies on maximum likelihood(ML)estimation,while the Bayesian approach leverages prior distributions and posterior analysis to enhance estimates.The results indicate that Bayesian methods offer better flexibility and precision compared to traditional ML estimates.Additionally,the predictive probabilities for the number of customers in the system are calculated for different hyper-parameter values of the prior through extensive simulation techniques.The results provide valuable insights for optimizing queue management and improving service efficiency in systems where reverse balking occurs.Moreover,a real-life example is presented to demonstrate the practical implementation of the proposed methodology.This work not only advances the theoretical understanding of queueing dynamics,but also offers practical implications for industries relying on efficient service mechanisms.展开更多
文摘During epidemics,controlling the patients’congestion is a way to reduce disease spreading.Raising medical demands converts hospitals into one of the sources of disease outbreaks.The long patient waiting time in queues to receive medical services leads to more casualties.The rise of patients increases their waste,which is another source of disease outbreak.In this study,a mathematical model is developed to control patients’congestion in a medical center and manage their waste,considering environmental issues.Besides a queueing system controlling the patients’congestion in the treatment center,another queue is considered for vehicles.An inventory model is employed to prevent waste accumulation.The developed model is solved and reaches an exact solution in small size,and obtains an acceptable solution in large size using the Grasshopper algorithm.A case study is considered to demonstrate the model’s applicability.Also,Sensitivity analysis and valuable managerial insights are presented.
文摘This article presents an in-depth exploration of classical and Bayesian inference methods to estimate the traffic intensity parameter,providing a comprehensive comparison of these two statistical paradigms in a novel multiserver Markovian queueing model(M/M/s)incorporating the phenomenon of reverse balking.The classical inference relies on maximum likelihood(ML)estimation,while the Bayesian approach leverages prior distributions and posterior analysis to enhance estimates.The results indicate that Bayesian methods offer better flexibility and precision compared to traditional ML estimates.Additionally,the predictive probabilities for the number of customers in the system are calculated for different hyper-parameter values of the prior through extensive simulation techniques.The results provide valuable insights for optimizing queue management and improving service efficiency in systems where reverse balking occurs.Moreover,a real-life example is presented to demonstrate the practical implementation of the proposed methodology.This work not only advances the theoretical understanding of queueing dynamics,but also offers practical implications for industries relying on efficient service mechanisms.
