Yazar "Ranjbar-Bourani, Mehdi" seçeneğine göre listele

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    Big data-driven cognitive computing system for optimization of social media analytics
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) Sangaiah, Arun Kumar; Goli, Alireza; Tirkolaee, Erfan Babaee; Ranjbar-Bourani, Mehdi; Pandey, Hari Mohan; Zhang, Weizhe
    The integration of big data analytics and cognitive computing results in a new model that can provide the utilization of the most complicated advances in industry and its relevant decision-making processes as well as resolving failures faced during big data analytics. In E-projects portfolio selection (EPPS) problem, big data-driven decision-making has a great importance in web development environments. EPPS problem deals with choosing a set of the best investment projects on social media such that maximum return with minimum risk is achieved. To optimize the EPPS problem on social media, this study aims to develop a hybrid fuzzy multi-objective optimization algorithm, named as NSGA-III-MOIWO encompassing the non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective invasive weed optimization (MOIWO) algorithms. The objectives are to simultaneously minimize variance, skewness and kurtosis as the risk measures and maximize the total expected return. To evaluate the performance of the proposed hybrid algorithm, the data derived from 125 active E-projects in an Iranian web development company are analyzed and employed over the period 2014-2018. Finally, the obtained experimental results provide the optimal policy based on the main limitations of the system and it is demonstrated that the NSGA-III-MOIWO outperforms the NSGA-III and MOIWO in finding efficient investment boundaries in EPPS problems. Finally, an efficient statistical-comparative analysis is performed to test the performance of NSGA-III-MOIWO against some well-known multi-objective algorithms.
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    A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect
    (Elsevier Ltd, 2020) Tirkolaee, Erfan Babaee; Aydın, Nadi Serhan; Ranjbar-Bourani, Mehdi; Weber, Gerhard Wilhelm
    This paper proposes a novel bi-objective mixed-integer linear programming (MILP) model for allocating and scheduling disaster rescue units considering the learning effect. When a natural phenomenon (e.g., earthquake or flood) occurs, the presented decision support model is expected to help decision-makers of emergency relief centers to provide efficient planning for rescue units to minimize the total weighted completion time of rescue operations, as well as the total delay in rescue operations. The problem has some features in common with unrelated parallel machine scheduling (UPMS) problem and traveling salesman problem (TSP). To deal with the inherent uncertainty and bi-objective nature of the problem, an uncertainty-set based robust optimization technique and multi-choice goal programming (MCGP) with utility functions are applied. To demonstrate the applicability of the proposed model, a real case study in Mazandaran province in Iran is presented. The computational results confirm the high complexity of the problem and the significant impacts of the uncertainty on the solution. Moreover, the analytical results provide useful insights to decision-makers for disastrous situations.