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.
The network has become a bottleneck for generative artificial intelligence (GAI) jobs. Accelerating GAI jobs in edge data centers using hybrid electrical/optical switch is considered a promising solution. This archite...
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The network has become a bottleneck for generative artificial intelligence (GAI) jobs. Accelerating GAI jobs in edge data centers using hybrid electrical/optical switch is considered a promising solution. This architecture optimizes bandwidth utilization by enabling demand-aware topology reconfiguration through flexible configuration of optical circuit switche optical circuit switches (OCS). However, frequent topology reconfiguration may increase latency. Therefore, there is a balanced relationship between latency and bandwidth utilization. In this article, we propose a multigranularity adaptive interleaved algorithm for service scheduling in edge data centers. First, different degrees of time slot shifts are introduced based on the latency sensitivity of jobs, where large bandwidth GAI jobs are transmitted in a single hop by configuring a demand-aware topology. Additionally, when the reconfiguration threshold is met, low-priority ports are prioritized for reconfiguration to ensure latency requirements are met. This approach effectively resolves the tradeoff between bandwidth utilization and latency by decoupling them from each other. Simulation results show that this approach can effectively reduce the latency and improve the network throughput.
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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A Mixed-Criticality System (MCS) integrates multiple applications with different criticality levels on the same hardware platform. For power and energy-constrained systems such as Unmanned Aerial Vehicles, it is impor...
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A Mixed-Criticality System (MCS) integrates multiple applications with different criticality levels on the same hardware platform. For power and energy-constrained systems such as Unmanned Aerial Vehicles, it is important to minimize energy consumption of the computing system while meeting reliability constraints. In this paper, we first determine the number of tolerated faults according to the given reliability target. Second, we propose a schedulability test for MCS with semi-clairvoyance and checkpointing. Third, we propose the Energy-Aware scheduling with Reliability Constraint (EASRC) scheduling algorithm for MCS with semi-clairvoyance and checkpointing. It consists of an offline phase and an online phase. In the offline phase, we determine the offline processor speed by reclaiming static slack time. In the online phase, we adjust the processor speed by reclaiming dynamic slack time to further save energy. Finally, we show the performance of our proposed algorithm through experimental evaluations. The results show that the proposed algorithm can save an average of 9.67% of energy consumption compared with existing methods.
scheduling cluster tools with EFEM involves collaborative scheduling among several modules, i.e., a vacuum module (VM), a loadlock module (LLM), and an equipment front-end module (EFEM). Typically, one-in-one-out load...
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ISBN:
(纸本)9798350358513;9798350358520
scheduling cluster tools with EFEM involves collaborative scheduling among several modules, i.e., a vacuum module (VM), a loadlock module (LLM), and an equipment front-end module (EFEM). Typically, one-in-one-out loadlocks are widely employed in wafer fabrication. Additionally, for module cooperation, LLM functioning as a shared module is essential in collaboration. However, LLM has pressure management capabilities, introducing additional challenges for collaborative scheduling. To address such collaborative scheduling, we analyze interaction behaviors among VM, EFEM, and LLM, focusing on the robot cooperative relationship. Then, we introduce a timeliness analysis of robot activities, providing insights into the scheduling problem. Based on these, a closedform algorithm is derived, offering a methodology to ascertain a schedule that minimizes tool cycle time. Moreover, the algorithm facilitates the identification of system bottlenecks, providing guidance for systematic improvements to enhance system performance. Furthermore, the developed algorithm can be embedded into the cluster tool controller, enabling the implementation of the obtained schedules.
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