محمدحسین سادات حسینی خواجویی

Today, management requires a new approach in the areas of production planning and operations process with a cost management approach. Organizations and industrial units to lead their lives, by recognizing the impact points of the challenges ahead and positive impact, guide and lead them to advance the goals of the organization. Economic dispatching with particle swarm optimization algorithm approach is an approach in the field of industrial units. Dispatching tries to determine the share of production capacity in a way that optimizes the overall performance of the system economically and improve system performance, including: production and process planning, supply and demand balance, cost management, productivity growth, optimal allocation of resources according to the capacity of tanks, formulation of production and operational strategies, the impact on the strategic vision document. In this research, an attempt has been made to perform economic dispatching with the approach of particle swarm optimization algorithm with hypothetical information to measure the feasibility of implementation and its impact on the overall performance of the system and production process and operations.

Education systems in the world are enduring COVID-19 induced perturbations and consequences. Given the growing use of E-learning during COVID-19 epidemic and expansion of Internet-based infrastructure, the need for a resilient approach to e-learning systems is deeply felt. This paper aims to address the issue of how to provide a model for evaluating the resilience of E-learning in Iranian virtual universities during the outbreak of coronavirus employing an Adaptive Neuro-Based Fuzzy Inference System (ANFIS). In the present paper, 5 substantial factors including individual, assessment and support, content, agility, and technology were identified as inputs, and e-learning resilience was considered as single output. Moreover, ANFIS was employed to model the resilience of E-learning systems. Findings revealed almost medium to low degree of resilience for the e-learning system established in Iran’s virtual university. Statistical analysis demonstrated that there was no meaningful difference between experts’ opinions and our proposed procedure for E-learning resilience measurement. The proposed model showed significant sensitivity to changes in agility. Therefore, agility should be considered as the first priority in achieving the desired level of resilience for the e-learning systems of the Iranian virtual university.