supply chain management analyzing/modelling
Javad Mohammadghasemi; esmaeil najafi; mohammad fallah; mohammad reza nabatchian
Abstract
In this paper, the modeling of a sustainable supply chain network of the electricity industry under uncertainty is discussed. The purpose of providing this supply chain network is to meet the customers' demand for solar panels in order to produce clean energy. In this model, important decisions were ...
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In this paper, the modeling of a sustainable supply chain network of the electricity industry under uncertainty is discussed. The purpose of providing this supply chain network is to meet the customers' demand for solar panels in order to produce clean energy. In this model, important decisions were made, including supplier selection, construction of production centers, optimal allocation of product flow and solar panel pricing. The results of the model showed that with increasing reliability in the network, the amount of net present value in the network has decreased and the amount of greenhouse gas emissions in the network has increased. Also, the analysis of the results showed that with the increase in the uncertainty rate in the network, the net present value and reliability in the network decreased and the amount of greenhouse gas emissions increased. On the other hand, further analysis on 15 numerical examples showed the high efficiency of two algorithms, MOALO and MOWOA, compared to the epsilon constraint method. Finally, the results of the statistical test also showed that there was no significant difference between the averages of the number of effective answers, the maximum spread and the metric distance between the two algorithms, and there was only a significant difference between the solving time of the two algorithms. The results of the presented solution methods show their high efficiency in solving the sustainable supply chain network model of the electricity industry.
Optimization in science and engineering
Nazila Nikdel
Abstract
Purpose: Nowadays, robotic systems are widely used in advanced industrial operations. Therefore, making appropriate control decisions to ensure the efficiency of these systems is critical. Criteria such as operation time and response speed, control cost, and system error need to be controlled by providing ...
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Purpose: Nowadays, robotic systems are widely used in advanced industrial operations. Therefore, making appropriate control decisions to ensure the efficiency of these systems is critical. Criteria such as operation time and response speed, control cost, and system error need to be controlled by providing appropriate methods to ensure the successful performance of industrial operations. Therefore, this article pursues two main objectives: 1) controlling the robotic system by presenting a method based on fractional-order calculus so that it can control the system despite its complexity and non-linearity, 2) presenting the meta-heuristic algorithm "Improved Grey Wolf" to optimize the system response.Methodology: First, the mathematical model of the robot is presented based on Lagrange rules, and then the fractional-order calculus is used to design the controller. In addition, the efficiency of the grey wolf algorithm is increased with the introduction of an improved method.Findings: Different cost functions based on the main performance criteria of the robotic system are introduced, and an improved algorithm is applied to them. The comparison results of the proposed algorithm and other algorithms, indicate its satisfying performance. In addition, the efficiency of the fractional-order controller is compared with its integer-order counterpart, and the results show a significant improvement in system performance.Originality/Value: The proposed controller can control the system well despite its complexity and non-linearity. In addition, inspired by the Grey Wolf algorithm, an improved optimization method is proposed that can increase the efficiency of the controlled system. Numerical results show the satisfying performances of the proposed controller and the improved optimization algorithm.