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Öğe A bi-level decision-making system to optimize a robust-resilient-sustainable aggregate production planning problem(Pergamon-Elsevier Science Ltd, 2023) Tirkolaee, Erfan Babaee; Aydin, Nadi Serhan; Mahdavi, IrajThis study introduces a sustainable-robust aggregate production planning (APP) problem taking into account workforce productivity, outsourcing option and supplier resilience. In this regard, a bi-level decision-making system is designed using multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models, respectively. In the MADM section, a hybrid method called BWM-WASPAS-Neutrosophic based on bestworst method (BWM) and weighted aggregated sum-product assessment (WASPAS) under type-2 neutrosophic number (T2NN) is utilized to investigate resilient supplier selection. To design the MODM section, a multiobjective mixed-integer linear programming (MILP) model is suggested which is then treated with the help of weighted goal programming (WGP) method. The multi-objective model honors sustainable development goals as it aims not only to minimize total cost and maximize weighted purchasing from suppliers, but also to minimize negative environmental impacts simultaneously. Robust optimization (RO) technique is then implemented to the MODM model to address the demand uncertainty. In order to validate the suggested methodology, a real case study from the literature is examined. The obtained results reveal the efficiency of our decision-making system in finding the optimal policy in less than 1 s where sensitivity analyses also contribute to practical managerial implications. Finally, it is revealed that the total weighted purchase from suppliers has the highest sensitivity to the conservatism levels, which determine the extent to which our uncertain demand parameter can deviate from its nominal value.Öğe Modelling and predicting the growth dynamics of Covid-19 pandemic: A comparative study including Turkey(DergiPark, 2022) Tirkolaee, Erfan Babaee; Aydin, Nadi SerhanEstimating the growth dynamics of a pandemic is critical for policy makers to fine-tune emergency policies in health and other public sectors. The paper presents country-level calibration and prediction results on some well-known models in the literature, namely, the logistic, exponential, Gompertz, SIR and SEIR models. The models are implemented on real data from various countries, including Turkey, and their performance for different estimation windows have been analyzed using R2 scores. The computational results are obtained using Python. The Gompertz model outperforms other models by consistently offering a better fit for the total number of infected. The exponential model is helpful in describing the growth dynamics in the early stages of the COVID-19 pandemic. Suspected-Infected-Recovered (SIR) and Susceptible-Exposed-Infectious-Removed (SEIR) models display a fair performance on the underlying active cases data in many circumstances. Quantitative models can offer policy makers in Turkey and elsewhere a better insight on the evolution of pandemic when everything else is held constant and the infections follow a typical path. The results can be highly sensitive to changes in policies. There is not a single model that can perfectly mimic all stages of pandemic. An ensemble model or multi-modal distributions can be used to capture the evolution of multi-wave pandemics.Öğe Preface: advances of machine learning and optimization in healthcare systems and medicine(Springer, 2023) Weber, Gerhard-Wilhelm; Arabnia, Hamid; Aydin, Nadi Serhan; Tirkolaee, Erfan Babaee[Abstract Not Available]
