Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

Ahn, Gilseung; Hur, Sun

doi:10.1155/2021/1305849

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…In this neighborhood, with the optimal solution as the center, the value of the objective function approaches the value from far to near. When the fitness of the current solution is large, the search should be carried out in a smaller neighborhood; otherwise, it should be searched in a larger neighborhood [11]. In this way, the region where the optimal solution is located can be positioned step by step, and finally, the optimal solution can be approached.…”

Section: Cloud Genetic Algorithmmentioning

confidence: 99%

Mathematical Modeling Method of the Improved Genetic Algorithm for Random Power Fluctuation

Zhu

2022

International Transactions on Electrical Energy Systems

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In order to solve the problem that the traditional genetic algorithm has a slow search speed and is easy to fall into the local optimal solution, a mathematical modeling method of an improved genetic algorithm in random power fluctuation is proposed. Drawing on the idea of a genetic algorithm (GA) and using the randomness and stability trend of cloud droplets of the normal cloud model, the author proposes a new genetic algorithm, cloud genetic algorithm (CGA). CGA is implemented by the Y-condition cloud generator of the normal cloud model to realize the cross operation, and the basic cloud generator realizes the mutation operation. Finally, the power function optimization experiment and the IIR digital filter optimization design are carried out, and the standard GA, NQGA, CAGA, and LARES algorithms are carried out compared. The experimental results show that the IIR digital filter designed by CGA has the smallest maximum ripple (Ap) in the passband, which is 0.342, and the largest minimum attenuation (As) in the stopband, which is 34.27. It can be observed that the overall performance is better. The validity of the algorithm is proved, and it has a certain reference and application value.

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Section: Cloud Genetic Algorithmmentioning

confidence: 99%

Mathematical Modeling Method of the Improved Genetic Algorithm for Random Power Fluctuation

Zhu

2022

International Transactions on Electrical Energy Systems

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“…Helo et al [22] built a cloud-based production scheduling system for sheet metal manufacturing and developed a genetic-algorithm-based scheduling application to serve distributed manufacturing lines in the form of cloud manufacturing. Gilseung Ahn [23] developed a mathematical model to minimise latency, cost, quality, and reliability and proposed a multi-objective genetic algorithm for real-time scheduling. Ghomi et al [24] proposed a queuing network for the parallel scheduling of multi-tasks and solved the model using a particle swarm algorithm based on the processing time of the tasks.…”

Section: Scheduling Optimisation Algorithm Problemmentioning

confidence: 99%

Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing

Zhang,

Li,

Duan

et al. 2024

Systems

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In cloud manufacturing environments, the scheduling of multi-user manufacturing tasks often fails to consider the impact of service supply on resource allocation. This study addresses this gap by proposing a bi-objective multi-user multi-task scheduling model aimed at simultaneously minimising workload and maximising customer satisfaction. To accurately capture customer satisfaction, a novel comprehensive rating index is introduced, integrating the actual completion cost, time, and processing quality against customer expectations. Furthermore, vehicle constraints are incorporated into the model to accommodate potential delays in transport vehicle availability, thereby enhancing its alignment with real-world manufacturing settings. The proposed mathematical model is solved using an improved three-stage genetic algorithm, which integrates the k-means algorithm and a real-time sequence scheduling strategy to optimise solution quality. Validation against alternative algorithms across various case scales demonstrates the efficacy of the approach in providing practical scheduling solutions for real-case scenarios.

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“…This accessibility has democratized the art design process, allowing individuals from diverse backgrounds to explore their creativity without significant upfront costs. Moreover, cloud-based collaboration tools enable artists to work seamlessly with teammates or clients, regardless of their geographical location [2]. This streamlined workflow fosters greater creativity and efficiency, as ideas can be shared and implemented in real-time.…”

Section: Introductionmentioning

confidence: 99%

Cloud Based Ccmc Model for Application of Genetic Algorithm Based on Cloud Computing in Art Product Design

Ouyang

2024

jes

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Art design has entered a new era with the integration of cloud computing technology, revolutionizing the creative process and expanding artistic possibilities. By leveraging the vast computational power and storage capabilities of cloud platforms, artists can explore innovative techniques, collaborate with peers remotely, and access a wealth of digital resources from anywhere in the world. Cloud computing enables artists to work with large-scale datasets, create complex visualizations, and render high-resolution artworks with ease. Moreover, cloud-based tools and applications offer flexibility and scalability, allowing artists to experiment freely and iterate rapidly. Whether creating digital paintings, 3D animations, or interactive installations, cloud computing empowers artists to push the boundaries of their creativity and bring their visions to life in ways previously unimaginable. This paper presents an innovative application of genetic algorithm (GA) leveraging cloud computing technology for art product design, with a focus on the Centralized Clustering Middle Chain (CCMC) framework. By harnessing the computational power and scalability of cloud platforms, GA facilitates the optimization of design parameters to generate novel and aesthetically pleasing art products. The CCMC framework streamlines the design process by centralizing data clustering and analysis, enabling efficient exploration of design space and identification of optimal solutions. Through simulated experiments and empirical evaluations, the effectiveness of the GA-based approach in art product design is assessed. Results demonstrate significant improvements in design quality and efficiency, with the GA-enabled cloud computing solution achieving a 40% reduction in design iteration time and a 30% increase in product innovation compared to traditional methods.

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Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

Cited by 12 publications

References 29 publications

Mathematical Modeling Method of the Improved Genetic Algorithm for Random Power Fluctuation

Mathematical Modeling Method of the Improved Genetic Algorithm for Random Power Fluctuation

Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing

Cloud Based Ccmc Model for Application of Genetic Algorithm Based on Cloud Computing in Art Product Design

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