scholarly journals Online Scheduling of Bounded Length Jobs to Maximize Throughput

2010 ◽  
pp. 116-127 ◽  
Author(s):  
Christoph Dürr ◽  
Łukasz Jeż ◽  
Kim Thang Nguyen
Keyword(s):  
Online Scheduling

Multi-agent reinforcement learning for online scheduling in smart factories

2021 ◽  
Vol 72 ◽  
pp. 102202
Author(s):  
Tong Zhou ◽  
Dunbing Tang ◽  
Haihua Zhu ◽  
Zequn Zhang
Keyword(s):  
Reinforcement Learning ◽  
Online Scheduling ◽  
Multi Agent ◽  
Smart Factories

Enabling Online Scheduling for Multi-Microgrid Systems: An Event-Triggered Approach

2020 ◽  
pp. 1-1
Author(s):  
Xiaodong Yang ◽  
Youbing Zhang ◽  
Hangfei Wu ◽  
Jinyu Wen ◽  
Shijie Cheng
Keyword(s):  
Online Scheduling ◽  
Event Triggered

Online Scheduling of Dynamic Task Graphs with Communication and Contention for Multiprocessors

2012 ◽  
Vol 23 (1) ◽  
pp. 126-133 ◽  
Author(s):  
P. Choudhury ◽  
P. P. Chakrabarti ◽  
R. Kumar
Keyword(s):  
Online Scheduling ◽  
Dynamic Task ◽  
Task Graphs

A simulation-driven online scheduling algorithm for the maintenance and operation of wind farm systems

SIMULATION ◽  
2021 ◽  
pp. 003754972110286
Author(s):  
Eduardo Pérez
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Computational Study ◽  
Scheduling Algorithm ◽  
Discrete Event ◽  
Wind Farms ◽  
Online Scheduling ◽  
Maintenance Scheduling ◽  
Lead Times ◽  
System Specification

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

scholarly journals Stochastic online scheduling

2011 ◽  
Vol 27 (3) ◽  
pp. 181-187 ◽  
Author(s):  
Tjark Vredeveld
Keyword(s):  
Online Scheduling

scholarly journals Rethinking elastic online scheduling of big data streaming applications over high-velocity continuous data streams

2017 ◽  
Vol 74 (2) ◽  
pp. 615-636 ◽  
Author(s):  
Dawei Sun ◽  
Hongbin Yan ◽  
Shang Gao ◽  
Xunyun Liu ◽  
Rajkumar Buyya
Keyword(s):  
Big Data ◽  
Data Streams ◽  
High Velocity ◽  
Online Scheduling ◽  
Continuous Data ◽  
Data Streaming ◽  
Streaming Applications
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