To address the complex, dynamic, and stochastic nature of an automated material handling system (AMHS) in a semiconductor fabrication facility (fab), practitioners have used a high-fidelity discrete-event simulation a...
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The use of online social platforms and networks has surged over the past decade and continues to grow in popularity. In many social networks, volunteers play a central role, and their behavior in volunteer-based netwo...
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The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resource...
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The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.
Purpose: According to the Project management Institute, 70% of projects fail globally. The causes of project failure in many instances can be identified as non-technical or behavioral in nature arising from interactio...
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As one of the important daily consumer goods, alcoholic beverage presents high safety risks and potential hazards. Therefore, ensuring its quality and safety to meet consumer demand is urgent. Meanwhile, in response t...
As one of the important daily consumer goods, alcoholic beverage presents high safety risks and potential hazards. Therefore, ensuring its quality and safety to meet consumer demand is urgent. Meanwhile, in response to the iterative and optimization issues in ensemble learning, this paper introduces a Genetic Algorithm (GA) to enhance the performance of the Stacking ensemble learning model. Based on the inspection data of alcoholic food products published by National Food Safety Sampling Inspection Results Query System in China, this study applies the Synthetic Minority Oversampling Technique and Tomek Links algorithm for comprehensive sampling, to achieve balanced categorization of different samples. It effectively mitigates the impact of non-smooth data and improves the classification results biased towards majority class samples. After cross-validation and hyperparameter optimization, the proposed GA-improved Stacking ensemble learning model has an accuracy of 0.885, a precision of 0.882, a recall of 0.885, and an F1 score of 0.876, comparing with traditional single classifier algorithms such as random search, Bayesian search, and other methods. This study provides an effective risk warning method for regulatory agencies' inspection focus.
Increasing sludge has posed great challenges to the environment and thus necessitates more sustainable disposal. Sludge valorization technologies are very promising pathways in the era of emphasizing sustainable devel...
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We study the basic computational problem of detecting approximate stationary points for continuous piecewise affine (PA) functions. Our contributions span multiple aspects, including complexity, regularity, and algori...
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We consider risk-averse contextual optimization problems where the decision maker (DM) faces two types of uncertainties: problem data uncertainty (PDU) and contextual uncertainty (CU) associated with PDU, the DM makes...
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The world is at peak of fifth-generation communication technology and adopting ideas like cloudification or virtualization, but, the most important element is still "security", since more and more data is co...
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