stochastic/Probabilistic/fuzzy/dynamic modeling
Morteza Abdolhosseini
Abstract
Purpose: Coronavirus (COVID-19) is a pandemic that has affected all countries of the world. Forecasting the spread of corona disease will lead to the necessary measures to be taken by the authorities to control this disease. These include increasing vaccinations, quarantining cities and banning entry ...
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Purpose: Coronavirus (COVID-19) is a pandemic that has affected all countries of the world. Forecasting the spread of corona disease will lead to the necessary measures to be taken by the authorities to control this disease. These include increasing vaccinations, quarantining cities and banning entry and exit, increasing the capacity of hospital beds, setting up round-the-clock vaccination centers, requiring the use of masks in public places, and observing social distances. Therefore, predicting such cases will reduce the number of corona cases and therefore reduce the mortality rate.Methodology: In this paper, using the Singular Spectrum Analysis (SSA) algorithm, the sixth peak of coronavirus in Iran is predicted by considering the current situation. To improve the grouping process of the SSA algorithm, eigenvalues have been selected in the optimization process, so that the predicted time series of which has been significantly improved according to the error-index.Findings: Comparing the proposed method with other forecasting methods include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), TBATS, and Neural Network Autoregression (NNAR), it is observed that the forecasting error is acceptable and the SSA method can be used for forecasting.Originality/Value: This article predicts a new case of COVID-19 using efficient method SSA and the presented results confirm the effectiveness of the proposed method.
Location Modeling
Alireza Roshani; Mohammad Reza Gholamian; Mahsa Arabi
Abstract
Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting ...
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Purpose: Due to the increasing complexity of uncertainty and its impact on the supply chain network, many researchers have resorted to coping approaches with data uncertainty. In addition, the occurrence of any disruption in the supply chain networks can cause irreparable damage. Therefore, adopting appropriate strategies to increase the level of the supply chain network resilience toward any disruptive events seem to be necessary.Methodology: In this paper, a multi-objective, multi-period, and scenario-based mathematical model is presented in which objective functions of delivery time and total network cost are minimized, and to increase network resilience, non-resilience measures are also minimized. Furthermore, a Two-Stage Stochastic Programming (TSSP) approach has been utilized to overcome the uncertain nature of the input parameters. Goal programming has also been used to transform the model into a single-objective one.Findings: In order to prove the model's applicability, the real-world data of a case study of Mashhad has been implemented. Eventually, according to the validation and sensitivity analysis results, the proposed uncertain model has clear superiority over the deterministic model.Originality/Value: This paper presents a multi-objective linear mathematical model for designing the Pharmaceutical Supply Chain (PSC) network under the COVID-19 situation. Two indicators of time and resilience as optimization tools have been considered simultaneously.