The research starts from the private desktop cloud, and realises the dynamic reception of computing services, the query of computing status and the task stealing based on the real-time load status by improving and opt...
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This study aims to improve the overall execution efficiency of the system by dividing workflow tasks into multiple levels and executing tasks at different levels simultaneously. Firstly, a parallel layered approach is...
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In order to overcome the problems of low network coverage and short network node survival time in the traditional node sleep scheduling algorithm, a redundant node sleep scheduling algorithm based on relative local de...
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Design for manufacturing and assembly (DfMA) is an engineering methodology which aims to increase ease of manufacture and efficiency of assembly by considering manufacturing and assembly constraints in the design proc...
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ISBN:
(数字)9780784485231
ISBN:
(纸本)9780784485231
Design for manufacturing and assembly (DfMA) is an engineering methodology which aims to increase ease of manufacture and efficiency of assembly by considering manufacturing and assembly constraints in the design process. However, current DfMA approaches in the construction sector are not automated enough to identify the design features that may cause project delay in real time. This leads to longer design cycle. Also, current scheduling algorithms rely on human intervention to generate activity network from a design output. Addressing these inefficiencies, we propose an interpretative machining learning model to predict the construction duration given a design output. More importantly, the same model identifies the design features that may cause the most delay in the project. The model is trained on a residential design dataset with various features, such as layout, geometry, and element typology. The output of the model is the project duration and an importance map, indicating the influence each feature of the given design has on the total project duration. The results from this model can considerably reduce the design cycle by supporting architects to create fabrication and assembly aware design even when they have little knowledge of production and assembly processes. This model will contribute to a novel computational approach for DfMA.
The non-clairvoyant scheduling problem has gained new interest within learning-augmented algorithms, where the decision-maker is equipped with predictions without any quality guarantees. In practical settings, access ...
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The non-clairvoyant scheduling problem has gained new interest within learning-augmented algorithms, where the decision-maker is equipped with predictions without any quality guarantees. In practical settings, access to predictions may be reduced to specific instances, due to cost or data limitations. Our investigation focuses on scenarios where predictions for only B job sizes out of n are available to the algorithm. We first establish near-optimal lower bounds and algorithms in the case of perfect predictions. Subsequently, we present a learning-augmented algorithm satisfying the robustness, consistency, and smoothness criteria, and revealing a novel tradeoff between consistency and smoothness inherent in the scenario with a restricted number of predictions.
With the development of society and the improvement of living standards, consumers prefer personalized products more and more. This makes the production mode change from large-scale production to single piece small ba...
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A dispersed computing standard that assists the users is cloud computing. In this model, users pay as much as use. Cloud servers try to achieve high performance, and one of the main factors is optimal scheduling. Seve...
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This paper discusses a novel hybrid population-based method for scheduling multiprocessor tasks on two dedicated processors. Combining a modified grey wolf optimizer with key enhancements, it yields a robust approxima...
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ISBN:
(纸本)9798350373981;9798350373974
This paper discusses a novel hybrid population-based method for scheduling multiprocessor tasks on two dedicated processors. Combining a modified grey wolf optimizer with key enhancements, it yields a robust approximate algorithm. Initialization uses a carefully selected combination of a greedy sequence and configurations, ensuring solution feasibility through a tailored sigmoid function. A straightforward local operator intensifies the search space, while a drop and rebuild operator counters premature convergence. Benchmark evaluations and comparative analysis discuss its effectiveness, highlighting the method's ability to discover new bounds compared to recent state-of-the-art approaches.
Railway transportation system faces the problem of tail gas emission while meeting the transportation demand, which puts forward higher requirements for energy saving and emission reduction. In order to meet this chal...
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In this paper, we study the online problem on three hierarchical machines with a buffer size of 1, and have two hierarchy, the objective is to minimize the maximum machine load. When there is only one low-hierarchy ma...
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