Bi-level optimization of shared manufacturing service composition based on improved NSGA-II

Wang, Ying; Liu, Peng

doi:10.1371/journal.pone.0303968

PLoS ONE

2024

DOI: 10.1371/journal.pone.0303968

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Bi-level optimization of shared manufacturing service composition based on improved NSGA-II

Ying Wang,

Peng Liu

Abstract: To address the issue of insufficiently comprehensive representation of service composition indexes in the shared manufacturing environment, service reliability, confidence, and other indexes are decomposed in detail to establish a composition evaluation system, and a shared manufacturing service composition optimization model based on bi-level programming is proposed. The model takes Quality of Service (QoS) as the upper objective function and service reliability, service confidence and task fit as the lower o… Show more

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2024

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Cited by 2 publications

References 29 publications

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Hybrid Optimization Approach Using Multiobjective Genetic Algorithm NSGA‐II, SCAPS‐1D Simulation, and Response Surface Methodology for Organic Solar Cell Analysis

Moulebhar,

Bendenia,

Merad‐Dib

et al. 2024

Physica Status Solidi (a)

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In the field of simulation, it is difficult to find the relevant values for the properties of materials and in this context this approach has been proposed on optimizing the performance of organic solar cells, a promising technology in the field of renewable energy, to increase their efficiency. It adopts a hybrid approach combining the response surface methodology (RSM) with a Box–Behnken design (BBD) and the nondominated sorting genetic algorithm II (NSGA‐II). The RSM BBD method is used to identify objective functions to be optimized, considering interactions between selected parameters such as the thickness of the active layer, electron‐transport layer (ETL), hole‐transport layer (HTL), and the doping of these layers. Concurrently, the NSGA‐II genetic algorithm aims to maximize the performance of the solar cell based on these parameters. The specific importance of NSGA‐II lies in its ability to solve complex multiobjective optimization problems. Indeed, NSGA‐II is designed to simultaneously manage several performance objectives, which is crucial for organic solar cells. Its ability to generate a diverse set of optimal solutions enables efficient configurations to be found that may not be obvious with simpler optimization approaches. The results of this study show that optimum solar cell performance is achieved with active layer, ETL layer, and HTL layer thicknesses of 100.86, 79.9, and 20.24 nm, respectively, and active layer doping of 8.71E + 21 cm−3, HTL layer doping of 9.90E + 21 cm−3, and ETL layer doping of 9.49E + 21 cm−3. Analysis using Solar Cell Capacitance Simulator‐1D (SCAPS‐1D) software shows that optimum performance is achieved with these specific parameter values. After optimization with NSGA‐II, the power conversion efficiency increases by 39% compared to previous work. This study provides evidence of the effectiveness of the proposed hybrid approach for optimizing the performance of organic solar cells. By showing remarkable agreement between the results obtained by NSGA‐II and SCAPS‐1D, this approach opens up promising prospects for the future of renewable energy.

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Hybrid Optimization Approach Using Multiobjective Genetic Algorithm NSGA‐II, SCAPS‐1D Simulation, and Response Surface Methodology for Organic Solar Cell Analysis

Moulebhar,

Bendenia,

Merad‐Dib

et al. 2024

Physica Status Solidi (a)

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Cyber-Physical Scheduling System for Multiobjective Scheduling Optimization of a Suspension Chain Workshop Using the Improved Non-Dominated Sorting Genetic Algorithm II

Zhao,

Hu,

et al. 2024

Machines

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Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm’s results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization.

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Bi-level optimization of shared manufacturing service composition based on improved NSGA-II

Cited by 2 publications

References 29 publications

Hybrid Optimization Approach Using Multiobjective Genetic Algorithm NSGA‐II, SCAPS‐1D Simulation, and Response Surface Methodology for Organic Solar Cell Analysis

Hybrid Optimization Approach Using Multiobjective Genetic Algorithm NSGA‐II, SCAPS‐1D Simulation, and Response Surface Methodology for Organic Solar Cell Analysis

Cyber-Physical Scheduling System for Multiobjective Scheduling Optimization of a Suspension Chain Workshop Using the Improved Non-Dominated Sorting Genetic Algorithm II

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