This paper presents wind turbine allocation in distribution systems to reduce active power loss and voltage deviations using a multi-objective artificial electric field algorithm (MOAEFA). The proposed method is a mat...
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This paper presents wind turbine allocation in distribution systems to reduce active power loss and voltage deviations using a multi-objective artificial electric field algorithm (MOAEFA). The proposed method is a mathematical algorithm which is suitably capable to find optimal solutions based on the Pareto solution set using a fuzzy decision-making method. The proposed problem is implemented on 10, 33 and 69 bus IEEE radial distribution networks. The installation location, size and power factors of wind turbines are determined optimally using the MOAEFA method. Single and multi-objective allocation problem of wind turbines is implemented using AEFA, GWO, PSO and MOAEFA, MOGWO, MOPSO methods. The obtained the results of AEFA method achieves less power loss and voltage deviations compared to the conventional GWO and PSO methods. Moreover, the results of multi-objective fuzzy allocation show that there is a compromise between single-objective results and MOAEFA method provides better performance given the loss power and voltage deviation reduction in distribution networks. Furthermore, MOAEFA method has found a better voltage profile in the allocation of wind turbines in the distribution network compared to the other methods. The performance comparison between MOAEFA method and the previous methods given in the literature verifies the superiority of the MOAEFA method. (C) 2021 Elsevier B.V. All rights reserved.
In automotive mixed-model assembly lines (MMALs), a large number of different parts need to be supplied to the assembly lines on time, which poses significant logistical challenges for manufacturers. However, consiste...
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In automotive mixed-model assembly lines (MMALs), a large number of different parts need to be supplied to the assembly lines on time, which poses significant logistical challenges for manufacturers. However, consistently supplying parts for MMALs is a very complex issue due to factors such as diverse component requirements and logistical coordination in the supply chain. In this paper, we propose a bi-objective optimization problem to minimize the line -side inventory and energy consumption in a milk-run material distribution system. Meanwhile, the number of Kanban and the capacity of the material bin that affect the scheduling is jointly optimized, so that the material distribution scheduling plan is optimized. Considering the character of the problem, a multiobjectiveartificialelectricfieldalgorithm with SARSA mechanism (MOAEFASA) is developed to solve the problem. The algorithm proposed combines the merits of the artificialelectricfieldalgorithm (AEFA) and the framework of the non-dominated sorting genetic algorithm (NSGA-II). In addition, several optimization strategies are used to optimize the performance of the algorithm. Finally, the validity of the mathematical model is verified through the Epsilon constraint method and the superiority of the MOAEFASA is illustrated by numerical experiments with four outstanding meta-heuristics.
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