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Dynamic Scheduling of Multiclass Many-Server Queues with Abandonment: The Generalized cμ/h Rule

Operational Research, 2020
In “Dynamic Scheduling of Multiclass Many-Server Queues with Abandonment: The Generalized cμ/h Rule,” Long, Shimkin, Zhang, and Zhang propose three scheduling policies to cope with any general cost...
Zhenghua Long   +3 more
semanticscholar   +1 more source

Dynamic Airline Scheduling

Transportation Science, 2009
Demand stochasticity is a major challenge for the airlines in their quest to produce profit maximizing schedules. Even with an optimized schedule, many flights on departure have empty seats while others suffer a lack of seats to accommodate passengers who desire to travel.
Hai Jiang, Cynthia Barnhart
openaire   +1 more source

Llumnix: Dynamic Scheduling for Large Language Model Serving

USENIX Symposium on Operating Systems Design and Implementation
Inference serving for large language models (LLMs) is the key to unleashing their potential in people's daily lives. However, efficient LLM serving remains challenging today because the requests are inherently heterogeneous and unpredictable in terms of ...
Biao Sun   +6 more
semanticscholar   +1 more source

Large-Scale Dynamic Scheduling for Flexible Job-Shop With Random Arrivals of New Jobs by Hierarchical Reinforcement Learning

IEEE Transactions on Industrial Informatics
As the intelligent manufacturing paradigm evolves, it is urgent to design a near real-time decision-making framework for handling the uncertainty and complexity of production line control.
Kun Lei   +5 more
semanticscholar   +1 more source

Real-Time Scheduling for Dynamic Partial-No-Wait Multiobjective Flexible Job Shop by Deep Reinforcement Learning

IEEE Transactions on Automation Science and Engineering, 2022
In modern discrete flexible manufacturing systems, dynamic disturbances frequently occur in real time and each job may contain several special operations in partial-no-wait constraint due to technological requirements.
Shu Luo, Linxuan Zhang, Yushun Fan
semanticscholar   +1 more source

Dynamic Scheduling on Heterogeneous Multicores

2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019
Heterogeneous multicore systems can help adherence to design goals by providing a diverse set of hardware components to meet application requirements. Each core may also have tunable hardware that can reconfigured for different applications. However, scheduling becomes difficult in the presence of tunable hardware due to the additional constraint that ...
Ruben Vazquez   +3 more
openaire   +1 more source

Dynamic Event-Triggered Scheduling and Platooning Control Co-Design for Automated Vehicles Over Vehicular Ad-Hoc Networks

IEEE/CAA Journal of Automatica Sinica, 2021
This paper deals with the co-design problem of event-triggered communication scheduling and platooning control over vehicular ad-hoc networks (VANETs) subject to finite communication resource.
Xiaohua Ge   +4 more
semanticscholar   +1 more source

Dynamics of propranolol dosing schedules

Clinical Pharmacology and Therapeutics, 1983
Kinetic and dynamic data from 27 healthy male subjects were evaluated in a double-blind, randomized, double-crossover study to test the hypothesis that 180 mg/day propranolol twice and three times a day would provide much the same plasma levels and beta 1-blockade. The data indicate that propranolol twice rather than three times a day should be favored
J B, Coelho   +8 more
openaire   +2 more sources

Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints

International Journal of Production Research, 2021
Dynamic flexible job-shop scheduling is traditionally a challenge in real-world manufacturing systems, especially considering the constraints of transportation resources and transportation time.
Weibo Ren, Yan Yan, Yaoguang Hu, Yu Guan
semanticscholar   +1 more source

Dynamic DVFS Scheduling

2007
As discussed in the previous chapter, offline analysis can be used to generate a schedule of DVFS state changes to minimize energy consumption, while ensuring sufficient processing cycles are available for all tasks to meet their deadlines, even under worst-case computation requirements.
Padmanabhan S. Pillai, Kang G. Shin
openaire   +1 more source

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