This paper proposes a novel adaptive nesting Evolutionary Algorithm to jointly optimize two important aspects of the configuration and planning of a Microgrid (MG): the structure's design and the way it is operate...
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This paper proposes a novel adaptive nesting Evolutionary Algorithm to jointly optimize two important aspects of the configuration and planning of a Microgrid (MG): the structure's design and the way it is operated in time (specifically, the charging and discharging scheduling of the Energy Storage System, ESS, elements). For this purpose, a real MG scenario consisting of a wind and a photovoltaic generator, an ESS made up of one electrochemical battery, and residential and industrial loads is considered. Optimization is addressed by nesting a two-steps procedure [the first step optimizes the structure using an Evolutionary Algorithm (EA), and the second step optimizes the scheduling using another EA] following different adaptive approaches that determine the number of fitness function evaluations to perform in each EA. Finally, results obtained are compared to non-nesting 2-steps algorithm evolving following a classical scheme. Results obtained show a 3.5 % improvement with respect to the baseline scenario (the non-nesting 2-steps algorithm), or a 21 % improvement when the initial solution obtained with the Baseline Charge and Discharge Procedure is used as reference.
This review article provides a comprehensive analysis of nesting optimization algorithms in the shipbuilding industry,emphasizing their role in improving material utilization,minimizing waste,and enhancing production ...
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This review article provides a comprehensive analysis of nesting optimization algorithms in the shipbuilding industry,emphasizing their role in improving material utilization,minimizing waste,and enhancing production *** shipbuilding process involves the complex cutting and arrangement of steel plates,making the optimization of these operations vital for cost-effectiveness and *** algorithms are broadly classified into four categories:exact,heuristic,metaheuristic,and *** algorithms ensure optimal solutions but are computationally *** contrast,heuristic algorithms deliver quicker results using practical rules,although they may not consistently achieve optimal *** algorithms combine multiple heuristics to effectively explore solution spaces,striking a balance between solution quality and computational *** algorithms integrate the strengths of different approaches to further enhance *** review systematically assesses these algorithms using criteria such as material dimensions,part geometry,component layout,and computational *** findings highlight the significant potential of advanced nesting techniques to improve material utilization,reduce production costs,and promote sustainable practices in *** adopting suitable nesting solutions,shipbuilders can achieve greater efficiency,optimized resource management,and superior overall *** research directions should focus on integrating machine learning and real-time adaptability to further enhance nesting algorithms,paving the way for smarter,more sustainable manufacturing practices in the shipbuilding industry.
The present paper reports an intelligent computer-aided nesting ( CAN) system for optimal nesting of two-dimensional parts, especially parts with complicated shapes, with the objective of effectively improving the uti...
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The present paper reports an intelligent computer-aided nesting ( CAN) system for optimal nesting of two-dimensional parts, especially parts with complicated shapes, with the objective of effectively improving the utilization ratio of sheet materials. This paper also systemically reviews the nesting algorithms that were developed to perform various nesting tasks, and attacks the irregular part nesting problem by efficiently integrating and improving the performance of nesting algorithms such as the rectangular enclosure method, bottom-left nesting algorithms, heuristic algorithms and genetic algorithms. The CAN system has also been developed as a nesting algorithm test platform for researching and developing new nesting algorithms. Through this test platform, the limitations of existing nesting algorithms are investigated and problems such as nesting parts in spaces within a single part or between parts are also studied. Efforts have been devoted to improving the nesting efficiency of the existing algorithms and developing new nesting algorithms. Case studies are carried out in a sheet metal cutting company. The results show that the intelligent CAN system can effectively nest both regular and irregular parts, and greatly improve the utilization ratio of raw sheet material.
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